Systems and methods for aeromagnetic surveying

Abstract

A method (200) for determining target magnetic field data d(Rβ,ω) is disclosed. The method (200) comprises selecting empirical magnetic field data d(Rω) that is associated with an influence region 105ω; fitting synthetic magnetic dipole source moments m(rω,s) to the empirical magnetic field data d(Rω): and determining target magnetic field data d(Rβ,ω) using the synthetic magnetic dipole source moments m(rω,s). A method (300) for determining target magnetic field data d(Rβ,ω) is also disclosed. The method (300) comprises selecting empirical magnetic field data d(Rω) that is associated with an influence region 105ω; determining empirical covariance data Yω based on the empirical magnetic field data d(Rω); determining an optimised value of one or more parameter of a covariance function C by fitting the covariance function C to the empirical covariance data Yω; and determining target magnetic field data d(Rβ,ω) using the covariance function C.

Description

[0001]SYSTEMS AND METHODS FOR AEROMAGNETIC SURVEYINGTECHNICAL FIELD The present disclosure relates to aeromagnetic surveying techniques. In particular, the present disclosurerelates to systems and methods for determining target magnetic field data indicative of one or morecharacteristics of a local magnetic field, at one or more target locations, using empirical magnetic fielddata. BACKGROUND Aeromagnetic surveying is a geophysical exploration technique for mapping variations in the Earth's magnetic field to identify geological structures and potential mineral deposits. Traditional aeromagnetic surveys typically involve flying an aircraft equipped with a magnetometer at a constant height aboveground level, over a survey area. The magnetometer measures the total magnetic field intensity at regularintervals along the flight path, providing data about subsurface geological features. However, conventional aeromagnetic surveying methods face certain limitations. The height aboveground level at which the aircraft flies represents a trade-off between data resolution and surveyefficiency. Flying at a higher ground clearance allows for faster coverage of large areas but results inlower resolution data, as magnetic signals attenuate with increasing distance from the source. Conversely,flying at a lower ground clearance provides higher resolution data but is more time-consuming andpotentially hazardous in areas with challenging terrain. Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. SUMMARYIn some embodiments of the present disclosure, there is provided a method (200) comprising:selecting empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103);fitting synthetic magnetic dipole source moments to the empirical magnetic field data ^(^^);anddetermining target magnetic field data that is indicative of a magnetic field characteristic of alocal magnetic field at one or more target location using the synthetic magnetic dipole sourcemoments ^^^^,^^.In some embodiments, the empirical magnetic field data ^(^^) comprises a first set of magnetic fielddata that is associated with a first flight path (123). The first set of magnetic field data ^^^^,^^comprises magnetic field measurements ^^^^,^^ taken at measurement locations ^^,^ along the firstflight path (123). A first aerial system (122) flies along the first flight path to take the magnetic fieldmeasurements ^^^^,^^. The first aerial system (122) is controlled to fly at or towards a target flight pathground clearance when taking the magnetic field measurements The target flight path groundclearance of the first flight path (123) is a first target flight path ground clearance.In some embodiments, the empirical magnetic field data ^(^^) comprises a second set of magnetic fielddata ^^^^,^^ that is associated with a second flight path (125). The second flight path (125) is differentfrom the first flight path (123). The second flight path (125) is below the first flight path (123) along amajority of the second flight path (125). The second set of magnetic field data comprisesmagnetic field measurements ^^^^,^^ taken at measurement locations along the second flight path(125). A second aerial system (142) flies along the second flight path (125) to take the magnetic fieldmeasurements ^^^^,^^. The second aerial system (142) is controlled to fly at or towards a target flightpath ground clearance when taking the magnetic field measurements ^^^^,^^. The target flight pathground clearance of the second flight path (125) is a second target flight path ground clearance. Thesecond target flight path ground clearance is less than the first target flight path ground clearance.In some embodiments, the method (200) comprises defining the one or more target location In someembodiments, the method (200) comprises defining the one or more target location at a targetground clearance that is different to the target flight path ground clearance of the first flight path (123). Insome embodiments, the method (200) comprises defining the one or more target location at a targetground clearance that is less than the target flight path ground clearance of the first flight path (123). Insome embodiments, the method (200) comprises defining the one or more target location at a targetground clearance that is different to the target flight path ground clearance of the second flight path (125).In some embodiments, the method (200) comprises defining the one or more target location at atarget ground clearance that is greater than the target flight path ground clearance of the second flight path(125). In some embodiments, the method (200) comprises defining the one or more target location ata target ground clearance that is different to the target flight path ground clearance of the first flightpath (123) and the target flight path ground clearance of the second flight path (125). In someembodiments, the method (200) comprises defining the one or more target location at a targetground clearance that is less than the target flight path ground clearance of the first flight path (123) and greater than the target flight path ground clearance of the second flight path (125).In some embodiments, the method (200) comprises defining a plurality of target regions ^^ at the targetground clearance.In some embodiments, the method (200) comprises selecting a target region ^^ of the plurality of targetregions ^^, the one or more target location being within the selected target region ^^.In some embodiments, the method (200) comprises defining a source region 113^ for the selected targetregion ^^, the source region 113^ being a three-dimensional region that is bound by one or more sourceregion boundary 115^.In some embodiments, the source region 113^ is defined below the selected target region ^^.In some embodiments, the source region 113^is bound by:an upper source region boundary 115^ defining a first source region surface 117^ ;a lower source region boundary 115^ defining a second source region surface 119^ ; andone or more lateral source region boundaries 115^, the one or more lateral source region boundaries 115^extending from the upper source region boundary 115^to the lower source region boundary 115^.In some embodiments, one or more source region boundary conditions define boundaries 115^ of thesource region 113^ . The one or more source region boundary conditions may define the upper sourceregion boundary 115^. The one or more source region boundary conditions may define the lower source region boundary 115^. The one or more source region boundary conditions may define the one or more lateral source region boundaries 115^. In some embodiments, the upper source region boundary 115^is defined at or near ground level. In some embodiments, the lower source region boundary 115^is defined at a depth below ground level.In some embodiments, the method (200) comprises defining the influence region 105^ around the targetregion ^^, the influence region 105^ being a three-dimensional region that is bound by one or moreinfluence region boundary 109^.In some embodiments, the influence region 105^ is bound by at least one of:an upper influence region boundary 109^; a lower influence region boundary 109^; and one or more lateral influence region boundaries 109^.In some embodiments, one or more influence region boundary conditions define boundaries 109^ of theinfluence region 105^ . The one or more influence region boundary conditions may define the upperinfluence region boundary 109^. The one or more influence region boundary conditions may define the lower influence region boundary 109^. The one or more influence region boundary conditions may define the one or more lateral influence region boundaries 109^.In some embodiments, the influence region 105^ is above the source region 113^ .In some embodiments, a lateral dimension of the influence region 105^ is greater than, or equal to, alateral dimension of the source region 113^.In some embodiments, the selected empirical magnetic field data ^(^^) is empirical magnetic field data^(^^) obtained at measurement locations ^^,^ within the influence region 105^ .In some embodiments, the empirical magnetic field data ^(^^) comprises a magnetic field intensityvalue |^| associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^ being within the influence region 105^ . In some embodiments, the empiricalmagnetic field data ^(^^) comprises a horizontal gradient value |^| associated with a measurementlocation ^^,^ of a plurality of measurement locations ^^,^, each measurement location ^^,^ being withinthe influence region 105^ . In some embodiments, the empirical magnetic field data ^(^^) comprises a^^ transverse horizontal gradient value ^ ^^^ associated with a measurement location ^^,^ of a plurality ofmeasurement locations ^^,^, each measurement location ^^,^ being within the influence region 105^ . Insome embodiments, the empirical magnetic field data ^(^^) comprises a longitudinal horizontal gradient^^ value ^ ^^^ associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^.In some embodiments, the method (200) comprises defining each synthetic magnetic dipole sourcemoment at a respective dipole source location In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises fitting synthetic magnetic fields of the synthetic magnetic dipolesource moments to the empirical magnetic field data ^(^^) synthetic magnetic dipole sourcemoments to the empirical magnetic field data ^(^^).In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises constructing a forward modelling matrix ^^. In some embodiments, the forward modelling matrix ^^relates the synthetic magnetic dipole sourcemoments to the empirical magnetic field data ^(^^).In some embodiments, the dipole source locations comprise a first group of dipole source locationsat or near ground level. In some embodiments, the dipole source locations comprise a secondgroup of dipole source locations at a defined depth below ground level. In some embodiments, thedipole source locations of the first group are above the dipole source locations of the secondgroup.In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises calculating a plurality of outputs of a forward modellingfunction ^^,^, each output of the forward modelling function ^^,^ being calculated using a respectivedipole source location and a respective measurement location ^^,^ as inputs of the forward modellingfunction In some embodiments, fitting the synthetic magnetic dipole source moments tothe empirical magnetic field data ^(^^) comprises assembling the forward modelling matrix ^^ from thecalculated outputs of the forward modelling function ^^,^. In some embodiments, a difference between ground level and the defined depth is about half of a lateraldimension of the source region 113^ .In some embodiments, calculating an output of the forward modelling function comprises calculating: where: is the forward modelling function; ^^is a magnetic constant; ^^,^is a measurement location ^^,^within the relevant influence region 103^; ^^,^is a dipole source location; dIn some embodiments, the method (200) comprises applying a smoothness constraint to the forwardmodelling matrix ^^.In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises determining the synthetic magnetic dipole source moments^^^^,^^ using the empirical magnetic field data ^(^^) and the forward modelling matrix ^^.In some embodiments, determining the synthetic magnetic dipole source moments comprisessolving a weighted least squares problem in which the empirical magnetic field data ^(^^) and theforward modelling matrix ^^are inputs.In some embodiments, determining the synthetic magnetic dipole source moments comprisessolving: where: is a vector of the synthetic magnetic dipole source moments ^^^^,^^ that are to be solvedfor; ^^is the forward modelling matrix; ^(^^) is a vector of the empirical magnetic field data; and^ is a noise vector associated with a first sensor system (124) and a second sensor system (144).In some embodiments, determining the target magnetic field data comprises constructing atarget surface forward modelling matrix ^^,^that is associated with the one or more target location ^^,^. In some embodiments, the target surface forward modelling matrix ^^,^relates a synthetic magnetic fieldgenerated by the synthetic magnetic dipole source moments ^(^^,^) to the one or more target locationIn some embodiments, constructing the target surface forward modelling matrix ^^,^comprisescalculating a plurality of outputs of a target surface forward modelling function ^^,^, each output of the target surface forward modelling function ^^,^being calculated using a respective dipole sourcelocation ^^,^ and a respective target location as inputs of the target surface forward modellingfunction ^^,^. In some embodiments, constructing the target surface forward modelling matrix ^^,^comprises assembling the target surface forward modelling matrix ^^,^from the calculated outputs of the target surface forward modelling function ^^,^.In some embodiments, calculating an output of the target surface forward modelling function ^^,^comprises calculating: where: ^^,^is the target surface forward modelling function; ^^is a magnetic constant; ^^,^is a target location; ^^,^is a dipole source location; is a first sub-matrix of the target surface forward modelling function; ^^^^ =is a second sub-matrix of the target surface forward modelling function; is a third sub-matrix of the target surface forward modelling function; and ^^,^ is a unit vector pointing from a source location to a target location ^^,^.In some embodiments, determining the target magnetic field data comprises multiplying thetarget surface forward modelling matrix ^^,^ and synthetic magnetic dipole source data comprising the synthetic magnetic dipole source moments ^^^^,^^.In some embodiments, the target magnetic field data comprises a magnetic field intensity valuefor each target location ^^,^.In some embodiments, the magnetic field characteristic is magnetic field intensity. In some embodiments,the magnetic field characteristic comprises magnetic field intensity.In some embodiments, the magnetic field characteristic is a magnetic field gradient. In someembodiments, the magnetic field characteristic comprises a magnetic field gradient.In some embodiments, the target magnetic field data that is indicative of a plurality of magneticfield characteristics of a local magnetic field at one or more target location That is, in someembodiments, the target magnetic field data comprises values that are indicative of a plurality ofmagnetic field characteristics of a local magnetic field at one or more target location The magneticfield characteristics may comprise at least one of magnetic field intensity and magnetic field gradient.In some embodiments, the method (200) comprises:defining a plurality of target regions ^^ at the target ground clearance; anditeratively: selecting a target region ^^ of the plurality of target regions ^^;defining a source region 113^ for the selected target region ^^, the source region 113^ being athree-dimensional region that is bound by one or more source region boundary 115^; defining the influence region 105^ around the target region ^^, the influence region 105^ being athree-dimensional region that is bound by one or more influence region boundary 109^; defining the synthetic magnetic dipole source moments ^^^^,^^ at respective dipole sourcelocations ^^,^ of the source region 113^ ; anddetermining the target magnetic field data the target magnetic field data beingindicative of the magnetic field characteristic of the local magnetic field at one or more target location of the selected target region ^^;for each target region ^^.In some embodiments, the method (200) comprises comprising constructing a model ^ of the magneticfield characteristic at the target ground clearance, across the survey area (103), using the target magneticfield dataIn some embodiments, there is provided a system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the at least one processor (104) to perform the method (200).In some embodiments, there is provided a system (100) comprising: at least one processor (104); and memory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the system (100) to perform the method (200).In some embodiments of the present disclosure, there is provided a system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the at least one processor (104) to:select empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103);fit synthetic magnetic dipole source moments to the empirical magnetic field data ^(^^);and determine target magnetic field data that is indicative of a magnetic field characteristic ofa local magnetic field at one or more target location using the synthetic magnetic dipole sourcemoments ^^^^,^^.In some embodiments, the empirical magnetic field data ^(^^) comprises a first set of magnetic fielddata ^^^^,^^ that is associated with a first flight path (123). The first set of magnetic field data comprises magnetic field measurements ^^^^,^^ taken at measurement locations ^^,^ along the firstflight path (123). A first aerial system (122) flies along the first flight path to take the magnetic field measurements ^^^^,^^. The first aerial system (122) is controlled to fly at or towards a target flight pathground clearance when taking the magnetic field measurements The target flight path groundclearance of the first flight path (123) is a first target flight path ground clearance.In some embodiments, the empirical magnetic field data ^(^^) comprises a second set of magnetic fielddata ^^^^,^^ that is associated with a second flight path (125). The second flight path (125) is differentfrom the first flight path (123). The second flight path (125) is below the first flight path (123) along amajority of the second flight path (125). The second set of magnetic field data comprisesmagnetic field measurements ^^^^,^^ taken at measurement locations along the second flight path(125). A second aerial system (142) flies along the second flight path (125) to take the magnetic field measurements The second aerial system (142) is controlled to fly at or towards a target flightpath ground clearance when taking the magnetic field measurements ^^^^,^^. The target flight pathground clearance of the second flight path (125) is a second target flight path ground clearance. The second target flight path ground clearance is less than the first target flight path ground clearance.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are configured to cause the at least one processor (104) to define the one or more targetlocation In some embodiments, the computer-executable instructions (108), when executed by the atleast one processor (104), are configured to cause the at least one processor (104) to define the one ormore target location at a target ground clearance that is different to the target flight path groundclearance of the first flight path (123). In some embodiments, the computer-executable instructions (108),when executed by the at least one processor (104), are configured to cause the at least one processor (104)to define the one or more target location at a target ground clearance that is less than the target flightpath ground clearance of the first flight path (123). In some embodiments, the computer-executableinstructions (108), when executed by the at least one processor (104), are configured to cause the at leastone processor (104) to define the one or more target location at a target ground clearance that isdifferent to the target flight path ground clearance of the second flight path (125). In some embodiments,the computer-executable instructions (108), when executed by the at least one processor (104), areconfigured to cause the at least one processor (104) to define the one or more target location at atarget ground clearance that is greater than the target flight path ground clearance of the second flight path(125). In some embodiments, the computer-executable instructions (108), when executed by the at leastone processor (104), are configured to cause the at least one processor (104) to define the one or moretarget location at a target ground clearance that is different to the target flight path ground clearanceof the first flight path (123) and the target flight path ground clearance of the second flight path (125). Insome embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are configured to cause the at least one processor (104) to define the one or more targetlocation at a target ground clearance that is less than the target flight path ground clearance of thefirst flight path (123) and greater than the target flight path ground clearance of the second flightpath (125).In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define a plurality oftarget regions ^^ at the target ground clearance.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to select a targetregion ^^ of the plurality of target regions ^^, the one or more target location being within theselected target region ^^.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define a source region113^ for the selected target region ^^, the source region 113^ being a three-dimensional region that isbound by one or more source region boundary 115^.In some embodiments, the source region 113^ is defined below the selected target region ^^.In some embodiments, the source region 113^is bound by:an upper source region boundary 115^ defining a first source region surface 117^ ;a lower source region boundary 115^ defining a second source region surface 119^ ; andone or more lateral source region boundaries 115^, the one or more lateral source region boundaries 115^extending from the upper source region boundary 115^to the lower source region boundary 115^.In some embodiments, one or more source region boundary conditions define boundaries 115^ of thesource region 113^. The one or more source region boundary conditions may define the upper source region boundary 115^. The one or more source region boundary conditions may define the lower source region boundary 115^. The one or more source region boundary conditions may define the one or more lateral source region boundaries 115^. In some embodiments, the upper source region boundary 115^is defined at or near ground level. In some embodiments, the lower source region boundary 115^is defined at a depth below ground level.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define the influenceregion 105^ around the target region ^^, the influence region 105^ being a three-dimensional regionthat is bound by one or more influence region boundary 109^ .In some embodiments, the influence region 105^ is bound by at least one of:an upper influence region boundary 109^; a lower influence region boundary 109^; and one or more lateral influence region boundaries 109^.In some embodiments, one or more influence region boundary conditions define boundaries 109^ of theinfluence region 105^ . The one or more influence region boundary conditions may define the upperinfluence region boundary 109^. The one or more influence region boundary conditions may define the lower influence region boundary 109^. The one or more influence region boundary conditions may define the one or more lateral influence region boundaries 109^.In some embodiments, the influence region 105^ is above the source region 113^ .In some embodiments, a lateral dimension of the influence region 105^ is greater than, or equal to, alateral dimension of the source region 113^.In some embodiments, the selected empirical magnetic field data ^(^^) is empirical magnetic field data^(^^) obtained at measurement locations ^^,^ within the influence region 105^ .In some embodiments, the empirical magnetic field data ^(^^) comprises a magnetic field intensityvalue |^| associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^ being within the influence region 105^ . In some embodiments, the empiricalmagnetic field data ^(^^) comprises a horizontal gradient value |^| associated with a measurementlocation ^^,^ of a plurality of measurement locations ^^,^, each measurement location ^^,^ being withinthe influence region 105^ . In some embodiments, the empirical magnetic field data ^(^^) comprises a^^ transverse horizontal gradient value ^ ^^^ associated with a measurement location ^^,^ of a plurality ofmeasurement locations ^^,^, each measurement location ^^,^ being within the influence region 105^ . Insome embodiments, the empirical magnetic field data ^(^^) comprises a longitudinal horizontal gradient^^ value ^ ^^^ associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define each syntheticmagnetic dipole source moment at a respective dipole source location In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises fitting synthetic magnetic fields of the synthetic magnetic dipolesource moments to the empirical magnetic field data ^(^^) synthetic magnetic dipole sourcemoments to the empirical magnetic field data ^(^^).In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises constructing a forward modelling matrix ^^.In some embodiments, the forward modelling matrix ^^relates the synthetic magnetic dipole sourcemoments to the empirical magnetic field data ^(^^).In some embodiments, the dipole source locations ^^,^ comprise a first group of dipole source locations^^,^ at or near ground level. In some embodiments, the dipole source locations ^^,^ comprise a second group of dipole source locations ^^,^at a defined depth below ground level. In some embodiments, thedipole source locations ^^,^ of the first group are above the dipole source locations ^^,^ of the secondgroup.In some embodiments, fitting the synthetic magnetic dipole source moments ^^^^,^^ to the empiricalmagnetic field data ^(^^) comprises calculating a plurality of outputs of a forward modellingfunction ^^,^, each output of the forward modelling function ^^,^ being calculated using a respectivedipole source location and a respective measurement location ^^,^ as inputs of the forward modellingfunction In some embodiments, fitting the synthetic magnetic dipole source moments tothe empirical magnetic field data ^(^^) comprises assembling the forward modelling matrix ^^ from thecalculated outputs of the forward modelling function ^^,^. In some embodiments, a difference between ground level and the defined depth is about half of a lateral dimension of the source region 113^. In some embodiments, calculating an output of the forward modelling function ^^,^comprises calculating: where: ^^,^is the forward modelling function; ^^is a magnetic constant; ^^,^is a measurement location ^^,^within the relevant influence region 103^; ^^,^is a dipole source location; In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to apply a smoothnessconstraint to the forward modelling matrix ^^. In some embodiments, applying the smoothness constraint comprises appending smoothness constraintrows to the forward modelling matrix ^^.In some embodiments, fitting the synthetic magnetic dipole source moments to the empiricalmagnetic field data ^(^^) comprises determining the synthetic magnetic dipole source moments^^^^,^^ using the empirical magnetic field data ^(^^) and the forward modelling matrix ^^.In some embodiments, determining the synthetic magnetic dipole source moments comprisessolving a weighted least squares problem in which the empirical magnetic field data ^(^^) and theforward modelling matrix ^^are inputs.In some embodiments, determining the synthetic magnetic dipole source moments comprisessolving: where: is a vector of the synthetic magnetic dipole source moments ^^^^,^^ that are to be solvedfor; ^^is the forward modelling matrix; ^(^^) is a vector of the empirical magnetic field data; and^ is a noise vector associated with a first sensor system (124) and a second sensor system (144).In some embodiments, determining the target magnetic field data comprises constructing atarget surface forward modelling matrix ^^,^ that is associated with the one or more target location ^^,^.In some embodiments, the target surface forward modelling matrix ^^,^ relates a synthetic magnetic fieldgenerated by the synthetic magnetic dipole source moments ^(^^,^) to the one or more targetlocation ^^,^.In some embodiments, constructing the target surface forward modelling matrix ^^,^comprisescalculating a plurality of outputs of a target surface forward modelling function each output of the target surface forward modelling function ^^,^being calculated using a respective dipole sourcelocation ^^,^ and a respective target location as inputs of the target surface forward modellingfunction ^^,^. In some embodiments, constructing the target surface forward modelling matrix ^^,^comprises assembling the target surface forward modelling matrix ^^,^from the calculated outputs of the target surface forward modelling function ^^,^.In some embodiments, calculating an output of the target surface forward modelling function ^^,^comprises calculating: where: ^^,^is the target surface forward modelling function; ^^is a magnetic constant; ^^,^is a target location; ^^,^is a dipole source location; is a first sub-matrix of the target surface forward modelling function; ^^^^ =is a second sub-matrix of the target surface forward modelling function; is a third sub-matrix of the target surface forward modelling function; and ^^,^ is a unit vector pointing from a source location to a target location ^^,^.In some embodiments, determining the target magnetic field data comprises multiplying thetarget surface forward modelling matrix ^^,^ and synthetic magnetic dipole source data comprising the synthetic magnetic dipole source moments ^^^^,^^.In some embodiments, the target magnetic field data comprises a magnetic field intensity valuefor each target location ^^,^. In some embodiments, the magnetic field characteristic is magnetic field intensity. In some embodiments, the magnetic field characteristic comprises magnetic field intensity. In some embodiments, the magnetic field characteristic is a magnetic field gradient. In some embodiments, the magnetic field characteristic comprises a magnetic field gradient.In some embodiments, the target magnetic field data that is indicative of a plurality of magneticfield characteristics of a local magnetic field at one or more target location That is, in someembodiments, the target magnetic field data comprises values that are indicative of a plurality ofmagnetic field characteristics of a local magnetic field at one or more target location The magneticfield characteristics may comprise at least one of magnetic field intensity and magnetic field gradient.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to:define a plurality of target regions ^^ at the target ground clearance; anditeratively: select a target region ^^ of the plurality of target regions ^^;define a source region 113^ for the selected target region ^^, the source region 113^ being athree-dimensional region that is bound by one or more source region boundary 115^ ;define the influence region 105^ around the target region ^^, the influence region 105^ being athree-dimensional region that is bound by one or more influence region boundary 109^; define the synthetic magnetic dipole source moments ^^^^,^^ at respective dipole sourcelocations ^^,^ of the source region 113^ ; anddetermine the target magnetic field data the target magnetic field data beingindicative of the magnetic field characteristic of the local magnetic field at one or more target location of the selected target region ^^;for each target region ^^.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to construct a model ^ ofthe magnetic field characteristic at the target ground clearance, across the survey area (103), using thetarget magnetic field data In some embodiments, the system (100) comprises:a first aerial system (122); anda second aerial system (142); andthe first flight path (123) is a flight path of the first aerial system (122); and the second flight path (125) is a flight path of the second aerial system (142).In some embodiments, the first aerial system (122) propels the second aerial system (142).In some embodiments, the first aerial system (122) comprises a first sensor system (124) that obtains thefirst set of magnetic field data ^^^^,^^. In some embodiments, the second aerial system (142) comprisesa second sensor system (144) that obtains the second set of magnetic field data ^^^^,^^.In some embodiments, the first sensor system (124) comprises at least one of a magnetometer; a GlobalNavigation Satellite System module; a height measurement system; an Inertial Measurement Unit; and agradiometer.In some embodiments, the second sensor system (144) comprises at least one of a magnetometer; aGlobal Navigation Satellite System module; a height measurement system; an Inertial Measurement Unit;and a gradiometer.In some embodiments of the present disclosure, there is provided a method (300) comprising:selecting empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103);determining empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determining an optimised value of one or more parameter of a covariance function ^ by fitting thecovariance function ^ to the empirical covariance data ^^, the covariance function ^ defining acovariance between a magnetic field characteristic of a local magnetic field and locations ^ within theinfluence region 105^ ; anddetermining target magnetic field data that is indicative of the magnetic field characteristic atone or more target location using the covariance function ^.In some embodiments, the empirical magnetic field data ^(^^) comprises a first set of magnetic fielddata ^^^^,^^ that is associated with a first flight path (123). The first set of magnetic field data ^^^^,^^comprises magnetic field measurements ^^^^,^^ taken at measurement locations ^^,^ along the firstflight path (123). A first aerial system (122) flies along the first flight path to take the magnetic field measurements ^^^^,^^. The first aerial system (122) is controlled to fly at or towards a target flight pathground clearance when taking the magnetic field measurements ^^^^,^^. The target flight path groundclearance of the first flight path (123) is a first target flight path ground clearance.In some embodiments, the empirical magnetic field data ^(^^) comprises a second set of magnetic fielddata ^^^^,^^ that is associated with a second flight path (125). The second flight path (125) is different from the first flight path (123). The second flight path (125) is below the first flight path (123) along amajority of the second flight path (125). The second set of magnetic field data ^^^^,^^ comprisesmagnetic field measurements ^^^^,^^ taken at measurement locations along the second flight path(125). A second aerial system (142) flies along the second flight path (125) to take the magnetic field measurements The second aerial system (142) is controlled to fly at or towards a target flightpath ground clearance when taking the magnetic field measurements ^^^^,^^. The target flight pathground clearance of the second flight path (125) is a second target flight path ground clearance. The second target flight path ground clearance is less than the first target flight path ground clearance.In some embodiments, the method (300) comprises defining the one or more target location In someembodiments, the method (300) comprises defining the one or more target location at a targetground clearance that is different to the target flight path ground clearance of the first flight path (123). Insome embodiments, the method (300) comprises defining the one or more target location at a targetground clearance that is less than the target flight path ground clearance of the first flight path (123). Insome embodiments, the method (300) comprises defining the one or more target location at a targetground clearance that is different to the target flight path ground clearance of the second flight path (125).In some embodiments, the method (300) comprises defining the one or more target location at atarget ground clearance that is greater than the target flight path ground clearance of the second flight path(125). In some embodiments, the method (300) comprises defining the one or more target location ata target ground clearance that is different to the target flight path ground clearance of the first flightpath (123) and the target flight path ground clearance of the second flight path (125). In someembodiments, the method (300) comprises defining the one or more target location at a targetground clearance that is less than the target flight path ground clearance of the first flight path (123) and greater than the target flight path ground clearance of the second flight path (125).In some embodiments, the method (300) comprises defining a plurality of target regions ^^ at the targetground clearance.In some embodiments, the method (300) comprises selecting a target region ^^ of the plurality of targetregions ^^, the one or more target location being within the selected target region ^^.In some embodiments, the method (300) comprises defining the influence region 105^ around the targetregion ^^, the influence region 105^ being a three-dimensional region that is bound by one or moreinfluence region boundary 109^.In some embodiments, the influence region 105^ is bound by at least one of:an upper influence region boundary 109^; a lower influence region boundary 109^; and one or more lateral influence region boundaries 109^.In some embodiments, one or more influence region boundary conditions define boundaries 109^ of theinfluence region 105^ . The one or more influence region boundary conditions may define the upperinfluence region boundary 109^. The one or more influence region boundary conditions may define the lower influence region boundary 109^. The one or more influence region boundary conditions may define the one or more lateral influence region boundaries 109^.In some embodiments, a lateral dimension of the influence region 105^ is greater than a lateraldimension of the target region ^^.In some embodiments, the selected empirical magnetic field data ^(^^) is empirical magnetic field data^(^^) obtained at measurement locations ^^,^ within the influence region 105^ .In some embodiments, the empirical magnetic field data ^(^^) comprises a magnetic field intensityvalue |^| associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^ being within the influence region 105^ . In some embodiments, the empiricalmagnetic field data ^(^^) comprises a horizontal gradient value |^| associated with a measurementlocation ^^,^ of a plurality of measurement locations ^^,^, each measurement location ^^,^ being withinthe influence region 105^ . In some embodiments, the empirical magnetic field data ^(^^) comprises a^^ transverse horizontal gradient value ^ ^^^ associated with a measurement location ^^,^ of a plurality ofmeasurement locations ^^,^, each measurement location ^^,^ being within the influence region 105^ . Insome embodiments, the empirical magnetic field data ^(^^) comprises a longitudinal horizontal gradient^^ value ^ ^^^ associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^. In some embodiments, determining the empirical covariance data ^^comprises defining a plurality ofmeasurement location pairs ^^^,^, ^^,^^, each measurement location pair ^^^,^, ^^,^^ comprising a firstmeasurement location of the influence region 105^ and a second measurement location ^^,^ of theinfluence region 105^. In some embodiments, determining the empirical covariance data ^^comprisesdetermining a value of a first spatial parameter ^ for each measurement location pair thevalue of the first spatial parameter ^ indicating a lateral distance between the first measurement locationand the second measurement location ^^,^ of a respective measurement location pair ^^^,^, Insome embodiments, determining the empirical covariance data ^^comprises determining a value of asecond spatial parameter ^ for each measurement location pair ^^^,^, ^^,^^, the value of the secondspatial parameter ^ being a sum of a ground clearance of the first measurement location ^^,^ and aground clearance of the second measurement location ^^,^ of a respective measurement location pair^^,^^. The value of a second spatial parameter ^ may indicate a vertical distance between the firstmeasurement location the second measurement location ^^,^ of a respective measurement locationpair ^^^,^, In some embodiments, determining the empirical covariance data comprisesgrouping the measurement location pairs ^^^,^, ^^,^^ based on the value of their associated first spatialparameter ^ and second spatial parameter ^, thereby determining a plurality of groups ofmeasurement location pairs ^^^,^, ^^,^^. In some embodiments, determining the empirical covariance data^^ comprises defining groups ^^,^ of magnetic field measurement pairs eachmagnetic field measurement pair corresponding to a respective measurementlocation pair ^^^,^, ^^,^^ and being defined based on the corresponding measurement location pair^^,^^. In some embodiments, determining the empirical covariance data ^^ comprises calculating avariance ^^^^^^,^^ of each group ^^,^ of magnetic field measurement pairs ^^^^,^,^^^. Insome embodiments, the empirical covariance data ^^ comprises the calculated variance ^^^^^^,^^ ofeach group ^^,^ of magnetic field measurement pairs In some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ is associated with arespective first group threshold ^^^. In some embodiments, each group of measurement location pairs ^^,^^ is associated with a respective second group threshold ^^^^^.In some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ comprises measurementlocation pairs ^^^,^, ^^,^^ with first spatial parameter ^ values that are greater than the respective firstgroup threshold ^^^. In some embodiments, each group ^^ of measurement location pairs comprises measurement location pairs with first spatial parameter ^ values that are less thanthe respective second group threshold ^^^^^. In some embodiments, each group of measurementlocation pairs comprises measurement location pairs ^^^,^, ^^,^^ with first spatial parameter^ values that are between the first group threshold ^^^ and the second group threshold of thatgroupIn some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ comprises measurementlocation pairs ^^^,^, ^^,^^ with second spatial parameter ^ values that are greater than the respective firstgroup threshold ^^^. In some embodiments, each group ^^ of measurement location pairs comprises measurement location pairs with second spatial parameter ^ values that are lessthan the respective second group threshold ^^^^^. In some embodiments, each group ^^ of measurementlocation pairs ^^^,^, ^^,^^ comprises measurement location pairs ^ with second spatialparameter ^ values that are between the first group threshold ^^^ and the second group threshold of that group ^^.In some embodiments, the threshold(s) for the first spatial parameter ^ of a group ^^ are different fromthe threshold(s) for the second spatial parameter ^ of that group ^^.In some embodiments, calculating the variance ^^^^^^,^^ of a particular group ^^,^ of magnetic fieldmeasurement pairs ^^^^^,^,^^, ^^^^,^,^^^ comprises calculating: where: is the variance of a group ^^,^ of magnetic field measurementpairs ^^,^ is the particular group ^^,^ of magnetic field measurement pairs ^^^^^,^,^^, ^^^^,^,^^^ of acalculation; ^and ^ are indices indicating the measurement locations ^^,^,^, ^^,^,^ at which the magnetic fieldmeasurements of a magnetic field measurement pair ^^^^^,^,^^, ^^^^,^,^^^ wereobtained; ^is an expectation value function;^^is the set of all measurement locations in the influence region 105^; ^^,^,^is a first measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^; and^^,^,^is a second measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^.In some embodiments, the covariance function ^ is a planar logarithmic covariance function.In some embodiments, determining the target magnetic field data comprises computing a Hilbertspace vector ^ by solving a system of linear equations that relate the empirical magnetic field data ^(^^)to the Hilbert space vector ^ and an auto covariance ^^^(^^, ^^) between measurement locations ^^,^.In some embodiments, determining the target magnetic field data ^ comprises computing an autocovariance matrix ^^^(^^, ^^), using the covariance function ^.In some embodiments, computing the auto covariance matrix ^^^(^^, ^^) comprises computing: In some embodiments, determining the target magnetic field data comprises determining a targetcross covariance matrix ^^^^^^,^, ^^^, the target cross covariance matrix covariance between the magnetic field characteristic at the target locations and the measurementlocations In some embodiments, determining the target magnetic field data comprisesdetermining a target cross covariance matrix ^^^^^^,^, ^^^, the target cross covariance matrix^^^^^^,^, ^^^ indicating a covariance between the magnetic field characteristic at the targetlocations and the magnetic field characteristic at the measurement locations ^^,^.In some embodiments, the target cross covariance matrix ^^^^^^,^, ^^^ is determined using thecovariance function ^. That is, the method (300) comprises determining the target cross covariancematrix ^^^^^^,^, ^^^ using the covariance function ^.In some embodiments, determining the target cross covariance matrix ^^^^^^,^, ^^^ comprisescalculating: In some embodiments, the method (300) comprises determining the target magnetic field data using the target cross covariance matrix ^^^ and the Hilbert space vector ^.In some embodiments, the method (300) comprises:defining a plurality of target regions ^^ at the target ground clearance; anditeratively:selecting a target region ^^ of the plurality of target regions ^^;defining the influence region 105^ around the target region ^^, the influence region 105^ being athree-dimensional region that is bound by one or more influence region boundary 109^; determining the empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determining the optimised value of one or more parameter of the covariance function ^ by fittingthe covariance function ^ to the empirical covariance data ^^; anddetermining target magnetic field data the target magnetic field data beingindicative of the magnetic field characteristic of the local magnetic field at one or more target location on the selected target region ^^;for each target region ^^.In some embodiments, the method (300) comprises constructing a model of the magnetic fieldcharacteristic at the target ground clearance, across the survey area (103), using the target magnetic fielddata ^^^^,^^.In some embodiments of the present disclosure, there is provided a system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the at least one processor (104) to perform the method (300).In some embodiments of the present disclosure, there is provided a system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the system (100) to perform the method (300).In some embodiments of the present disclosure, there is provided a system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at least oneprocessor (104), cause the at least one processor (104) to:select empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103); determine empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determine an optimised value of one or more parameter of a covariance function ^ by fitting thecovariance function ^ to the empirical covariance data ^^, the covariance function ^ defining acovariance between a magnetic field characteristic of a local magnetic field and locations ^ within theinfluence region 105^ ; anddetermine target magnetic field data ^ that is indicative of the magnetic field characteristicat one or more target location using the covariance function ^.In some embodiments, the empirical magnetic field data ^(^^) comprises a first set of magnetic fielddata ^^^^,^^ that is associated with a first flight path (123). The first set of magnetic field data comprises magnetic field measurements ^^^^,^^ taken at measurement locations ^^,^ along the firstflight path (123). A first aerial system (122) flies along the first flight path to take the magnetic field measurements The first aerial system (122) is controlled to fly at or towards a target flight pathground clearance when taking the magnetic field measurements The target flight path groundclearance of the first flight path (123) is a first target flight path ground clearance.In some embodiments, the empirical magnetic field data ^(^^) comprises a second set of magnetic fielddata ^^^^,^^ that is associated with a second flight path (125). The second flight path (125) is differentfrom the first flight path (123). The second flight path (125) is below the first flight path (123) along amajority of the second flight path (125). The second set of magnetic field data comprisesmagnetic field measurements ^^^^,^^ taken at measurement locations along the second flight path(125). A second aerial system (142) flies along the second flight path (125) to take the magnetic field measurements The second aerial system (142) is controlled to fly at or towards a target flightpath ground clearance when taking the magnetic field measurements ^^^^,^^. The target flight pathground clearance of the second flight path (125) is a second target flight path ground clearance. The second target flight path ground clearance is less than the first target flight path ground clearance.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define a plurality oftarget regions ^^ at the target ground clearance.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to select a targetregion ^^ of the plurality of target regions ^^, the one or more target location being within theselected target region ^^.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to define the influenceregion 105^ around the target region ^^, the influence region 105^ being a three-dimensional regionthat is bound by one or more influence region boundary 109^ .In some embodiments, the influence region 105^ is bound by at least one of:an upper influence region boundary 109^; a lower influence region boundary 109^; and one or more lateral influence region boundaries 109^.In some embodiments, one or more influence region boundary conditions define boundaries 109^ of theinfluence region 105^ . The one or more influence region boundary conditions may define the upperinfluence region boundary 109^. The one or more influence region boundary conditions may define the lower influence region boundary 109^. The one or more influence region boundary conditions may define the one or more lateral influence region boundaries 109^.In some embodiments, a lateral dimension of the influence region 105^ is greater than a lateraldimension of the target region ^^.In some embodiments, the selected empirical magnetic field data ^(^^) is empirical magnetic field data^(^^) obtained at measurement locations ^^,^ within the influence region 105^ .In some embodiments, the empirical magnetic field data ^(^^) comprises a magnetic field intensityvalue |^| associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^ being within the influence region 105^ . In some embodiments, the empiricalmagnetic field data ^(^^) comprises a horizontal gradient value |^| associated with a measurementlocation ^^,^ of a plurality of measurement locations ^^,^, each measurement location ^^,^ being withinthe influence region 105^ . In some embodiments, the empirical magnetic field data ^(^^) comprises a^^ transverse horizontal gradient value ^ ^^^ associated with a measurement location ^^,^ of a plurality ofmeasurement locations ^^,^, each measurement location ^^,^ being within the influence region 105^ . Insome embodiments, the empirical magnetic field data ^(^^) comprises a longitudinal horizontal gradient^^ value ^ ^^^ associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^.In some embodiments, determining the empirical covariance data ^^ comprises defining a plurality ofmeasurement location pairs ^^^,^, ^^,^^, each measurement location pair ^^^,^, ^^,^^ comprising a firstmeasurement location of the influence region 105^ and a second measurement location ^^,^ of theinfluence region 105^. In some embodiments, determining the empirical covariance data ^^comprisesdetermining a value of a first spatial parameter ^ for each measurement location pair thevalue of the first spatial parameter ^ indicating a lateral distance between the first measurement location^^,^ and the second measurement location ^^,^ of a respective measurement location pair In some embodiments, determining the empirical covariance data ^^comprises determining a value of asecond spatial parameter ^ for each measurement location pair ^^^,^, ^^,^^, the value of the secondspatial parameter ^ being a sum of a ground clearance of the first measurement location and aground clearance of the second measurement location ^^,^ of a respective measurement location pair^^,^^. In some embodiments, determining the empirical covariance data ^^ comprises grouping themeasurement location pairs ^^^,^, ^^,^^ based on the value of their associated first spatial parameter ^and second spatial parameter ^, thereby determining a plurality of groups of measurement locationpairs In some embodiments, determining the empirical covariance data ^^ comprisesdefining groups ^^,^ of magnetic field measurement pairs each magnetic fieldmeasurement pair corresponding to a respective measurement location pair^^,^^ and being defined based on the corresponding measurement location pair Insome embodiments, determining the empirical covariance data ^^comprises calculating a variance of each group ^^,^ of magnetic field measurement pairs In someembodiments, the empirical covariance data ^^ comprises the calculated variance ^^^^^^,^^ of eachgroup ^^,^ of magnetic field measurement pairs ^^^^^,^,^^, ^^^^,^,^^^.In some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ is associated with arespective first group threshold ^^^. In some embodiments, each group of measurement location pairs ^^,^^ is associated with a respective second group threshold ^^^^^.In some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ comprises measurementlocation pairs with first spatial parameter ^ values that are greater than the respective firstgroup threshold ^^^. In some embodiments, each group ^^ of measurement location pairs comprises measurement location pairs with first spatial parameter ^ values that are less thanthe respective second group threshold ^^^^^. In some embodiments, each group of measurementlocation pairs comprises measurement location pairs ^^^,^, ^^,^^ with first spatial parameter^ values that are between the first group threshold ^^^ and the second group threshold of thatgroupIn some embodiments, each group of measurement location pairs ^^^,^, ^^,^^ comprises measurementlocation pairs ^^^,^, ^^,^^ with second spatial parameter ^ values that are greater than the respective firstgroup threshold ^^^. In some embodiments, each group ^^ of measurement location pairs comprises measurement location pairs with second spatial parameter ^ values that are lessthan the respective second group threshold ^^^^^. In some embodiments, each group ^^ of measurementlocation pairs ^^^,^, ^^,^^ comprises measurement location pairs ^ with second spatialparameter ^ values that are between the first group threshold ^^^ and the second group threshold of that group ^^.In some embodiments, the threshold(s) for the first spatial parameter ^ of a group ^^ are different fromthe threshold(s) for the second spatial parameter ^ of that group ^^.In some embodiments, calculating the variance ^^^^^^,^^ of a particular group ^^,^ of magnetic fieldmeasurement pairs ^^^^^,^,^^, ^^^^,^,^^^ comprises calculating: where: is the variance of a group ^^,^ of magnetic field measurementpairs ^^,^ is the particular group ^^,^ of magnetic field measurement pairs ^^^^^,^,^^, ^^^^,^,^^^ of acalculation; ^and ^ are indices indicating the measurement locations ^^,^,^, ^^,^,^ at which the magnetic fieldmeasurements of a magnetic field measurement pair ^^^^^,^,^^, ^^^^,^,^^^ wereobtained; ^is an expectation value function;^^is the set of all measurement locations in the influence region 105^; ^^,^,^is a first measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^; and^^,^,^is a second measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^.In some embodiments, the covariance function ^ is a planar logarithmic covariance function.In some embodiments, determining the target magnetic field data comprises computing a Hilbertspace vector ^ by solving a system of linear equations that relate the empirical magnetic field data ^(^^)to the Hilbert space vector ^ and an auto covariance ^^^(^^, ^^) between measurement locations ^^,^.In some embodiments, determining the target magnetic field data ^ comprises computing an autocovariance matrix ^^^(^^, ^^), using the covariance function ^.In some embodiments, computing the auto covariance matrix ^^^(^^, ^^) comprises computing: In some embodiments, determining the target magnetic field data comprises determining a targetcross covariance matrix ^^^^^^,^, ^^^, the target cross covariance matrix covariance between the magnetic field characteristic at the target locations and the measurementlocations In some embodiments, determining the target magnetic field data comprisesdetermining a target cross covariance matrix ^^^^^^,^, ^^^, the target cross covariance matrix^^^^^^,^, ^^^ indicating a covariance between the magnetic field characteristic at the targetlocations and the magnetic field characteristic at the measurement locations ^^,^.In some embodiments, the target cross covariance matrix ^^^^^^,^, ^^^ is determined using thecovariance function ^.In some embodiments, determining the target cross covariance matrix ^^^^^^,^, comprisescalculating: In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to determine the targetmagnetic field data ^^^^,^ ^ using the target cross covariance matrix ^^^ and the Hilbertspace vector ^.In some embodiments, computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to:define a plurality of target regions ^^ at the target ground clearance; and iteratively: select a target region ^^ of the plurality of target regions ^^;define the influence region 105^ around the target region ^^, the influence region 105^ being athree-dimensional region that is bound by one or more influence region boundary 109^; determine the empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determine the optimised value of one or more parameter of the covariance function ^ by fitting thecovariance function ^ to the empirical covariance data ^^; anddetermine target magnetic field data the target magnetic field data beingindicative of the magnetic field characteristic of the local magnetic field at one or more target location on the selected target region ^^;for each target region ^^.In some embodiments, the computer-executable instructions (108), when executed by the at least oneprocessor (104), are further configured to cause the at least one processor (104) to construct a model ^ ofthe magnetic field characteristic at the target ground clearance, across the survey area (103), using thetarget magnetic field data In some embodiments, the system (100) comprises:a first aerial system (122); anda second aerial system (142); andthe first flight path (123) is a flight path of the first aerial system (122); andthe second flight path (125) is a flight path of the second aerial system (142).In some embodiments, the first aerial system (122) propels the second aerial system (142).In some embodiments, the first aerial system (122) comprises a first sensor system (124) that obtains the first set of magnetic field data In some embodiments, the second aerial system (142) comprises a second sensor system (144) that obtains the second set of magnetic field data ^^^^,^^.In some embodiments, the first sensor system (124) comprises at least one of a magnetometer; a GlobalNavigation Satellite System module; a height measurement system; an Inertial Measurement Unit; and agradiometer.In some embodiments, the second sensor system (144) comprises at least one of a magnetometer; aGlobal Navigation Satellite System module; a height measurement system; an Inertial Measurement Unit;and a gradiometer. In some embodiments, there is provided a non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method (200). In some embodiments, there is provided a non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the method (300). BRIEF DESCRIPTION OF THE DRAWINGS Embodiments of the present disclosure will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:Figure 1 shows a side view of a surveying system for obtaining empirical magnetic field data, accordingto some embodiments;Figure 2 shows a front view of the surveying system, according to some embodiments;Figure 3 shows an overhead view of a survey area, with a flight path of the surveying system shown,according to some embodiments;Figure 4 shows a schematic diagram of a system, according to some embodiments;Figure 5 shows a process flow diagram of a method, according to some embodiments;Figure 6 shows a graphical representation of a target region of a target surface, with an influence regiondefined around the target region, a source region defined below the target surface, and a plurality of synthetic magnetic dipole source moments positioned at dipole source locations, in accordance with themethod of Figure 5, according to some embodiments;Figure 7 shows the overhead view of the survey area shown in Figure 3, with a first target region at a firstposition and an influence region and a source region defined with respect to the first target region;Figure 8 shows the overhead view of the survey area shown in Figure 3, with a second target region at asecond position and an influence region and a source region defined with respect to the second target region;Figure 9 shows the overhead view of the survey area shown in Figure 3, with a third target region at athird position and an influence region and a source region defined with respect to the third target region;Figure 10A shows a graphical representation of empirical magnetic field data obtained over a part of asurvey area, using a sensor system, with a target flight path ground clearance of the sensor system, whenthe empirical magnetic field data was obtained, being 40m, according to some embodiments;Figure 10B shows a graphical representation of an actual ground clearance of the sensor system when theempirical magnetic field data of Figure 10A was obtained, with blue indicating near-nominal groundclearance and warmer colours indicating higher ground clearance, according to some embodiments;Figure 10C shows a graphical representation of ground elevation of the part of the survey area of Figure10A, with warm colours indicating higher ground elevation, according to some embodiments;Figure 10D shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 10A, for a target surface at a 60m ground clearance, in accordance with amethod described herein, using a square source region with a 2000m side length, a square influenceregion with a 2000m side length, a first layer of synthetic magnetic dipole source moments located at ground level and a second layer of synthetic magnetic dipole source moments located 2000m belowground level, according to some embodiments;Figure 10E shows a graphical representation of a difference between the empirical magnetic field data ofFigure 10A and the target magnetic field data of Figure 10D, where white indicates no difference, blueindicates a negative difference and red indicates a positive difference, according to some embodiments;Figure 10F shows a graphical representation of a misfit (Root Mean Square Error) between the empiricalmagnetic field data of Figure 10A and the target magnetic field data of Figure 10D, where white indicatesno difference and red indicates a difference, according to some embodiments;Figure 11A shows a graphical representation of empirical magnetic field data obtained over the part ofthe survey area of Figure 10A, using a second sensor system, with a target flight path ground clearance ofthe second sensor system, when the empirical magnetic field data was obtained, being 80m, according to some embodiments;Figure 11B shows a graphical representation of an actual ground clearance of the second sensor systemwhen the empirical magnetic field data of Figure 11A was obtained, with blue indicating near-nominal clearance and warmer colours indicating higher ground clearance, according to some embodiments;Figure 11C shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 11A, for a target surface at a 60m ground clearance, in accordance with amethod described herein, using a square source region with a 2000m side length, a square influenceregion with a 2000m side length, a first layer of synthetic magnetic dipole source moments located at ground level and a second layer of synthetic magnetic dipole source moments located 2000m below ground level, according to some embodiments;Figure 11D shows a graphical representation of a difference between the empirical magnetic field data ofFigure 11A and the target magnetic field data of Figure 11C, where white indicates no difference, blue indicates a negative difference and red indicates a positive difference, according to some embodiments;Figure 12A shows a graphical representation of empirical magnetic field data obtained over the part ofthe survey area of Figure 10A, using a first sensor system and a second sensor system, with a target flightpath ground clearance of the first sensor system, when the empirical magnetic field data was obtained,being 40m, and a target flight path ground clearance of the second sensor system, when the empiricalmagnetic field data was obtained, being 80m, according to some embodiments;Figure 12B shows a graphical representation of an actual ground clearance of the first sensor system andthe second sensor system when the empirical magnetic field data of Figure 12A was obtained, with blue indicating near-nominal clearance and warmer colours indicating higher ground clearance, according to some embodiments;Figure 12C shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 12A, for a target surface at a 60m ground clearance, in accordance with amethod described herein, using a square source region with a 2000m side length, a square influence region with a 2000m side length, a first layer of synthetic magnetic dipole source moments located at ground level and a second layer of synthetic magnetic dipole source moments located 2000m below ground level, according to some embodiments;Figure 12D shows a graphical representation of a difference between the empirical magnetic field data ofFigure 12A and the target magnetic field data of Figure 12C, where white indicates no difference, blue indicates a negative difference and red indicates a positive difference, according to some embodiments;Figure 13A shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 12A, for a target surface at a 60m ground clearance, in accordance with a method described herein, using a square source region with a 2000m side length, a square influence region with a 2000m side length, a first layer of synthetic magnetic dipole source moments located at ground level and a second layer of synthetic magnetic dipole source moments located 2000m below ground level, according to some embodiments;Figure 13B shows a graphical representation of a difference between the empirical magnetic field data ofFigure 12A and the target magnetic field data of Figure 13A, where white indicates no difference, blue indicates a negative difference and red indicates a positive difference, according to some embodiments;Figure 13C shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 12A, for a target surface at a 10m ground clearance, in accordance with a method described herein, using a square source region with a 2000m side length, a square influence region with a 2000m side length, a first layer of synthetic magnetic dipole source moments located at ground level and a second layer of synthetic magnetic dipole source moments located 2000m below ground level, according to some embodiments;Figure 13D shows a graphical representation of a difference between the empirical magnetic field data ofFigure 12A and the target magnetic field data of Figure 13C, where white indicates no difference, blue indicates a negative difference and red indicates a positive difference, according to some embodiments;Figure 14A shows a graphical representation of a different subset of empirical magnetic field data to thatof Figure 12A, from the same aeromagnetic survey, according to some embodiments;Figure 14B shows a graphical representation of target magnetic field data calculated using the data ofFigure 14A, in accordance with a method described herein, for a case where all synthetic dipole sourcemoments are at a ground surface (no deep layer source), according to some embodiments;Figure 14C shows a graphical representation of target magnetic field data calculated, in accordance witha method described herein, for a target surface that is a constant 60m above the ground, determined using150m x 150m target regions, a source region and influence region both of 1000m x 1000m, and 2synthetic magnetic dipole source layers, one at ground level and one at 2000m below ground level,according to some embodiments;Figure 14D shows a graphical representation of target magnetic field data calculated, in accordance witha method described herein, for the target surface that is a constant 60m above the ground, like that ofFigure 14C, except a smaller source region and influence region, both of 750m x 750m, were used,according to some embodiments;Figure 15 shows a process flow diagram of another method, according to some embodiments;Figure 16 shows a schematic representation of a target region of a target surface, with an influence regiondefined around the target region in accordance with the method of Figure 15, according to someembodiments;Figure 17 shows a graph including empirical covariance data and a fitted model, according to someembodiments;Figure 18A shows target magnetic field data determined in accordance with the method of Figure 15,where the target surface is 200m above the ground, according to some embodiments;Figure 18B shows target magnetic field data determined in accordance with the method of Figure 15,where the target surface is 80m above the ground, according to some embodiments;Figure 18C shows target magnetic field data determined in accordance with the method of Figure 15,where the target surface is at ground level, according to some embodiments;Figure 19A shows target magnetic field data determined in accordance with the method of Figure 5,where the target surface is at ground level; andFigure 19B shows target magnetic field data determined in accordance with the method of Figure 15,where the target surface is at ground level, according to some embodiments. DETAILED DESCRIPTIONSpecific embodiments of the disclosed system and methods will now be described by way of exampleonly. The terminology used herein is for the purpose of describing specific embodiments of the disclosureonly and is not intended to limit the scope of the disclosed system and method. Unless defined otherwise,all technical and scientific terms used herein have the same meanings as commonly understood by one ofordinary skill in the art pertaining to the disclosed system and method.The present disclosure provides a system and methods for aeromagnetic surveying. Specifically, thepresent disclosure provides a system and methods for the acquisition and processing of aeromagneticsurvey data. Aeromagnetic surveying is a geophysical exploration technique that maps variations in theEarth's magnetic field to identify geological structures and potential resource deposits. Traditionalaeromagnetic surveys involve flying an aircraft equipped with a magnetometer at a constant height aboveground level over a survey area. However, conventional methods of aeromagnetic surveying face certainlimitations, such as a trade-off between data resolution and survey efficiency, and potential hazards in areas with challenging terrain. A particular technical challenge exists in using magnetic field data obtained using different, spatiallyseparated sensors, to estimate characteristics of a local magnetic field (such as the Earth’s magnetic field)at spatial locations different to those for which measurements were made. Aeromagnetic surveys typicallyemploy sensors mounted on aircraft to measure the local magnetic field. However, the quality andresolution of these measurements can be limited by the height above ground level at which the aircraftoperates. It will be appreciated that throughout this description, a height above ground level mayalternatively be referred to as a ground clearance. Similarly, a ground clearance may be understood tomean a height above ground level. The ground clearance of a particular point may be measured along avertical axis extending from a point on the ground directly below the relevant point. To overcome quality and resolution challenges provided by operating aircraft at a relatively large groundclearance, an aeromagnetic survey operation may utilise a combination of sensor systems – one or moremounted directly on the aircraft and another housed in a towed "bird" flying at a lower ground clearance.This dual-sensor approach aims to capture both broad-scale magnetic features from the sensors on theaircraft at the higher ground clearance and finer details from sensors on the bird flying closer to theground.Significant operational benefits can be achieved where data from an aircraft-mounted sensor and a lowerbird sensor can be merged, with the goal of reconstructing the magnetic field at an intermediate groundclearance. Performing such a reconstruction presents several challenges, including accurately estimatingintermediate magnetic field intensities and gradients, dealing with potentially different noisecharacteristics between the various sensor systems and optimizing the trade-off between data resolution,proximity to the ground, and noise reduction in the final processed result.By addressing these challenges, the systems and methods described herein can enable the provision of amore accurate and detailed representation of the magnetic field at intermediate spatial positions for whichexplicit magnetic field data has not been obtained, enabling improved geological interpretations and more effective resource exploration activities. Further, the systems and methods described herein can enable generation of estimations of magnetic field data at ground clearances above those for which empirical magnetic field data was obtained, or ground clearances below those for which empirical magnetic field data was obtained.Specifically, the system and methods disclosed can enable magnetic field measurements taken alongvarious aerial flight paths to be used to infer magnetic field characteristics at target points that do not fall on the flight paths. In particular, magnetic field measurements taken along vertically offset flight paths can be used to estimate magnetic field characteristics such as magnetic field intensity and magnetic field gradient at target points that are between the vertically offset flight paths.A method described herein involves fitting simulated magnetic dipole source moments, herein referred toas synthetic magnetic dipole source moments, to empirical magnetic field data. These synthetic magneticdipole source moments are then used to calculate magnetic field characteristics at new locations. Themethod uses a finite number of synthetic magnetic dipole source moments distributed in two layers, oneat ground surface and one at a depth below the ground surface. The dual-layer synthetic magnetic dipolesource moment implementation allows for both induced magnetism and permanent magnetisation in areconstruction of a synthetic magnetic field from the simulated magnetic dipole source moments.Another method described herein involves the computation of empirical covariance data using theempirical magnetic field data, and the fitting of a covariance function to the empirical covariance data. Aplurality of spatial boundary thresholds (i.e. ‘bins’) are defined and measurement locations at which empirical magnetic field data has been obtained are grouped based on the spatial boundary thresholds. The variance of each group is calculated, with the empirical covariance data comprising the calculated variance of each group. The parameters of a covariance function are determined by fitting the covariancefunction to the empirical covariance data. The covariance function and the empirical magnetic field dataare then used to calculate magnetic field characteristics at new locations.The methods described herein provide advanced techniques for the processing of aeromagnetic data. The methods can efficiently process large data sets and perform stable downward continuation, thereby increasing the value and interpretability of aeromagnetic survey data. System 100Figure 4 shows a system 100, according to some embodiments of the present disclosure. The system 100may be referred to as an aeromagnetic surveying system. The system 100 may be referred to as amagnetic data processing system. The system 100 may be referred to as an aeromagnetic survey dataprocessing system. Surveying System 120The system 100 comprises a surveying system 120. The surveying system 120 is configured to conduct anaeromagnetic survey of a survey area. That is, the surveying system 120 is operable to perform anaeromagnetic survey of the survey area. The surveying system 120 flies along a flight path over the survey area in order to conduct the aeromagnetic survey. Onboard sensors sense characteristics of a localmagnetic field as the surveying system 120 flies along the flight path with the sensed characteristics beingstored as empirical magnetic field data.The surveying system 120 obtains empirical magnetic field data ^(^) that is associated with a plurality ofmeasurement locations ^^. The empirical magnetic field data ^(^) comprises measurements ^(^^) ofone or more characteristics of the local magnetic field at a number of measurement locations ^^. Eachmeasurement ^(^^) is associated with a respective measurement location ^^. In particular, eachmeasurement ^(^^) indicates one or more characteristic of a local magnetic field at the associatedmeasurement location ^^. In some embodiments, the empirical magnetic field data ^(^) may be said tocomprise the measurement locations ^^. That is, the empirical magnetic field data ^(^) may comprisemeasurements ^(^^) of one or more characteristics of the local magnetic field, and the associatedmeasurement location ^^ of each measurement ^(^^). That is, the empirical magnetic field data ^(^)may comprise the measurements ^(^^) of one or more characteristics of the local magnetic field, and theassociated measurement location ^^ at which each measurement ^(^^) was obtained.It will be understood that throughout this description, the local magnetic field is the Earth’s magnetic field, and may also be influenced by magnetic fields of other sources such as nearby electronicequipment, if present. ^ is a matrix comprising each measurement location ^^. In other words, eachmeasurement location ^^ ∈ ^, ∀ ^.The surveying system 120 obtains empirical magnetic field data ^(^^) at each of a plurality ofmeasurement locations ^^. Throughout this description, it will be appreciated that where empiricalmagnetic field data ^(^^) is referred to with the label ^(^^), the empirical magnetic field data ^(^^) isassociated with a particular measurement location ^^. Where empirical magnetic field data ^(^) isreferred to with the label ^(^), the empirical magnetic field data ^(^) is associated with the plurality ofmeasurement location ^^. That is, empirical magnetic field data ^(^) is the set of all empirical magneticfield data collected at all measurement locations ^^of the survey.The measurement locations ^^ of the aeromagnetic survey are referred to with ^ as an index. The value ofthe ^ index indicates the position of a particular measurement location ^^. This may be a spatial position.This may be a position in the matrix ^. This may be both a spatial position and a position in the matrix ^.The value of the ^ index uniquely identifies a particular measurement location ^^ from othermeasurement locations ^^. The matrix ^ may be referred to as a measurement location matrix ^. Thesurveying system 120 takes magnetic field measurements at a total of ^ spatial positions, in theaeromagnetic survey. That is, the surveying system 120 takes magnetic field measurements at ^ locationsin the aeromagnetic survey. The magnetic field measurements comprise magnetic field intensitymeasurements. The magnetic field measurements comprise magnetic field gradient measurements.Therefore, the index ^ of the measurement locations ^^ may take a value between 1 and ^. Expresseddifferently, 1 ≤ ^ ≤ ^.First Aerial System 122 The system 100 comprises a first aerial system 122. In particular, the surveying system 120 comprises thefirst aerial system 122. The first aerial system 122 flies over the survey area during the performance of anaeromagnetic survey. The first aerial system 122 comprises a propulsion system. The propulsion systempropels the first aerial system 122, such that the first aerial system 122 can fly. The first aerial system 122comprises a steering system. The steering system can be used to control a heading of the first aerialsystem 122. The illustrated first aerial system 122 is in the form of a plane.The first aerial system 122 comprises at least one processor 126. The at least one processor 126 may bereferred to as a first aerial system processor. In some embodiments, the first aerial system 122 comprisesa plurality of processors 126. The first aerial system 122 comprises a memory 128. Memory 128 may bereferred to as first aerial system memory. Memory 128 stores computer-executable instructions 130. Thecomputer-executable instructions 130 may be referred to as first aerial system computer-executableinstructions. The computer-executable instructions 130 may be referred to as program instructions. The atleast one processor 126 is operably connected to memory 128. That is, the at least one processor 126 isconfigured to communicate with memory 128. The computer-executable instructions 130 are accessibleby the at least one processor 126. The at least one processor 126 is configured to execute thecomputer-executable instructions 130. The at least one processor 126 executes the computer-executableinstructions to perform certain functionality described herein. The computer-executable instructions may comprise executable program code modules that are configured to be executed by the at least one processor 126.The at least one processor 126 comprises one or more microprocessors, central processing units (CPUs),application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs), tensor processing units (TPUs) or other processors capable of reading and executing computer-executableinstructions. It will be appreciated that the at least one processor 126 may be a distributed processor. Thatis, one or more processor of the at least one processor 126 may be physically separated from one or moreother processor of the at least one processor 126.Memory 128 may comprise one or more volatile or non-volatile memory types. Memory 128 maycomprise at least one of random-access memory (RAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM) and flash memory. Memory 128 may comprise one ormore computer-readable storage medium. A computer-readable storage medium can be a medium that can tangibly contain or store computer-executable instructions. In some examples, the storage medium is a transitory computer-readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and / or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well aspersistent solid-state memory such as flash memory, solid state drives, and the like.The first aerial system 122 comprises a network interface 132. The network interface 132 facilitatescommunication between the first aerial system 122 and one or more other apparatus and / or computingdevices. The at least one processor 126 is operably connected to the network interface 132. In otherwords, the at least one processor 126 is configured to communicate with the network interface 132. Thenetwork interface 132 may comprise a combination of network interface hardware and network interfacesoftware suitable for establishing, maintaining and facilitating communication over a relevant communications network. Examples of a suitable communications network include a cloud servernetwork, wired or wireless internet connection, Bluetooth™ or other near field radio communication, anda physical network such as a wired Universal Serial Bus (USB) network or an Ethernet network.The first aerial system 122 comprises a sensor system 124. The sensor system 124 may be referred to as asensor system 124 of the first aerial system 122. The sensor system 124 may be referred to as a firstsensor system 124. The sensor system 124 is mounted to the first aerial system 122. The at least oneprocessor 126 uses the sensor system 124 to obtain empirical measurements indicative of characteristics of the magnetic field at the sensor system 124. The at least one processor 126 stores these measurementsin memory 128 as sensor system data. The sensor system data generated using the sensor system 124 ofthe first aerial system 122 can be referred to as empirical magnetic field data ^(^^). Specifically, thesensor system data generated using the sensor system 124 of the first aerial system 122 can be referred toas empirical magnetic field data ^(^^) that is associated with measurement locations ^^ of a first subset^^ of the plurality of measurement locations ^^. ^^ is a matrix comprising each measurementlocation ^^. That is, ^^ is a matrix that includes each measurement location ^^ of the first subset ^^ ofthe plurality of measurement locations ^^. In other words, ^^ ∈ ^^, ∀ ^.Throughout this description, it will be appreciated that where empirical magnetic field data ^(^^) isreferred to with the label ^(^^), the empirical magnetic field data ^(^^) is associated with a particularmeasurement location ^^of the first subset ^^of the plurality of measurement locations ^^. Whereempirical magnetic field data ^(^^) is referred to with the label ^(^^), the empirical magnetic field data^(^^) is associated with the plurality of measurement locations ^^ of the first subset ^^ of the pluralityof measurement locations ^^. That is, empirical magnetic field data ^(^^) is the set of all empiricalmagnetic field data collected at all measurement locations ^^of the first subset ^^of the plurality of measurement locations ^^.The measurement locations ^^ of the first subset ^^ are on a first flight path 123 (see Figures 1, 6 and16). The measurement locations ^^ are referred to with ^ as an index. The value of ^ indicates theposition of a particular measurement location ^^. This may be a spatial position. This may be a position in the matrix ^^. This may be both a spatial position and a position in the matrix ^^. The matrix ^^may be referred to as a first flight path measurement location matrix ^^. The surveying system 120 recordsmagnetic field data at a total of ^ spatial positions along the first flight path 123. That is, the surveyingsystem 120 takes ^ total magnetic field measurements along the first flight path 123. Therefore, the index^ of the measurement locations ^^ may take a value between 1 and ^. Expressed differently, 1 ≤ ^ ≤ ^.It will be appreciated that ^ < ^.The measurement locations ^^ of the first subset ^^ of the plurality of measurement locations ^^ eachrepresent a three-dimensional point in space. That is, ^^ = {^^ , ^^ , ^^}. In this case, ^^ is an x-coordinateof the relevant point in space, ^^ is a y-coordinate of the relevant point in space and ^^ is a z-coordinateof the relevant point in space. ^^may be a longitude of the point in space. ^^may be a latitude of thepoint in space. ^^ may be a height of the point in space, relative to a reference location. This may be aheight above ground level or a height above sea level. The total number of measurement locations ^^^^^^^^within the first set ^^ of measurement locations ^^ is ^. ^ is an integer. So ^^ = ^ … … … ^. Each^^^^^^{^^ , ^^ , ^^} item of the first flight path measurement location matrix ^^ is a measurement location ^^associated with the first flight path 123. So ^^ is a matrix that comprises each measurement location ^^along the first flight path 123. The measurement locations ^^ of the first flight path measurement locationmatrix ^^ may be referred to as first flight path measurement locations ^^.In some embodiments, the at least one processor 126 generates sensor system data using the sensorsystem 124. That is, the at least one processor 126 generates the empirical magnetic field data ^(^^)using the sensor system 124. In other words, the at least one processor 126 of the first aerial system 122generates empirical magnetic field data ^(^^) at each of the measurement locations ^^ of the first subset^^ of the measurement locations ^^ using the sensor system 124. The at least one processor 126 maygenerate the sensor system data using one or more subsystem of the sensor system 124. That is, the atleast one processor 126 may generate the empirical magnetic field data ^(^^) using one or moresubsystem of the sensor system 124.In some embodiments, the sensor system 124 may be said to generate the sensor system data. That is, thesensor system 124 generates the empirical magnetic field data ^(^^). In other words, the sensor system124 of the first aerial system 122 generates empirical magnetic field data ^(^^) at each of themeasurement locations ^^ of the first subset ^^ of the measurement locations ^^. One or more subsystemof the sensor system 124 may generate the sensor system data. That is, one or more subsystem of thesensor system 124 may generate the empirical magnetic field data ^(^^). In such cases, the at least oneprocessor 126 may receive the sensor system data from the sensor system 124. In other words, the at leastone processor 126 may receive the empirical magnetic field data ^(^^) from the sensor system 124 of thefirst aerial system 122. As described herein, the at least one processor 126 stores the sensor system data inmemory 128. In some embodiments, the at least one processor 126 generates part of the sensor system data using the sensor system 124. The sensor system 124 may generate part of the sensor system data, which may be received by the at least one processor 126.The sensor system 124 comprises a magnetometer system (not shown). The magnetometer system can beused to take magnetometer system measurements. The magnetometer system measurements can be considered magnetometer system data. Therefore, the magnetometer system is configured to enable thegeneration of magnetometer system data. In some embodiments, the at least one processor 126 uses themagnetometer system to generate magnetometer system data. In some embodiments, the magnetometer system can be said to generate the magnetometer system data. In such a case, the at least one processor 126 may receive the magnetometer system data from the magnetometer system.The magnetometer system data is indicative of one or more magnetic field characteristics. In particular,the magnetometer system data indicates one or more characteristics of the magnetic field at the spatialposition of the magnetometer system, at the particular points in time at which measurements are made.This may include a superposition of the Earth’s magnetic field and locally generated magnetic fields (generated, for example, through operation of electrical equipment in the vicinity of the magnetometersystem). The magnetometer system data comprises a magnetometer system data set. Values of elementsof the magnetometer system data set reflect measurements of the magnetic field at the spatial position ofthe magnetometer system, at the particular points in time at which measurements are made. For example,the magnetometer system may be used to measure one or more of magnetic field intensity, magnetic field direction and individual components of the magnetic field in different directions. The sensor system data comprises the magnetometer system data. The magnetometer system comprises a magnetometer. The magnetometer may be a Caesium VapourMagnetometer. The magnetometer measures magnetic field strength. The magnetometer measuresmagnetic field intensity. The magnetometer measures magnetic field direction. The magnetometergenerates magnetometer data. The magnetometer data may comprise one or more of magnetic fieldstrength data, magnetic field intensity data and magnetic field direction data. The magnetic field strengthdata is indicative of a strength of a magnetic field at the magnetometer, over the time window during which measurements are made. The magnetic field strength data comprises a plurality of magnetic field strength values, each associated with a time at which the respective measurement was taken. The magnetic field intensity data is indicative of an intensity of the magnetic field at the magnetometer, over the time window during which measurements are made. The magnetic field intensity data comprises a plurality of magnetic field intensity values, each associated with the time at which the respective measurement was taken. The magnetic field direction data is indicative of a direction of the magneticfield at the magnetometer, over the time window during which measurements are made. The magneticfield direction data comprises a plurality of magnetic field direction values, each associated with the time at which the respective measurement was taken. The magnetic field characteristics indicated by the magnetometer system data may comprise one or more of magnetic field strength, magnetic field intensity and magnetic field direction. Each measured magnetic field characteristic may be associated, in the magnetometer system data, with a three-dimensionalposition. The relevant three-dimensional position will be the position of the magnetometer system at the time the measurement was made. In use, the magnetometer system is used to measure the strength and / or direction of the local magnetic field at various points in space. The sensor system 124 may comprise a plurality of magnetometers. That is, the magnetometer system of the sensor system 124 may comprise a plurality of magnetometers. For example, the sensor system 124may comprise three magnetometers. A first magnetometer may be located at a first lateral position of thefirst aerial system 122. For example, the first magnetometer may be located on a first wingtip of the firstaerial system 122. Another magnetometer may be located at a second lateral position of the first aerialsystem 122. For example, the additional magnetometer may be located on a second wingtip of the firstaerial system 122. Yet another magnetometer may be located at a longitudinal position of the first aerialsystem 122. The longitudinal position may be longitudinally offset with respect to the first lateralposition. The longitudinal position may be longitudinally offset with respect to the second lateral position.For example, this additional magnetometer may be located at a tail portion of the first aerial system 122.One or more of the magnetometers may be a Caesium Vapour Magnetometer. In some embodiments, each magnetometer is a Caesium Vapour Magnetometer.The sensor system 124 comprises a Global Navigation Satellite System (GNSS) module (not shown). TheGNSS module can be used to determine GNSS data. That is, the GNSS module can be used to generateGNSS data. In some embodiments, the at least one processor 126 uses the GNSS module to generate theGNSS data. In some embodiments, the GNSS module generates the GNSS data. In such a case, the atleast one processor 126 may receive the GNSS data from the GNSS module. The GNSS data indicates a latitude and a longitude of the first aerial system 122. In particular, the GNSSdata indicates the latitude and longitude of the first aerial system 122 across a time window during whichthe GNSS module is active. The GNSS data may also indicate a height of the first aerial system 122. Thismay be a height with reference to a particular reference point. For example, the height may be a heightabove sea level (i.e. an altitude). The height may be a radial distance from the centre of the Earth. Theheight may be a ground clearance (i.e. a distance between the ground directly beneath the reference pointand the reference point itself). Therefore, the GNSS data may indicate the height of the first aerial system 122 across the time window during which the GNSS module is active. The GNSS data comprises a plurality of positional measurements. Each positional measurement is associated with a particular point intime, that time being the time at which the particular positional measurement was made. The sensorsystem data comprises the GNSS data.The sensor system 124 comprises a height measurement system (not shown). The height measurementsystem comprises a radar altimeter. The height measurement system can be used to determine groundclearance data. That is, the height measurement system can be used to generate ground clearance data. Insome embodiments, the at least one processor 126 uses the height measurement system to generate theground clearance data. In some embodiments, the height measurement system generates the groundclearance data. In such a case, the at least one processor 126 may receive the ground clearance data from the height measurement system.The ground clearance data indicates a distance between the first aerial system 122 and a referenceposition. The reference position may be the ground level below the first aerial system 122 at the times therelevant measurements were taken. Thus, the ground clearance data indicates the distance between thefirst aerial system 122 and the ground. In other words, the ground clearance data indicates a groundclearance of the first aerial system 122 at points in time during which the ground clearance data wasrecorded. Expressed differently, the ground clearance data indicates a height above ground level of thefirst aerial system 122 over time.The ground clearance data indicates the ground clearance of the first aerial system 122 across a time window during which the height measurement system is active. The ground clearance data comprises a plurality of ground clearance measurements. Each ground clearance measurement is associated with a particular point in time, that time being the time at which the particular ground clearance measurementwas conducted. The sensor system data comprises the ground clearance data. The ground clearance datamay alternatively be referred to as height data. The sensor system 124 comprises a gradiometer (not shown). The gradiometer can be used to determine magnetic field gradient data. That is, the gradiometer can be used to generate magnetic field gradient data. In some embodiments, the at least one processor 126 uses the gradiometer to generate the magnetic field gradient data. In some embodiments, the gradiometer can be said to generate the magnetic field gradient data. In such a case, the at least one processor 126 may receive the magnetic field gradient data from the gradiometer. The magnetic field gradient data indicates a gradient of a magnetic field. In particular, the magnetic field gradient data indicates the gradient of the magnetic field at the gradiometer, at a particular point in time. The magnetic field gradient data comprises a plurality of magnetic field gradient measurements. Each magnetic field gradient measurement is associated with a particular point in time, that time being the time at which the particular magnetic field gradient measurement was made. Each magnetic field gradientmeasurement is associated with a particular location, that location being the location at which theparticular magnetic field gradient measurement was made. The sensor system data comprises the magnetic field gradient data.The sensor system 124 comprises an inertial measurement unit (IMU) (not shown). The IMU comprisesan accelerometer. The accelerometer can be used to determine acceleration data. That is, the accelerometer can be used to generate acceleration data. In some embodiments, the at least one processor 126 uses the accelerometer to generate the acceleration data. In some embodiments, the accelerometergenerates the acceleration data. In such a case, the at least one processor 126 may receive the accelerationdata from the accelerometer.The acceleration data indicates an acceleration of the first aerial system 122. In particular, the accelerationdata indicates the acceleration of the first aerial system 122 across a time window during which the accelerometer is active. The acceleration data comprises a plurality of acceleration measurements. Each acceleration measurement is associated with a particular point in time, that time being the time at which the particular acceleration measurement was made. The acceleration data is indicative of the accelerationof the first aerial system 122 in at least one of a first direction, a second direction and a third direction.The first direction, second direction and third direction may be orthogonal with respect to each other. Oneor more of the acceleration measurements may indicate the instantaneous acceleration of the first aerial system 122 at the associated point in time. In particular, an acceleration measurement may indicate the instantaneous acceleration of the first aerial system 122, in at least one of the first direction, the seconddirection and the third direction. In some cases, an acceleration measurement may indicate theinstantaneous acceleration of the first aerial system 122, in each of the first acceleration direction, second acceleration direction and third acceleration direction. The sensor system data comprises the acceleration data.The IMU comprises a gyroscope. The gyroscope can be used to determine orientation data. That is, thegyroscope can be used to generate orientation data. The orientation data may alternatively be referred to as gyroscopic data. In some embodiments, the at least one processor 126 uses the gyroscope to generate the orientation data. In some embodiments, the gyroscope generates the orientation data. In such a case,the at least one processor 126 may receive the orientation data from the gyroscope.The orientation data indicates an orientation of the first aerial system 122. In particular, the orientationdata indicates the orientation of the first aerial system 122 across a time window during which the gyroscope is active. The orientation data comprises a plurality of orientation measurements. Each orientation measurement is associated with a particular point in time, that time being the time at which theparticular orientation measurement was made. The sensor system data comprises the orientation data.The at least one processor 126 is operably connected to the sensor system 124. In other words, the at leastone processor 126 is configured to communicate with the sensor system 124. The at least one processor126 may be configured to communicate with one or more of the subsystems of the sensor system 124. This communication may be one-way communication. That is, the at least one processor 126 may sample sensor readings generated using the sensor system 124. The communication between the at least one processor 126 and the sensor system 124 may be two-way communication. Thus, the at least oneprocessor 126 may transmit data to, and receive data from, the sensor system 124. The at least oneprocessor 126 receives the sensor system data from the sensor system 124. That is, the at least oneprocessor 126 receives the sensor system data generated using the sensor system 124. The at least oneprocessor 126 stores the sensor system data generated using the sensor system 124 in memory 128. Thecomputer-executable instructions stored in memory 128 may define how the at least one processor 126interacts with the sensor system 124, in use. It will be understood that at least one of the magnetometer system, magnetometer(s), GNSS module, height measurement system, gradiometer, IMU, accelerometer and gyroscope may be considered a subsystem of the sensor system 124. In some embodiments, each of the magnetometer system, magnetometer(s), GNSS module, height measurement system, gradiometer, IMU, accelerometer and gyroscope are subsystems of the sensor system 124. Second Aerial System 142The system 100 comprises a second aerial system 142. In particular, the surveying system 120 comprisesthe second aerial system 142. The second aerial system 142 flies over the survey area during theperformance of an aeromagnetic survey. In the illustrated embodiment, the second aerial system 142 istowed by the first aerial system 122. Specifically, the second aerial system 142 is in the form of a towed‘bird’. The second aerial system 142 comprises a body. The body houses one or more components of thesecond aerial system 142.The second aerial system 142 comprises at least one processor 146. The at least one processor 146 of thesecond aerial system 142 may be referred to as a second aerial system processor. In some embodiments,the second aerial system 142 comprises a plurality of processors 146. The second aerial system 142comprises a memory 148. Memory 148 may be referred to as second aerial system memory. Memory 148stores computer-executable instructions 150. The computer-executable instructions 150 may be referredto as second aerial system computer-executable instructions. The computer-executable instructions 150may be referred to as program instructions. The at least one processor 146 is operably connected tomemory 148. That is, the at least one processor 146 is configured to communicate with memory 148. Thecomputer-executable instructions 150 are accessible by the at least one processor 146. The at least oneprocessor 146 is configured to execute the computer-executable instructions 150. The at least oneprocessor 146 executes the computer-executable instructions to perform certain functionality describedherein. The computer-executable instructions may comprise executable program code modules that are configured to be executed by the at least one processor 146.The at least one processor 146 of the second aerial system 142 comprises one or more microprocessors,central processing units (CPUs), application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs), tensor processing units (TPUs) or other processors capable of readingand executing computer-executable instructions. It will be appreciated that the at least one processor 146may be a distributed processor. That is, one or more processor of the at least one processor 146 may bephysically separated from one or more other processor of the at least one processor 146.Memory 148 of the second aerial system 142 may comprise one or more volatile or non-volatile memorytypes. Memory 148 may comprise at least one of random-access memory (RAM), read-only memory(ROM), electrically erasable programmable read-only memory (EEPROM) and flash memory.Memory 148 may comprise one or more computer-readable storage medium. A computer-readablestorage medium can be a medium that can tangibly contain or store computer-executable instructions. In some examples, the storage medium is a transitory computer-readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and / or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD,DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash memory, solid statedrives, and the like.The second aerial system 142 comprises a network interface 152. The network interface 152 of the secondaerial system 142 may be referred to as a second network interface. The network interface 152 of thesecond aerial system 142 facilitates communication between the second aerial system 142 and one ormore other apparatus and / or computing devices. For example, the network interface 152 enables thesecond aerial system 142 to communicate with the first aerial system 122 (e.g. via the network interface132). The at least one processor 146 is operably connected to the network interface 152. In other words,the at least one processor 146 of the second aerial system 142 is configured to communicate with thenetwork interface 152 of the second aerial system 142. The network interface 152 of the second aerialsystem 142 may comprise a combination of network interface hardware and network interface software suitable for establishing, maintaining and facilitating communication over a relevant communications network. Examples of a suitable communications network include a cloud server network, wired orwireless internet connection, Bluetooth™ or other near field radio communication, and a physicalnetwork such as a wired Universal Serial Bus (USB) network or an Ethernet network.The second aerial system 142 comprises a sensor system 144. The sensor system 144 may be referred toas a sensor system 144 of the second aerial system 142. The sensor system 144 may be referred to as asecond sensor system 144. The sensor system 144 is mounted to the second aerial system 142. The atleast one processor 146 of the second aerial system 142 uses the sensor system 144 to obtain empiricalmeasurements indicative of characteristics of the magnetic field at the sensor system 144. The at least oneprocessor 146 of the second aerial system 142 stores these measurements in memory 148 as sensorsystem data. The sensor system data generated using the sensor system 144 of the second aerial vehicle142 can be referred to as empirical magnetic field data ^(^^). Specifically, the sensor system datagenerated using the sensor system 144 of the second aerial system 142 can be referred to as empiricalmagnetic field data ^(^^) that is associated with measurement locations ^^ of a second subset ^^ of theplurality of measurement locations ^^. ^^ is a matrix comprising each measurement location ^^. That is,^^ is a matrix that includes each measurement location ^^ of the second subset ^^ of the plurality ofmeasurement locations ^^. In other words, ^^ ∈ ^^, ∀ ^.Throughout this description, it will be appreciated that where empirical magnetic field data ^(^^) isreferred to with the label ^(^^), the empirical magnetic field data ^(^^) is associated with a particularmeasurement location ^^of the second subset ^^of the plurality of measurement locations ^^. Whereempirical magnetic field data ^(^^) is referred to with the label ^(^^), the empirical magnetic field data^(^^) is associated with the plurality of measurement locations ^^ of the second subset ^^ of theplurality of measurement locations ^^. That is, empirical magnetic field data ^(^^) is the set of allempirical magnetic field data collected at all measurement locations ^^of the second subset ^^of the plurality of measurement locations ^^.The measurement locations ^^ of the second subset ^^ are on a second flight path 125. The measurementlocations ^^form a set of all measurement locations, including ^^and ^^. The measurement locations ^^are referred to with ^ as an index. The value of ^ indicates the position of a particular measurementlocation ^^. This may be a spatial position. This may be a position in the matrix ^^. This may be both aspatial position and a position in the matrix ^^. The matrix ^^ may be referred to as a second flight pathmeasurement location matrix ^^. The surveying system 120 takes ^ total magnetic field measurementsalong the second flight path 125. Therefore, the index ^ of the measurement locations ^^ may take avalue between 1 and ^. Expressed differently, 1 ≤ ^ ≤ ^. It will be appreciated that ^ < ^.The measurement locations ^^ of the second subset ^^ of the plurality of measurement locations ^^ areeach a three-dimensional point in space. That is, ^^ = {^^ , ^^ , ^^}. In this case, ^^ is an x-coordinate ofthe relevant point in space, ^^ is a y-coordinate of the relevant point in space and ^^ is a z-coordinate ofthe relevant point in space. ^^may be a longitude of the point in space. ^^may be a latitude of the pointin space. ^^ may be a height of the point in space relative to a reference location. This may be a heightabove ground level or a height above sea level. Alternatively, ^^may be a ground clearance of the point. The total number of measurement locations ^^within the second set ^^of measurement locations ^^is ^^^^^^^. ^ is an integer. So ^^ = ^ … … … ^. Each {^^ , ^^ , ^^} item of the second flight path measurement^^^^^^matrix ^^ is a measurement location ^^ associated with the second flight path 125. So ^^ is a matrix thatcomprises each measurement location ^^ along the second flight path 125. The measurement locations ^^of ^ may be referred to as second flight path measurement locations ^^.^ is a matrix that comprises each measurement location ^^ of the survey. In other words, ^ may bereferred to as a survey measurement location matrix ^. The measurement locations ^^ include allmeasurement locations ^^and all measurement locations ^^. This may be expressed as: ^^,^…ü ï ^^,^^^,^. ý … ï ^^,^þIn some embodiments, the at least one processor 146 of the second aerial system 142 generates sensorsystem data using the sensor system 144. That is, the at least one processor 146 of the second aerialsystem 142 generates the empirical magnetic field data ^(^^) using the sensor system 144. In otherwords, the at least one processor 146 of the second aerial system 142 generates empirical magnetic fielddata ^(^^) at each of the measurement locations ^^ of the second subset ^^ of the measurement locations^^using the sensor system 144. The at least one processor 146 may generate the sensor system data usingone or more subsystem of the sensor system 144 of the second aerial system 142. That is, the at least oneprocessor 146 may generate the empirical magnetic field data ^(^^) using one or more subsystem of thesensor system 144 of the second aerial system 142.In some embodiments, the sensor system 144 of the second aerial system 142 may be said to generate thesensor system data. That is, the sensor system 144 of the second aerial system 142 generates the empiricalmagnetic field data ^(^^). In other words, the sensor system 144 of the second aerial system 142generates empirical magnetic field data ^(^^) at each of the measurement locations ^^ of the secondsubset ^^of the measurement locations ^^. One or more subsystem of the sensor system 144 may generate the sensor system data. That is, one or more subsystem of the sensor system 144 may generate the empirical magnetic field data ^(^^). In such cases, the at least one processor 146 of the second aerialsystem 142 may receive the sensor system data from the sensor system 144. In other words, the at leastone processor 146 may receive the empirical magnetic field data ^(^^) from the sensor system 144 of thesecond aerial system 142. As described herein, the at least one processor 146 of the second aerialsystem 142 stores the sensor system data in memory 148.In some embodiments, the at least one processor 146 generates part of the sensor system data using the sensor system 144. The sensor system 144 may generate part of the sensor system data, which may be received by the at least one processor 146.The sensor system 144 of the second aerial system 142 comprises a magnetometer system (not shown).The magnetometer system of the second aerial system 142 may be referred to as a second magnetometersystem. The magnetometer system of the second aerial system 142 can be used to take magnetometersystem measurements. The magnetometer system measurements can be considered magnetometer systemdata. Therefore, the magnetometer system of the second aerial system 142 is configured to enable thegeneration of magnetometer system data. In some embodiments, the at least one processor 146 of thesecond aerial system 142 uses the magnetometer system to generate magnetometer system data. In someembodiments, the magnetometer system can be said to generate the magnetometer system data. In such acase, the at least one processor 146 of the second aerial system 142 may receive the magnetometer systemdata from the magnetometer system. The magnetometer system data is indicative of one or more magnetic field characteristics. In particular, the magnetometer system data indicates one or more characteristics of the magnetic field at the spatialposition of the magnetometer system of the second aerial system 142, at the particular points in time atwhich measurements are made. This may include a superposition of the Earth’s magnetic field and locally generated magnetic fields (generated, for example, through operation of electrical equipment in the vicinity of the magnetometer system). The magnetometer system data comprises a magnetometer system data set. Values of elements of the magnetometer system data set reflect measurements of the magnetic field at the spatial position of the magnetometer system, at the particular points in time at whichmeasurements are made. The sensor system data comprises the magnetometer system data generatedusing the magnetometer system of the second aerial system 142.The magnetometer system of the sensor system 144 comprises a magnetometer. The magnetometer may be a Caesium Vapour Magnetometer. The magnetometer measures magnetic field strength. The magnetometer measures magnetic field intensity. The magnetometer measures magnetic field direction. The magnetometer generates magnetometer data. The magnetometer data may comprise one or more of magnetic field strength data, magnetic field intensity data and magnetic field direction data. The magnetic field strength data is indicative of a strength of a magnetic field at the magnetometer, over the time window during which measurements are made. The magnetic field strength data comprises a plurality ofmagnetic field strength values, each associated with a time at which the respective measurement wastaken. The magnetic field intensity data is indicative of an intensity of the magnetic field at the magnetometer, over the time window during which measurements are made. The magnetic field intensity data comprises a plurality of magnetic field intensity values, each associated with the time at which the respective measurement was taken. The magnetic field direction data is indicative of a direction of the magnetic field at the magnetometer, over the time window during which measurements are made. The magnetic field direction data comprises a plurality of magnetic field direction values, each associated with the time at which the respective measurement was taken. The magnetic field characteristics indicated by the magnetometer system data may comprise one or more of magnetic field strength, magnetic field intensity and magnetic field direction. Each measured magnetic field characteristic may be associated, in the magnetometer system data, with a three-dimensional position. The relevant three-dimensional position will be the position of the magnetometer system at the time the measurement was made. In use, the magnetometer system is used to measure the strength and / or direction of the local magnetic field at various points in space.The sensor system 144 of the second aerial system 142 may comprise a plurality of magnetometers. Thatis, the magnetometer system of the second aerial system 142 may comprise a plurality of magnetometers.The sensor system 144 of the second aerial system 142 comprises a Global Navigation Satellite System(GNSS) module (not shown). The GNSS module can be used to determine GNSS data. That is, the GNSS module can be used to generate GNSS data. In some embodiments, the at least one processor 146 of thesecond aerial system 142 uses the GNSS module to generate the GNSS data. In some embodiments, theGNSS module generates the GNSS data. In such a case, the at least one processor 146 of the second aerialsystem 142 may receive the GNSS data from the GNSS module.The GNSS data generated using the sensor system 144 of the second aerial system 142 indicates a latitudeand a longitude of the second aerial system 142. In particular, the GNSS data indicates the latitude andlongitude of the second aerial system 142 across a time window during which the GNSS module is active.The GNSS data may also indicate a height of the second aerial system 142. This may be a height withreference to a particular reference point. For example, the height may be a height above sea level (i.e. an altitude). The height may be a radial distance from the centre of the Earth. The height may be a ground clearance (i.e. a distance between the ground directly beneath the reference point and the reference pointitself). Therefore, the GNSS data may indicate the height of the second aerial system 142 across the timewindow during which the GNSS module is active. The GNSS data generated using the sensor system 144of the second aerial system 142 comprises a plurality of positional measurements. Each positionalmeasurement is associated with a particular point in time, that time being the time at which the particular positional measurement was made. The sensor system data generated using the sensor system 144 of thesecond aerial system 142 comprises the GNSS data.The sensor system 144 of the second aerial system 142 comprises a height measurement system (notshown). The height measurement system comprises a radar altimeter. The height measurement system canbe used to determine ground clearance data. That is, the height measurement system can be used to generate ground clearance data. In some embodiments, the at least one processor 146 of the second aerial system 142 uses the height measurement system to generate the ground clearance data. In some embodiments, the height measurement system generates the ground clearance data. In such a case, the at least one processor 146 of the second aerial system 142 may receive the ground clearance data from the height measurement system.The ground clearance data generated using the sensor system 144 of the second aerial system 142indicates a distance between the second aerial system 142 and a reference position. The reference positionmay be the ground level below the second aerial system 142 at the times the relevant measurements weretaken. Thus, the ground clearance data indicates the distance between the second aerial system 142 andthe ground. In other words, the ground clearance data indicates a ground clearance of the second aerialsystem 142 at points in time during which the ground clearance data was recorded. Expressed differently,the ground clearance data indicates a height above ground level of the second aerial system 142 over time.The ground clearance data indicates the ground clearance of the second aerial system 142 across a timewindow during which the height measurement system is active. The ground clearance data comprises a plurality of ground clearance measurements. Each ground clearance measurement is associated with a particular point in time, that time being the time at which the particular ground clearance measurementwas made. The sensor system data generated using the sensor system 144 of the second aerial system 142comprises the ground clearance data. The ground clearance data may alternatively be referred to as heightdata.The sensor system 144 of the second aerial system 142 comprises a gradiometer (not shown). Thegradiometer can be used to determine magnetic field gradient data. That is, the gradiometer can be used to generate magnetic field gradient data. In some embodiments, the at least one processor 146 of the second aerial system 142 uses the gradiometer to generate the magnetic field gradient data. In some embodiments, the gradiometer can be said to generate the magnetic field gradient data. In such a case, theat least one processor 146 of the second aerial system 142 may receive the magnetic field gradient datafrom the gradiometer. The magnetic field gradient data indicates a gradient of a magnetic field. In particular, the magnetic field gradient data indicates the gradient of the magnetic field at the gradiometer, at a particular point in time. The magnetic field gradient data comprises a plurality of magnetic field gradient measurements. Each magnetic field gradient measurement is associated with a particular point in time, that time being the time at which the particular magnetic field gradient measurement was made. Each magnetic field gradientmeasurement is associated with a particular location, that location being the location at which theparticular magnetic field gradient measurement was made. The sensor system data generated using thesensor system 144 of the second aerial system 142 comprises the magnetic field gradient data.The sensor system 144 of the second aerial system 142 comprises an inertial measurement unit (IMU) (not shown). The IMU comprises an accelerometer. The accelerometer can be used to determine acceleration data. That is, the accelerometer can be used to generate acceleration data. In some embodiments, the at least one processor 146 of the second aerial system 142 uses the accelerometer to generate the acceleration data. In some embodiments, the accelerometer generates the acceleration data. In such a case, the at least one processor 146 of the second aerial system 142 may receive the acceleration data from the accelerometer.The acceleration data indicates an acceleration of the second aerial system 142. In particular, theacceleration data indicates the acceleration of the second aerial system 142 across a time window duringwhich the accelerometer is active. The acceleration data comprises a plurality of acceleration measurements. Each acceleration measurement is associated with a particular point in time, that time being the time at which the particular acceleration measurement was made. The acceleration datagenerated using the sensor system 144 of the second aerial system 142 is indicative of the acceleration ofthe second aerial system 142 in at least one of a first direction, a second direction and a third direction.The first direction, second direction and third direction may be orthogonal with respect to each other. Oneor more of the acceleration measurements may indicate the instantaneous acceleration of the second aerialsystem 142 at the associated point in time. In particular, an acceleration measurement may indicate theinstantaneous acceleration of the second aerial system 142, in at least one of the first direction, the seconddirection and the third direction. In some cases, an acceleration measurement may indicate theinstantaneous acceleration of the second aerial system 142, in each of the first acceleration direction,second acceleration direction and third acceleration direction. The sensor system data generated using thesensor system 144 of the second aerial system 142 comprises the acceleration data.The IMU of the sensor system 144 comprises a gyroscope (not shown). The gyroscope can be used todetermine orientation data. That is, the gyroscope can be used to generate orientation data. The orientation data may alternatively be referred to as gyroscopic data. In some embodiments, the at leastone processor 146 of the second aerial system 142 uses the gyroscope of the sensor system 144 togenerate the orientation data. In some embodiments, the gyroscope of the sensor system 144 generates theorientation data. In such a case, the at least one processor 146 of the second aerial system 142 mayreceive the orientation data from the gyroscope.The orientation data indicates an orientation of the second aerial system 142. In particular, the orientationdata indicates the orientation of the second aerial system 142 across a time window during which thegyroscope is active. The orientation data comprises a plurality of orientation measurements. Each orientation measurement is associated with a particular point in time, that time being the time at which theparticular orientation measurement was made. The sensor system data generated using the sensor system144 of the second aerial system 142 comprises the orientation data.The at least one processor 146 of the second aerial system 142 is operably connected to the sensor system144 of the second aerial system 142. In other words, the at least one processor 146 is configured tocommunicate with the sensor system 144. The at least one processor 146 may be configured tocommunicate with one or more of the subsystems of the sensor system 144 of the second aerial system142. This communication may be one-way communication. That is, the at least one processor 146 maysample sensor readings generated using the sensor system 144. The communication between the at leastone processor 146 and the sensor system 144 of the second aerial system 142 may be two-waycommunication. Thus, the at least one processor 146 may transmit data to, and receive data from, thesecond sensor system 144. The at least one processor 146 receives the sensor system data from the secondsensor system 144. That is, the at least one processor 146 receives the sensor system data generated usingthe sensor system 144 of the second aerial system 142. The at least one processor 146 stores the sensorsystem data generated using the second sensor system 144 in memory 148. The computer-executableinstructions stored in memory 148 may define how the at least one processor 146 interacts with the sensorsystem 144, in use. It will be understood that at least one of the magnetometer system, magnetometer(s), GNSS module, height measurement system, gradiometer, IMU, accelerometer and gyroscope may be considered a subsystem of the sensor system 144. In some embodiments, each of the magnetometer system, magnetometer(s), GNSS module, height measurement system, gradiometer, IMU, accelerometer and gyroscope are subsystems of the sensor system 124. In the illustrated embodiment, the first aerial system 122 is connected to the second aerial system 142. The first aerial system 122 tows the second aerial system 142. The first aerial system 122 includes apropulsion system that propels the first aerial system 122, enabling the first aerial system 122 to fly. Thefirst aerial system 122 is connected to the second aerial system 142 such that movement of the first aerial system 122 causes corresponding movement of the second aerial system 142. In the illustrated case, thefirst aerial system 122 is connected to the second aerial system 142 via a cable 155. The cable 155 may bereferred to as a tow cable. A first end of the cable 155 is connected to the first aerial system 122. Asecond end of the cable 155 is connected to the second aerial system 142. The first aerial system 122 towsthe second aerial system 142, in use, via the cable 155.An effective length of the cable 155, in use, may be variable. For example, the first end of the cable 155may be connected to a spool of the first aerial system 122. The spool may be rotatable to enable aneffective length of the cable 155 to be controlled.As described herein, the first aerial system 122 is configured to communicate with the second aerial system 142. Similarly, the second aerial system 142 is configured to communicate with the first aerialsystem 122. In some embodiments, the cable 155 enables the first aerial system 122 and the second aerialsystem 142 to communicate. The cable 155 may comprise a communication line that enables thiscommunication. Alternatively, the first aerial system 122 may wirelessly communicate with the second aerial system 142. Similarly, the second aerial system 142 may wirelessly communicate with the firstaerial system 122. This communication may be facilitated by the network interface 132 of the first aerialsystem 122 and the network interface 152 of the second aerial system 142. Where the first aerial system122 communicates with the second aerial system 142, it will be appreciated that the at least one processor126 of the first aerial system 122 may receive data from, or transmit data to, the at least one processor 146of the second aerial system 142. Similarly, where the second aerial system 142 communicates with thefirst aerial system 122, it will be appreciated that the at least one processor 146 of the second aerial system 142 may receive data from, or transmit data to, the at least one processor 126 of the first aerialsystem 122.Computing System 102 The system 100 comprises a computing system 102. The computing system 102 comprises at least one processor 104. In some embodiments, the computing system 102 comprises a plurality of processors 104. The processor(s) 104 of the computing system 102 may be referred to as computing system processors. The computing system 102 may therefore be said to comprise at least one computing system processor. The computing system 102 comprises memory 106. Memory 106 may be referred to as computing system memory. The computing system 102 may therefore be said to comprise computing system memory 106. Memory 106 may comprise or be in the form of one or more non-transitory computer readable storagemedium. Memory 106 stores computer-executable instructions 108. The computer-executable instructions108 may be referred to as computing system computer-executable instructions. The memory 106 may therefore be said to store computing system computer-executable instructions. The computing system 102 comprises a network interface 110. The network interface 110 may be referred to as a computing system network interface. The at least one processor 104 is configured to execute the computer-executable instructions 108. In particular, the at least one processor 104 is configured to execute the computer-executable instructions 108 to cause the computing system 102 to function as described herein. That is, when executed, the computer-executable instructions 108 cause the at least one processor 104 to function as described herein. In some embodiments, the computer-executable instructions 108 are in the form of instruction program code. The at least one processor 104 comprises one or more microprocessors, central processing units (CPUs), application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs), tensor processing units (TPUs) or other processors capable of reading and executing computer-executable instructions. It will be appreciated that the at least one processor 104 may be a distributed processor. That is, one or more processor of the at least one processor 104 may be physically separated from one or moreother processor of the at least one processor 104. In some embodiments, the computing system 102 maybe said to comprise a plurality of processors 104. Where functionality is described herein as beingperformed by the at least one processor 104 of the computing system 102, it will be understood that the relevant functionality may be performed by one or more, or a plurality of processors 104 of the computing system 102. Memory 106 may comprise one or more volatile or non-volatile memory types. For example, memory 106 may comprise at least one of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Memory 106 may comprise oneor more computer-readable storage medium. A computer-readable storage medium can be a medium thatcan tangibly contain or store computer-executable instructions. In some examples, the storage medium is a transitory computer readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and / or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-statememory such as flash memory, solid state drives, and the like.Memory 106 is configured to store the computer-executable instructions 108. The computer-executable instructions 108 are accessible by the at least one processor 104. The computer-executable instructions 108 may be referred to as program instructions. The computer-executable instructions 108 comprise one or more executable code modules. The code modules are configured to be executable by the at least one processor 104. The code modules, when executed by the at least one processor 104, cause the computingsystem 102 to perform certain functionality, as described herein. That is, the code modules, whenexecuted by the at least one processor 104, cause the at least one processor 104 to perform certain functionality, as described herein. The network interface 110 facilitates communication between the computing system 102 and one or moreother components of the system 100. In particular, the network interface 110 enables the computingsystem 102 to communicate with the first aerial system 122. The network interface 110 may also enablethe computing system 102 to communicate with the second aerial system 142. The network interface 110 may comprise a combination of network interface hardware and network interface software suitable for establishing, maintaining and facilitating communication over a relevant communications network. Examples of a suitable communications network include a cloud server network, wired or wirelessinternet connection, Bluetooth™ or other near field radio communication, and / or a physical network suchas a wired Universal Serial Bus (USB) network or an Ethernet network. The computing system 102 comprises a user interface 111. The user interface 111 is configured to enable a user of the computing system 102 to provide an input to the computing system 102. That is, one or more user(s) of the user interface 111 can submit requests to the computing system 102 via the user interface 111. The computing system 102 can provide outputs to the user. In other words, the user interface 111 is configured to enable the computing system 102 to provide one or more outputs to a user. The user interface 111 may comprise one or more user interface components, such as one or more of a display, a touch screen display, a keyboard, a mouse, a camera, a microphone, buttons, switches and lights. The computing system 102 communicates with one or more other computing device via a communications network 154. For example, the computing system 102 communicates with the first aerialsystem 122 via the communications network 154. The first aerial system 122 may transmit sensor systemdata generated using the sensor system 124 of the first aerial system 122 to the computing system 102 forprocessing, via the communications network 154. The first aerial system 122 may transmit sensor systemdata generated using the sensor system 144 of the second aerial system 142 to the computing system 102for processing, via the communications network 154. In some embodiments, the computing system 102communicates with the second aerial system 142 via the communications network 154. For example, thesecond aerial system 142 may transmit sensor system data generated using its sensor system 144 to thecomputing system 102 for processing, via the communications network 154. The system 100 may comprise the communications network 154. Alternatively, one or more components of the system 100 may utilise a third party communications network. That is, the communicationsnetwork 154 may be a third party communications network.The communications network 154 may comprise, or be in the form of, at least one of a wireless local areanetwork (WLAN) such as Wi-Fi (IEEE 82.15.1) or Zigbee (IEE 802.15.4), a wireless wide area network(WWAN) such as cellular 4G LTE and 5G or another cellular network and a low power wide areanetwork (LPWAN) such as SigFox and Lora, Bluetooth™. The communications network 154 maycomprise, or be in the form of, another type of wireless network. The communications network 154 mayinvolve a connection with the Internet. The communications network 138 may comprise, or be in the formof, a wired network. For example, the first aerial system 122 may be connected to the computingsystem 102 using a wired connection (such as a USB connection or an Ethernet connection) to enable datato be transferred between the first aerial system 122 and the computing system 102. The system 100 may be said broadly to comprise at least one processor. A processor of the system 100 may be referred to as a system processor. Therefore, the system 100 may be said to comprise at least onesystem processor. The at least one processor of the system 100 may comprise the at least one processor104 of the computing system 102. The at least one processor of the system 100 may comprise the at leastone processor 126 of the first aerial system 122. The at least one processor of the system 100 maycomprise the at least one processor 146 of the second aerial system 142. The system 100 may therefore be said to comprise a plurality of processors. The system 100 may be said broadly to comprise memory. The memory of the system 100 may be referred to as system memory. Therefore, the system 100 may be said to comprise system memory. The memory of the system 100 may comprise the memory 106 of the computing system 102. The memory of the system 100 may comprise the memory 128 of the first aerial system 122. The memory of the system100 may comprise the memory 148 of the second aerial system 142. The system 100 may therefore besaid to comprise a plurality of memory modules. Obtaining Magnetic Field DataReferring to Figure 3, the surveying system 120 is capable of performing an aeromagnetic survey. Thesurveying system 120 performs the aeromagnetic survey in order to obtain magnetic field data that is used in the methods described herein.The surveying system 120 flies through a survey area 103 to perform the aeromagnetic survey. Thesurvey area 103 is a three-dimensional region in space that extends upwards from ground level. Thesurvey area 103 may be considered to be bound by lateral survey area boundaries. The surveying system120 flies along a surveying flight path 121 to perform the aeromagnetic survey. The surveying flight path 121 is within the lateral survey area boundaries. The illustrated surveying flight path 121 comprises aplurality of parallel, straight portions, connected by curved portions (see Figure 3). The surveying system120 stores sensor system data obtained at a plurality of measurement locations ^^using the sensorsystems 124, 144 during the aeromagnetic survey. In other words, the surveying system 120 measures oneor more characteristic of the local magnetic field at each measurement location ^^during the aeromagnetic survey. The surveying system 120 uses the sensor system 124 of the first aerial system 122 and the sensorsystem 144 of the second aerial system 142 to record sensor system data as the surveying system 120 fliesover the survey area 103. The sensor system data recorded by the surveying system 120 may be referredto as empirical magnetic field data ^(^). This is because the sensor system data is associated with one ormore characteristics of the local magnetic field, such as magnetic field intensity or magnetic fieldgradient, at the measurement locations ^^. The empirical magnetic field data ^(^) of the surveycomprises empirical magnetic field data ^(^^) obtained at each measurement location ^^. For thepurposes of this disclosure, recording data may be considered to be obtaining the relevant data. Similarly, performing measurements and storing the values obtained in performing the measurements may be considered to be obtaining the relevant data. Therefore, the surveying system 120 may be said to obtain sensor system data when performing the aeromagnetic survey. Similarly, the surveying system 120 maybe said to obtain empirical magnetic field data ^(^) when performing the aeromagnetic survey. Asdescribed herein, the empirical magnetic field data ^(^) may also be considered to comprise themeasurement locations ^^. Each measurement locations ^^ may be associated with a respective magneticfield measurement of the sensor system data. The first aerial system 122 and the second aerial system 142 are operably connected. Specifically, the second aerial system 142 is connected to the first aerial system 122 via the cable 155. The propulsionsystem of the first aerial system 122 propels the first aerial system 122, which in turn tows the secondaerial system 142. When performing the aeromagnetic survey, the first aerial system 122 flies along a firstflight path 123 (see Figure 1). The second aerial system 142 flies along a second flight path 125 (seeFigure 1). The first flight path 123 may be characterised by a series of spatial positions along which the first aerialsystem 122 flies, over the survey area 103. The operator of the surveying system 120 defines a targetflight path ground clearance of the first flight path 123. That is, the first flight path 123 is a flight path at atarget flight path ground clearance. The target flight path ground clearance is a height above ground levelat which the first aerial system 122 is to fly, when conducting the aeromagnetic survey. The target flightpath ground clearance of the first flight path 123 may be referred to as a first target flight path groundclearance. The first target flight path ground clearance may be about 40m, 50m, 60m or 70m. In theillustrated embodiment, the first target flight path ground clearance is 50m. It will be appreciated thatduring operation, the ground clearance of the first aerial system 122 may deviate from the first targetflight path ground clearance, for example, because of turbulence. However, it will be understood that thefirst flight path 123 will trend towards the target flight path ground clearance following deviations.One or more of the propulsion system and the steering system of the first aerial system 122 may becontrolled such that the first aerial system 122 flies along the first flight path 123. It will be understood that the at least one processor 126 of the first aerial system 122 may control the first aerial system 122 such that the first aerial system 122 flies along the first flight path 123. The first aerial system 122 may be autonomously controlled such that it flies along the first flight path 123. The second aerial system 142 flies along a second flight path 125. In particular, the second aerial system142 flies along the second flight path 125 as the first aerial system 122 flies along the first flight path 123.The operator of the surveying system 120 defines a target flight path ground clearance of the second flightpath 125. The target flight path ground clearance of the second flight path 125 is a height above groundlevel at which the second aerial system 142 is to fly, when the aeromagnetic survey is being conducted.The target flight path ground clearance of the second flight path 125 may be referred to as a second targetflight path ground clearance. The second target flight path ground clearance may be about 20m, 30m,40m or 50m. In the illustrated embodiment, the second target flight path ground clearance is 30m. It willbe appreciated that during operation, the ground clearance of the second aerial system 142 may deviatefrom the second target flight path ground clearance, for example, because of turbulence. However, it will be understood that the second flight path 125 will trend towards the second target flight path groundclearance following deviations.A length of the cable 155 may be controlled to control the flight path of the second aerial system 142. Aheading control system of the second aerial system 142 may be controlled such that the second aerialsystem 142 flies along the second flight path 125. For example, one or more fins or wings of the secondaerial system 142 may be controlled such that the second aerial system 142 flies at the second target flightpath ground clearance. The at least one processor 146 of the second aerial system 142 may control thesecond aerial system 142 such that the second aerial system 142 flies along the second flight path 125.Specifically, the at least one processor 146 of the second aerial system 142 may control the heading control system to control the flight path of the second aerial system 142.The target flight path ground clearance of the second aerial system 142 is different to the target flight pathground clearance of the first aerial system 122. Specifically, the target flight path ground clearance of thesecond aerial system 142 is less than the target flight path ground clearance of the first aerial system 122.In other words, the target height above ground level of the second aerial system 142 is less than the targetheight above ground level of the first aerial system 122. The second aerial system 142 therefore flies at alower height above ground level than the first aerial system 122 when the surveying system 120 performsthe aeromagnetic survey. This may be expressed differently. In particular, the first flight path 123 isabove the second flight path 125. That is, the first flight path 123 is above the second flight path 125along the first flight path 123 and the second flight path 125. The first flight path 123 is above the secondflight path 125 along the entire length of first flight path 123. The second flight path 125 is below the firstflight path 123 along the entire length of the second flight path 125. The first aerial system 122 thereforeflies at a height above ground level that is greater than that of the second aerial system 142, during theaeromagnetic survey. The first flight path 123 and the second flight path 125 are vertically aligned.The first aerial system 122 operates the sensor system 124 while it is traversing the first flight path 123. In particular, the first aerial system 122 uses the sensor system 124 to generate sensor system data. In some embodiments, the at least one processor 126 of the first aerial system 122 uses the sensor system 124 to generate the sensor system data as the first aerial system 122 travels along the first flight path 123. In some embodiments, the sensor system 124 of the first aerial system 122 generates the sensor system data as the first aerial system 122 flies along the first flight path 123. The at least one processor 126 may receive the generated sensor system data.The sensor system data generated using the sensor system 124 of the first aerial system 122 may beconsidered to be associated with the first flight path 123. This is because the sensor system data indicatescharacteristics of the Earth’s magnetic field (and other local magnetic fields) along the first flightpath 123.The at least one processor 126 of the first aerial system 122 stores this sensor system data in thememory 128 of the first aerial system 122. In other words, the at least one processor 126 of the first aerial system 122 stores values generated as the result of measurements taken using the sensor system 124 in memory 128 as sensor system data.Each measurement ^(^^) taken using the sensor system 124 of the first aerial system 122 is associatedwith a particular time and a particular measurement location ^^. That is, each portion of the sensor systemdata generated using the sensor system 124 of the first aerial system 122 is associated with a particulartime and a particular measurement location ^^. The measurement locations ^^ are measurement locationsof the first subset ^^ of the measurement locations ^^. The time a particular measurement ^(^^) isassociated with is the time at which that measurement ^(^^) was taken. The measurement location ^^that a particular measurement ^(^^) is associated with indicates the location at which thatmeasurement ^(^^) was taken. The sensor system data generated using the sensor system 122 of the firstaerial system 122 may be referred to as empirical magnetic field data ^(^^). A particular instance of thatempirical magnetic field data ^(^^), that is, a portion of the empirical magnetic field data ^(^^) that isassociated with a measurement location ^^of the first subset ^^of the measurement locations ^^may bereferred to as an empirical magnetic field measurement ^(^^). The empirical magnetic field data ^(^^)comprises the empirical magnetic field measurements ^(^^). The empirical magnetic field data ^(^^)comprises the times at which each empirical magnetic field measurement ^(^^) are obtained. That is, theempirical magnetic field data ^(^^) comprises time data associated with each empirical magnetic fieldmeasurement ^(^^). The time data associated with a particular empirical magnetic fieldmeasurement ^(^^) indicates the time at which that empirical magnetic field measurement ^(^^) wasmeasured. That is, the time data comprises a plurality of time measurements, each indicating a time atwhich that empirical magnetic field measurement ^(^^) was measured, with the associated empiricalmagnetic field measurement ^(^^) indicating the one or more local magnetic field characteristic at thattime. The empirical magnetic field data ^(^^) may comprise the time data. The empirical magnetic fielddata ^(^^) may comprise the measurement locations ^^. Each measurement location ^^ is associatedwith a respective empirical magnetic field measurement ^(^^). The measurement location ^^ indicates aspatial location at which the empirical magnetic field measurement ^(^^) was measured. Eachmeasurement location ^^is associated with a respective time. That is, each measurement location ^^is associated with the time data indicating the time at which that empirical magnetic fieldmeasurement ^(^^) was measured at that measurement location ^^.The sensor system data generated using the sensor system 124 of the first aerial system 122 comprises aplurality of measurement locations ^^(see Figure 6). The measurement locations ^^may be stored in a first measurement location matrix ^^. The measurement locations ^^of the sensor system data generatedusing the sensor system 124 of the first aerial system 122 are a first subset ^^ of the measurementlocations ^^. Measurements associated with the magnetic field at the sensor system 124 of the first aerialsystem 122 are each associated with a respective measurement location ^^. Each of these measurementlocations ^^ is on the first flight path 123. The measurement locations ^^ may be determined using theGNSS data. The measurement locations ^^may comprise GNSS data. The measurement locations ^^may be determined using the ground clearance data. The measurement locations ^^ may comprise groundclearance data. The measurement locations ^^ may be determined using the acceleration data. Themeasurement locations ^^ may be determined using the orientation data. The measurement locations ^^associated with the first flight path 123 may be referred to as a first subset of the measurementlocations ^^. A first surface 127 may connect each measurement location ^^ associated with the firstflight path 123 (see Figure 6). The first surface 127 may intersect each measurement location ^^associated with the first flight path 123. That is, the first surface 127 may intersect each measurementlocation ^^ of the first subset ^^ of the measurement locations ^^. Referring to Figure 6, each dot on thefirst surface 127 is a measurement location ^^. Each dot on the first surface 127 is also a measurementlocation ^^ of the first subset ^^ of the measurement locations ^^. Therefore, each dot labelled with ^^on the first surface 127 may also be labelled with ^^.The sensor system data generated using the sensor system 124 of the first aerial system 122 comprisesmagnetic field intensity data. The magnetic field intensity data comprises a magnetic field intensity value|^| associated with each of the plurality of measurement locations ^^ along the first flight path 123. Themagnetic field intensity value |^| associated with a particular measurement location ^^ indicates anintensity of a magnetic field ^ at that measurement location ^^ at the time the sensor system 124 of thefirst aerial system 122 was used to take the measurement.The sensor system data generated using the sensor system 124 of the first aerial system 122 may thereforebe said to comprise a plurality of magnetic field measurements ^(^^). Each magnetic field measurement^(^^) is associated with a respective measurement location ^^. The measurement locations ^^ are on thefirst flight path 123. Therefore, each magnetic field measurement ^(^^) is associated with the first flightpath 123. The magnetic field measurements ^(^^) that are associated with the first flight path 123 may becollectively referred to as a first set of magnetic field measurements ^(^^). The magnetic fieldmeasurements ^(^^) comprise magnetic field intensity values |^| that each indicate the intensity of thelocal magnetic field at a respective measurement location ^^. That is, the magnetic field measurements^(^^) comprise the magnetic field intensity values |^| associated with a measurement location ^^ of thefirst subset ^^ of the measurement locations ^^. The magnetic field measurements ^(^^) that are takenalong the first flight path 123 are stored as the first set of empirical magnetic field data ^(^^).The at least one processor 126 of the first aerial system 122 stores the sensor system data generated usingthe sensor system 124 of the first aerial system 122 in the memory 128 of the first aerial system 122. Thesensor system data generated using the sensor system 124 of the first aerial system 122 may be referred toas empirical magnetic field data ^(^^). The sensor system data generated using the sensor system 124 ofthe first aerial system 122 may be referred to as empirical magnetic field data ^(^^) that is associatedwith the first flight path 123. Therefore, the at least one processor 126 of the first aerial system 122 storesempirical magnetic field data ^(^^) associated with the first flight path 123 in the memory 128 of thefirst aerial system 122. It will be understood that the empirical magnetic field data ^(^^) is a collectivereference to all of the magnetic field measurements ^(^^) taken along the second flight path 125.The first aerial system 122 generates the sensor system data between the start of the aeromagnetic surveyand the end of the aeromagnetic survey. In other words, the first aerial system 122 generates the empiricalmagnetic field data ^(^^) associated with the first flight path 123 between the start of the aeromagneticsurvey and the end of the aeromagnetic survey. The second aerial system 142 operates the onboard sensor system 144 while it is traversing the second flight path 125. In particular, the second aerial system 142 uses its sensor system 144 to generate sensor system data. In some embodiments, the at least one processor 146 of the second aerial system 142 uses the sensor system 144 of the second aerial system 142 to generate the sensor system data as the secondaerial system 142 travels along the second flight path 125. In some embodiments, the sensor system 144of the second aerial system 142 generates the sensor system data as the second aerial system 142 fliesalong the second flight path 125. The at least one processor 146 of the second aerial system 142 may receive the generated sensor system data.The sensor system data generated using the sensor system 144 of the second aerial system 142 may beconsidered to be associated with the second flight path 125. This is because the sensor system dataindicates characteristics of the Earth’s magnetic field (and other local magnetic fields) along the second flight path 125.The at least one processor 146 of the second aerial system 142 stores this sensor system data in thememory 148 of the second aerial system 142. In other words, the at least one processor 146 of the secondaerial system 142 stores values generated as the result of measurements taken using the sensor system 144 in memory 148 as sensor system data.Each measurement ^(^^) taken using the sensor system 144 of the second aerial system 142 is associatedwith a particular time and a particular measurement location ^^. That is, each portion of the sensor systemdata generated using the sensor system 144 of the second aerial system 142 is associated with a particulartime and a particular measurement location ^^. The measurement locations ^^ are measurement locationsof the second subset ^^ of the measurement locations ^^. The time a particular measurement ^(^^) isassociated with is the time at which that measurement ^(^^) was taken. The measurement location ^^that a particular measurement ^(^^) is associated with indicates the location at which that measurement^(^^) was taken. The sensor system data generated using the sensor system 144 of the second aerialsystem 142 may be referred to as empirical magnetic field data ^(^^). A particular instance of thatempirical magnetic field data ^(^^), that is, a portion of the empirical magnetic field data ^(^^) that isassociated with a measurement location ^^ of the second subset ^^ of the measurement locations ^^ maybe referred to as an empirical magnetic field measurement ^(^^). The empirical magnetic fielddata ^(^^) comprises the empirical magnetic field measurements ^(^^). The empirical magnetic fielddata ^(^^) comprises the times at which each empirical magnetic field measurement ^(^^) are obtained.That is, the empirical magnetic field data ^(^^) comprises time data associated with each empiricalmagnetic field measurement ^(^^). The time data associated with a particular empirical magnetic fieldmeasurement ^(^^) indicates the time at which that empirical magnetic field measurement ^(^^) wasmeasured. That is, the time data comprises a plurality of time measurements, each indicating a time atwhich that empirical magnetic field measurement ^(^^) was measured, with the associated empiricalmagnetic field measurement ^(^^) indicating the one or more local magnetic field characteristic at thattime. The empirical magnetic field data ^(^^) may comprise the time data. The empirical magnetic fielddata ^(^^) may comprise the measurement locations ^^. Each measurement location ^^ is associatedwith a respective empirical magnetic field measurement ^(^^). The measurement location ^^ indicates aspatial location at which the empirical magnetic field measurement ^(^^) was measured. Eachmeasurement location ^^is associated with a respective time. That is, each measurement location ^^is associated with the time data indicating the time at which that empirical magnetic fieldmeasurement ^(^^) was measured at that measurement location ^^.The sensor system data generated using the sensor system 144 of the second aerial system 142 comprisesa plurality of measurement locations ^^(see Figure 6). The measurement locations ^^may be stored in asecond measurement location matrix ^^. The measurement locations ^^ of the sensor system datagenerated using the sensor system 144 of the second aerial system 142 are a second subset ^^ of themeasurement locations ^^. Measurements associated with the magnetic field at the sensor system 144 ofthe second aerial system 142 are each associated with a respective measurement location ^^. Each ofthese measurement locations ^^ falls on the second flight path 143. The measurement locations ^^ maybe determined using the GNSS data. The measurement locations ^^may comprise GNSS data. Themeasurement locations ^^ may be determined using the ground clearance data. The measurementlocations ^^ may comprise ground clearance data. The measurement locations ^^ may be determinedusing the acceleration data. The measurement locations ^^may be determined using the orientation data.The measurement locations ^^ associated with the second flight path 125 may be referred to as a secondsubset ^^ of the measurement locations ^^. A second surface 129 may connect each measurementlocation ^^ associated with the second flight path 125 (see Figure 6). The second surface 129 mayintersect each measurement location ^^ associated with the second flight path 125. That is, the secondsurface 129 may intersect each measurement location ^^ of the second subset ^^ of the measurementlocations ^^. Referring to Figure 6, each dot on the second surface 129 is a measurement location ^^.Each dot on the second surface 129 is also a measurement location ^^ of the second subset of themeasurement locations ^^. Therefore, each dot labelled with ^^ on the second surface 129 may also belabelled with ^^.The sensor system data generated using the sensor system 144 of the second aerial system 142 comprisesmagnetic field intensity data. The magnetic field intensity data comprises a magnetic field intensity value|^| associated with each of the plurality of measurement locations ^^ along the second flight path 125.The magnetic field intensity value |^| associated with a particular measurement location ^^ indicates anintensity of a magnetic field ^ at that measurement location ^^ at the time the sensor system 144 of thesecond aerial system 142 was used to take the measurement.The sensor system data generated using the sensor system 144 of the second aerial system 142 maytherefore be said to comprise a plurality of magnetic field measurements ^(^^). Each magnetic fieldmeasurement ^(^^) is associated with a respective measurement location ^^. The measurement locations^^ are on the second flight path 125. Therefore, each magnetic field measurement ^(^^) is associatedwith the second flight path 125. The magnetic field measurements ^(^^) that are associated with thesecond flight path 125 may be collectively referred to as a second set of magnetic field measurements^(^^). The magnetic field measurements ^(^^) comprise magnetic field intensity values |^| that eachindicate the intensity of the local magnetic field at a respective measurement location ^^. That is, themagnetic field measurements ^(^^) comprise the magnetic field intensity values |^| associated with ameasurement location ^^ of the second subset ^^ of the measurement locations ^^. The magnetic fieldmeasurements ^(^^) that are taken along the second flight path 125 are stored as the second set ofempirical magnetic field data ^(^^).The at least one processor 146 of the second aerial system 142 stores the sensor system data generatedusing the sensor system 144 of the second aerial system 142 in the memory 148 of the second aerialsystem 142. The sensor system data generated using the sensor system 144 of the second aerial system142 may be referred to as empirical magnetic field data ^(^^). The sensor system data generated usingthe sensor system 144 of the second aerial system 142 may be referred to as empirical magnetic field data^(^^) that is associated with the second flight path 125. Therefore, the at least one processor 146 of thesecond aerial system 142 stores empirical magnetic field data ^(^^) associated with the second flightpath 125 in the memory 148 of the second aerial system 142. It will be understood that the empiricalmagnetic field data ^(^^) is a collective reference to all of the magnetic field measurements ^(^^) takenalong the second flight path 125.The second aerial system 142 generates the sensor system data between the start of the aeromagneticsurvey and the end of the aeromagnetic survey. In other words, the second aerial system 142 generates theempirical magnetic field data ^(^^) associated with the second flight path 125 between the start of theaeromagnetic survey and the end of the aeromagnetic survey.The sensor system data generated using the sensor system 124 of the first aerial system 122 may beconsidered a first sensor system dataset ^(^^). The sensor system data generated using the sensor system144 of the second aerial system 142 may be considered a second sensor system dataset ^(^^). The sensorsystem data generated using the sensor system 144 of the second aerial system 142 may be combined withthe sensor system data generated using the sensor system 124 of the first aerial system 122. In otherwords, the first sensor system dataset ^(^^) may be combined with the second sensor systemdataset ^(^^). The combined first sensor system dataset and second sensor system dataset may bereferred to as a composite dataset ^(^). Expressed differently: Each measurement ^(^^) of the composite dataset ^(^) is associated with a respective measurementlocation ^^of the plurality of measurement locations ^^. In this case, the plurality of measurementlocations ^^ comprises each measurement location ^^ of the first subset ^^ of the measurementlocations ^^. That is, the plurality of measurement locations ^^ comprises each measurement location ^^that is associated with the first flight path 123. Expressed differently, ^^ ∈ ^^. The plurality ofmeasurement locations ^^comprises each measurement location ^^of the second subset ^^of themeasurement locations ^^. That is, the plurality of measurement locations ^^ comprises eachmeasurement location ^^that is associated with the second flight path 125. Expressed differently,^^ ∈ ^^.The sensor system data generated during the aeromagnetic survey may be referred to as empirical data. The combined dataset comprising the sensor system data generated using the sensor system 124 of thefirst aerial system 122 and the sensor system data generated using the sensor system 144 of the secondaerial system 142 may be referred to as empirical magnetic field data ^(^). This is because the data wasobtained through measurements, or inferences made from measurements. It will be appreciated that thecombined dataset ^(^) comprises a first subset of empirical magnetic field data ^(^^) that is associatedwith the first flight path 123. That is, the combined dataset ^(^) comprises empirical magnetic field data^(^^) of a first subset of the combined dataset ^(^). Similarly, the combined dataset ^(^) comprises asecond subset of empirical magnetic field data ^(^^) that is associated with the second flight path 125.That is, the combined dataset ^(^) comprises empirical magnetic field data ^(^^) of a second subset ofthe combined dataset ^(^). The combined dataset ^(^) may be referred to as empirical magnetic fielddata ^(^). The combined dataset ^(^) may be referred to as survey magnetic field data ^(^). Thus, theempirical magnetic field data ^(^) of the aeromagnetic survey comprises the first set of empiricalmagnetic field data ^(^^) and the second set of empirical magnetic field data ^(^^). This may be expressed as: where: |^(^^)| is a magnetic field intensity vector comprising the magnetic field intensity valuesassociate with each measurement location ^^ of the measurement locations ^^ associated with the firstflight path 123; and|^(^^)| is a magnetic field intensity vector comprising the magnetic field intensity valuesassociate with each measurement location ^^ of the measurement locations ^^ associated with the secondflight path 125.The ellipses in the above representation of the empirical magnetic field data ^(^) indicate that this datamay comprise other data obtained using the sensor systems 124, 144, time elements indicating times at which respective measurements were obtained etc.It will be appreciated that the sensor system data generated using the sensor system 124 of the first aerialsystem 122 and the sensor system data generated using the sensor system 144 of the second aerial system142 may be stored in a number of ways. For example, the sensor system data generated using the sensor system 124 of the first aerial system 122 may be stored in the memory 128 of the first aerial system 122. The sensor system data generated using the sensor system 144 of the second aerial system 142 may be stored in the memory 148 of the second aerial system 142. Alternatively, the sensor system data generated using the sensor system 144 of the second aerial system 142 may be transmitted to the first aerial system 122 and stored in the memory 128 of the first aerial system 122. In some embodiments, the sensor system data generated using the sensor system 124 of the first aerial system 122 may be transmitted to the second aerial system 142 and stored in the memory 148 of the second aerial system 142. The sensor system data generated by the surveying system 120 is provided to the computing system 102 for processing. In other words, the surveying system 120 provides the empirical magnetic field data ^(^)to the computing system 102 for processing. The sensor system data generated using the sensor system124 of the first aerial system 122 is provided to the computing system 102. That is, the empiricalmagnetic field data ^(^^) generated using the sensor system 124 of the first aerial system 122 isprovided to the computing system 102. This may be done via the communications network 154. Thesensor system data generated using the sensor system 144 of the second aerial system 142 is provided tothe computing system 102. That is, the empirical magnetic field data ^(^^) generated using the sensorsystem 144 of the second aerial system 142 is provided to the computing system 102. This may be done via the communications network 154. The at least one processor 104 of the computing system 102 may receive the sensor system data and store it in the memory 106 of the computing system 102. That is, the at least one processor 104 of the computing system 102 may receive the empirical magnetic field data ^(^)and store it in the memory 106 of the computing system 102. The at least one processor 104 of the computing system 102 may subsequently retrieve some or all of the sensor system data for processing. That is, the at least one processor 104 of the computing system 102 may subsequently retrieve some or allof the empirical magnetic field data ^(^) for processing.It will be appreciated that, throughout this description, a processor retrieving data from memory receives the retrieved data. That is, in retrieving data from memory, a processor receives that data. Therefore, where a processor or computer system is described herein to receive data, it will be appreciated that theprocessor or computer system may retrieve that data as part of the process of receiving it. It will also beappreciated that throughout this description, the term sensor system data may be used interchangeably with the term empirical magnetic field data ^(^). Method 200 Figure 5 shows a process flow diagram of a method 200, according to some embodiments of the present disclosure. Figure 6 shows a graphical representation of a portion of the survey area 103, along withcertain regions defined in accordance with the method 200. The method 200 is performed by the system100. In particular, the method 200 may be performed entirely, or in part, by one or more of the components of the system 100. For example, one or more steps of the method 200 may be performed bythe computing system 102. One or more steps of the method 200 may be performed by the at least oneprocessor 104 of the computing system 102. The method 200 is a computer-implemented method. Themethod 200 may be considered to be a method of estimating a characteristic of a magnetic field at a targetlocation. The method 200 may be considered to be a method of determining target magnetic field datathat is indicative of a magnetic field characteristic at a target location. The method 200 may be referred toas a method of determining target magnetic field data.Referring to Figure 3, the surveying system 120 obtains empirical magnetic field data ^(^) during anaeromagnetic survey of the survey area 103. The empirical magnetic field data ^(^) comprises empiricalmagnetic field data ^(^^) associated with the first flight path 123. The empirical magnetic fielddata ^(^) comprises empirical magnetic field data ^(^^) associated with the second flight path 125 (seeFigures 1, 2 and 6). In particular, the empirical magnetic field data ^(^) comprises empirical magneticfield data ^(^^) obtained along the first flight path 123 and empirical magnetic field data ^(^^)obtained along the second flight path 125. The empirical magnetic field data ^(^) indicates one or morecharacteristic of the magnetic field at the sensor systems 124, 144 when the aeromagnetic survey wasconducted. In particular, the empirical magnetic field data ^(^) indicates one or more characteristic ofthe local magnetic field (which, in this case, includes the Earth’s magnetic field), at the sensor system 124of the first aerial system 122 and the sensor system 144 of the second aerial system 142, at the timesmeasurements were taken during the aeromagnetic survey. As described herein, the empirical magneticfield data ^(^) comprises measurements of magnetic field intensity |^| at the measurement locations ^^.The method 200 enables the empirical magnetic field data ^(^) to be used to determine an estimate ofone or more characteristic of the measured magnetic field at target locations that do not fall on the firstflight path 123 or the second flight path 125. In the described embodiment, these target locations arelocations at a predefined ground clearance between the first flight path 123 and the second flight path125. Define Target Locations ^^At 202, one or more target location ^^ is defined. In the described embodiment of the method 200, aplurality of target locations ^^ are defined at 202. That is, at 202, a plurality of target locations ^^ aredefined. The at least one processor 104 defines the target locations ^^. It will be appreciated, that in someembodiments, the method 200 may be performed for a case where one target location ^^ is defined at202.The at least one processor 104 defines the target locations ^^ at a target ground clearance. That is, eachtarget location ^^is a location at the target ground clearance. The ground clearance of each targetlocation ^^ is therefore equal to the ground clearance of each other target location ^^. The target groundclearance is a ground clearance at which it is desired to estimate the characteristic of the local magnetic field.The target locations ^^ may be considered to form a target surface ^. The target surface ^ is a notionalsurface of constant ground clearance within the survey area 103. Each target location ^^ falls on thetarget surface ^. In particular, each target location ^^is a point on the target surface ^, with the targetsurface ^ being a surface of constant ground clearance across the survey area 103, that constant groundclearance being the target ground clearance. The target locations ^^may therefore be said to beassociated with the target surface ^. In some embodiments, the target ground clearance is constant.Each target location ^^represents a three-dimensional point in space. In particular, each targetlocation ^^ represents a three-dimensional point of the survey area 103. Each target location ^^ has an ^-coordinate, a ^-coordinate and a ^-coordinate. That is, ^^ = {^^ , ^^, ^^}. In this case, ^^ is an^-coordinate of the relevant point in space, ^^ is a ^-coordinate of the relevant point in space and ^^ is a^-coordinate of the relevant point in space. ^^may be a longitude of the point in space. ^^may be a latitude of the point in space. ^^may be a height of the point in space, relative to a reference location. This may be a height above ground level or a height above sea level. That is, the reference location maybe at ground level or sea level. In some embodiments, each of the ^-coordinate ^^, ^-coordinate ^^ and^-coordinate ^^are defined relative to an origin.The at least one processor 104 defines the target locations based on a value of at least one targetlocation parameter. A first target location parameter is a target location ground clearance parameter. Thetarget location ground clearance parameter is a scalar that sets the ground clearance of the targetlocations defined at 202. That is, the value of the target location ground clearance parameter sets theground clearance at which the target locations are defined. This ground clearance may be a distanceabove the ground. That is, the value of the target location ground clearance parameter indicates a height above the ground, at the relevant point of the survey area 103, for which the respective target location ^^is defined. This ground clearance may be referred to as a target ground clearance. The at least one processor 104 defines the target locations ^^, based on the value of the target location ground clearanceparameter, such that the target locations are defined at the target ground clearance. The target locations^^ may therefore be said to be associated with the target ground clearance.The at least one processor 104 defines the target locations ^^using the empirical magnetic fielddata ^(^). The empirical magnetic field data ^(^) indicates the ground level across the survey area 103.This ground level is referred to when defining each target location ^^as the target locations ^^aredefined at the target ground clearance, which is a height above ground level. The value of the ^-coordinate of a target location ^^ may therefore be the sum of the ^-coordinate of ground level at thelocation of the relevant ^-^ coordinate pair of the target location ^^, relative to a reference origin, and thetarget ground clearance. Alternatively, the ^-coordinate of a target location ^^ may be the height aboveground level of the relevant target location ^^. In the illustrated embodiment, the ground clearance of each target location ^^is the same. It will be appreciated that in some embodiments, the ground clearance of different target locations ^^may vary across the survey area 103.The at least one processor 104 defines the target locations ^^ in a target location arrangement. The targetlocations ^^ extend across the survey area 103 to form the target location arrangement. The targetlocation arrangement is a grid. The at least one processor 104 therefore defines a grid of target locations^^ at 202. The target location arrangement comprises a plurality of rows of target locations ^^. Eachtarget location ^^of a row of the target locations ^^may have a common ^-coordinate ^^. Each target location ^^of a row of the target locations ^^may have a different ^-coordinate ^^from the other target locations ^^of that row. The ^-coordinates ^^of a row of target locations ^^will vary along the row, with variations in elevation of the ground of the survey area 103 across that row of target locations ^^.The target location arrangement comprises a plurality of columns of target locations ^^. Each targetlocation of a column of the target locations may have a common ^-coordinate ^^. Each target location of a column of the target locations may have a different ^-coordinate from the other target locations of that column. The ^-coordinates of a column of target locations will vary along the column, with variations in elevation of the ground of the survey area 103 across that column of target locations ^^. The at least one processor 104 defines the target locations in the target location arrangement based ona value of one or more of the target location parameters. The target location parameters comprise anarrangement dimension parameter. A value of the arrangement dimension parameter sets a distancebetween adjacent target locations ^^. In other words, the value of the arrangement dimension parametersets a minimum distance between target locations ^^. The at least one processor 104 defines each targetlocation such that it is separated from adjacent target locations by a distance corresponding to thevalue of the arrangement dimension parameter. In other words, the at least one processor 104 defines eachtarget location such that it is separated from other target locations by a minimum distance. Thisminimum distance is defined by the value of the arrangement dimension parameter. In someembodiments, there may be a plurality of arrangement dimension parameters, each setting a distancebetween adjacent target locations in a particular direction.As described herein, the target location arrangement is a grid. The grid has a plurality of cells. The cellsof the grid are quadrilateral. In some embodiments, the side lengths of the cells of the grid are equal. Insome embodiments, the side lengths of vertical projections of the cells are equal. The target locations ^^are defined at nodes of this grid. The at least one processor 104 defines the grid of target locations atthe target ground clearance. The value of the arrangement dimension parameter indicates the side lengthof a cell of the grid. In other words, the arrangement dimension parameter is a grid cell size parameter. While the target location arrangement described herein is a grid with quadrilateral cells, it will be appreciated that in some embodiments, the cells of the grid may take another shape. For example, thecells may be triangular. Alternatively, the cells may be polygonal. It will be appreciated that in someembodiments, the target location arrangement may comprise target locations arranged in an arrangement other than a grid. For example, the target locations may be randomly arranged, orarranged in another regular arrangement. The target location arrangement may be defined by a user of thesystem 100. The value of the arrangement dimension parameter may be related to one or more characteristic of the aeromagnetic survey. In other words, the at least one processor 104 may define the target locations such that they are spaced apart from adjacent target locations by a distance that is related to acharacteristic of the aeromagnetic survey. The flight lines of the aeromagnetic survey (i.e. the straightlines of the flight path 121 shown in Figure 3) are separated by a flight line offset. The flight line offset isthe distance between the straight line sections of the flight path 121 (see Figure 3). In some embodiments,the value of the arrangement dimension parameter is related to the flight line offset. For example, thevalue of the arrangement dimension parameter may be a multiple of the flight line offset. This can be awhole-number multiple or a fractional multiple that is greater than 0. For example, the value of thearrangement dimension parameter may be equal to one quarter of the flight line offset. In this case, themultiple is 0.25. That is, the value of the arrangement dimension parameter may be less than the flight line offset.The at least one processor 104 defines the target locations such that the target locations are within atarget location boundary. The target location boundary may be considered a boundary of the targetsurface ^. The target location boundary may be defined by peripheral target locations ^^. The peripheraltarget locations are target locations that are not adjacent to other target locations on at least one side. That is, the peripheral target locations are outer target locations. At least one target location parameter may define the target location boundary. That is, at least one targetlocation parameter may define the spatial boundaries within which the target locations are defined.This may be considered a boundary of the target surface ^. The parameters that define the target location boundary may be called target location boundary condition parameters. The values of the target location boundary condition parameters may be said to be target location boundary conditions.The target location boundary is associated with the lateral boundary of the survey area 103. For example,the target location boundary may correspond to the lateral boundary of the survey area 103. The targetlocation boundary corresponding to the lateral boundary of the survey area 103 means that the targetlocation boundary and the lateral boundary of the survey area 103 are vertically aligned. That is, aperimeter of the target surface ^ formed by the target locations is vertically aligned with the perimeterof the survey area 103.It will be appreciated, however, that in some cases, the target location boundary may differ from theboundary of the survey area 103. For example, the target location boundary may be smaller than theboundary of the survey area 103. A value of at least one target location parameter may define a lateraloffset between the target location boundary and the lateral boundary of the survey area 103. That is, thevalue of the relevant target location parameter may define the offset between the boundary of the targetsurface ^ on which the target locations are defined and the lateral boundary of the survey area 103.Alternatively, the value of one or more of the target location parameters may define maximum lateralcoordinate values of the target locations ^^. ^ and ^ coordinates may be considered lateral coordinates.For example, a target location parameter may take a value of with the value indicating themaximum value of an ^-coordinate of a target location ^^. A target location parameter may take a valueof ^^, with the value ^^ indicating the minimum value of an ^-coordinate of a target location ^^. A targetlocation parameter may take a value of ^^, with the value ^^indicating the maximum value of a ^-coordinate of a target location ^^. A target location parameter may take a value of ^^, with the value ^^indicating the minimum value of a ^-coordinate of a target location ^^. Thus, the value of one or moretarget location parameter may set one or more lateral boundaries within which the target locations aredefined.Each target location is provided with a ^ index. The ^ index of a target location indicates theparticular target surface ^ that target location is on. In other words, the ^ index of a target location ^^indicates the ground clearance at which the target location is defined. That is, the ^ index of a targetlocation indicates the target ground clearance of that target location ^^. As described herein, the computing system 102 comprises a user interface 111 that enables a user of thecomputing system 102 to interact with the computing system 102 by providing inputs to the computingsystem 102. The user of the computing system 102 can provide inputs in the form of values of the targetlocation parameters. The at least one processor 104 may define the target locations based on the inputsprovided by the user. At least some of target locations are between the first flight path 123 and the second flight path 125. In other words, at least some of the target locations are between the measurement locations ^^of the firstsubset ^^ of the measurement locations ^^ and the measurement locations ^^ of the second subset ^^ ofthe measurement locations ^^. In the illustrated embodiment, all of the target locations are between the first flight path 123 and the second flight path 125. It will be appreciated however, that in some cases,the ground clearance of the first flight path 123 and / or the ground clearance of the second flight path 125may vary from the respective target flight path ground clearances (e.g. due to turbulence or rapid changes in ground surface topology). As the target locations are defined at a specific target ground clearance, in such cases, some of the target locations may be below the second flight path 125. Similarly, some ofthe target locations may be above the first flight path 123. The method 200 can still be used toestimate magnetic field characteristics in such cases. The ground clearance of each of the target locations ^ is greater than the target flight path ground clearance of the second flight path 125. The ground clearance of each of the target locations is lessthan the target flight path ground clearance of the first flight path 123. The ground clearance of the targetlocations is less than the mean ground clearance of the measurement locations ^^ of the first subset ^^of the measurement locations ^^. That is, the ground clearance of a majority of the target locations isless than the ground clearance of a majority of the measurement locations ^^ of the first subset ^^ of themeasurement locations ^^. The ground clearance of the target locations is greater than the meanground clearance of the measurement locations ^^of the second subset ^^of the measurement locations^^. That is, the ground clearance of a majority of the target locations is greater than the groundclearance of a majority of the measurement locations ^^ of the second subset ^^ of the measurementlocations ^^. It will be appreciated that the specific target locations of the majorities mentioned hereinmay vary between the relevant majorities. The height above ground level of each of the target locations is typically greater than the height above ground level of the second flight path 125. The height above ground level of each of the target locations ^^is typically less than the height above ground level of the first flight path 123. In other words, the height above ground level of the target locations ^^less than the mean height above ground level of themeasurement locations ^^ of the first subset ^^ of the measurement locations ^^. Similarly, the heightabove ground level of the target locations ^^is greater than the mean height above ground level of themeasurement locations ^^ of the second subset ^^ of the measurement locations ^^.The at least one processor 104 stores target locations ^^as target location data ^^. The target location data ^^is a matrix comprising each target location ^^. The at least one processor 104 may therefore be said to generate target location data ^^at 202. The target location data ^^comprises the targetlocations ^^.The target location data ^^ is provided with a ^ index. The ^ index of the target location data ^^indicates the particular target surface ^ that the target locations ^^ of the target location data ^^ are on.In other words, the ^ index of the target location data ^^ indicates the ground clearance at which thetarget locations ^^ of that target location data ^^ are defined.Define a Plurality of Target Regions ^^,^The method 200 involves fitting synthetic magnetic dipole source moments to the empirical magneticfield data obtained from the aeromagnetic survey and calculating characteristics of the synthetic magneticfield generated by the fitted synthetic magnetic dipole source moments at particular locations where nomeasurements were taken during the aeromagnetic survey. Where sufficient computing resources areavailable, the entire empirical magnetic field data ^(^) dataset and target location data ^^ dataset couldbe considered when the synthetic magnetic dipole source moments are generated and the magnetic fieldcharacteristic estimated. However, in practice, given the size of the datasets involved and available computing resources, this may not be possible.The method 200 provides a solution to this problem by defining smaller target regions ^^ that eachcomprise subsets of the target location data ^^, and iteratively selecting a target region ^^, defining asource region, below the target locations ^^,^ of the selected target region ^^, within or on whichsynthetic magnetic dipole source moments are positioned, defining an influence region around the targetlocations ^^,^ of the selected target region ^^, fitting synthetic magnetic fields of the synthetic magneticdipole source moments to the empirical magnetic field data obtained within the influence region, andestimating the characteristic of the magnetic field at the target locations ^^,^ of the selected targetregion ^^ using the fitted synthetic magnetic dipole source moments. The influence region isdimensioned to capture empirical magnetic field data that would have a material impact on the magneticfield at the target locations ^^,^ within the selected target region ^^. This process is iteratively repeatedfor each target region ^^, until estimates of the characteristic of the magnetic field have been computedacross the entire set of target location data ^^. That is, the process is iteratively repeated for each targetregion ^^, until estimates of the characteristic of the magnetic field have been computed for each of thetarget locations ^^,^. By performing the method 200 in this way, the computational resources required toestimate the characteristic of the magnetic field across the target surface ^ can be significantly reducedwith an insignificant increase in error.At 204, a plurality of target regions ^^ are defined. The at least one processor 104 defines the targetregions ^^. The target regions ^^ are surfaces defined at the target ground clearance. In particular, thetarget regions ^^ are surfaces defined over the survey area 103, at the target ground clearance. A targetregion ^^is a subsection of a surface defined over the survey area 104 at the target ground clearance. That is, each target region ^^is a portion of the survey area 103 at the target ground clearance. In other words, each target region is a portion of the target surface ^.Each target region ^^ is a surface that is bound by at least one target region boundary 107^ . That is, eachtarget region ^^ is a surface defined at the target ground clearance that is bound by at least one targetregion boundary 107^. The at least one processor 104 defines the target region boundaries 107^. In some embodiments of the present disclosure, the at least one processor 104 defines a grid across thesurvey area 103 at the target ground clearance. That is, the at least one processor 104 defines a grid acrossthe target surface ^. One or more cell of this grid may be, for form part of, a respective target region ^^.The target regions ^^ are surfaces defined at the target ground clearance that are bound by respectivetarget region boundaries 107^ . The target region boundaries 107^ of a target region ^^ define laterallimits of that target region ^^. That is, the target region boundaries 107^ define the ^ and ^ coordinatelimits of a target region ^^. The target regions ^^ may therefore be said to be defined by the targetregion boundaries 107^ .The at least one processor 104 defines the target regions ^^ based on at least one target region parameter.That is, the at least one processor 104 defines the boundaries 107^ of the target regions ^^ based on thevalue of at least one target region parameter. In some embodiments, the at least one processor 104 definesthe target regions ^^ based on the values of a plurality of target region parameters.The value(s) of the target region parameter(s) control the position of the boundaries 107^ of the targetregions ^^. The values of the target region parameters may be pre-set. The user input may comprise avalue of one or more of the target region parameters.The target regions ^^ are defined with respect to an origin. The origin is a reference point with respect towhich one or more of the target regions ^^ are defined. In some embodiments, a plurality of the targetregions ^^ are defined with respect to a common origin. In some embodiments, each target region ^^ isdefined with respect to a common origin. In some embodiments, one or more target region ^^ is definedwith respect to an origin unique to that target region ^^. The ^, ^ and ^ coordinates of the respectiveorigin may be considered target region parameters.The at least one target region parameter comprises a target region boundary condition parameter. Thevalue of the target region boundary condition parameter sets a dimension of the target regions ^^. Thevalue of the target region boundary condition parameter may be said to be a boundary condition of thetarget regions ^^. The target regions ^^ are quadrilateral. The length of each side of the targetregions ^^ is equal. The value of the target region boundary condition parameter sets a side length of thetarget regions ^^. In other words, the value of the target region boundary condition parameter defines adistance between vertices of a target region ^^. The at least one processor therefore defines the targetregion boundaries 107^ based on the value of the target region boundary condition parameter. The areawithin the target region boundaries 107^ of a target region ^^ may be said to satisfy the target regionboundary condition of that target region ^^.In the embodiment shown in Figure 6, the target region ^^is a quadrilateral with equal side lengths. Insome embodiments, the target regions ^^ take another shape. For example, the target regions ^^ may becircular. In such a case, the value of the target region boundary condition parameter may set a radius ofthe circular target regions ^^. In some embodiments, the target regions may be polygonal. For example,the target regions ^^ may be triangular, or have a different number of sides. In such a case, the value of the target region boundary condition parameter may set a side length of the relevant polygon. In someembodiments, different sides of a target region ^^ may be different lengths. In such a case, the relevanttarget regions ^^ may be defined by two or more target region boundary condition parameters. The valueof one target region boundary condition parameter may set the length of one or more side of the relevanttarget regions ^^ and the value of another target region boundary condition parameter may set the lengthof one or more other sides. This could be the case where a grid with cells of uneven side lengths is used.In some embodiments, different target regions ^^ may be different shapes. For example, some targetregions ^^ may be quadrilateral and some target regions ^^ may be circular.Each target region ^^ is offset with respect to other target regions ^^. The offset is a lateral offset. Thus,the at least one processor 104 defines a plurality of laterally offset target regions ^^ at 204. It will beappreciated that a longitudinal offset (i.e. an offset resulting in a change in longitude of a boundary 107^)is a lateral offset. Thus, each target region ^^ is longitudinally offset with respect to one or more othertarget region ^^. Similarly, a latitudinal offset (i.e. an offset resulting in a change in latitude of aboundary 107^) is a lateral offset. Thus, each target region ^^ is latitudinally offset with respect to oneor more other target region ^^. The target regions ^^ are defined in this way such that the targetregions ^^, together, span the survey area 103, at the target ground clearance.Each target region ^^ is provided with an ^ index. The value of the ^ index indicates the particulartarget region ^^ of the plurality of target regions ^^ that is under consideration. The at least oneprocessor 104 defines ^ target regions ^^. ^ is an integer. Therefore1 ≤ ^ ≤ ^. Each target region boundary 107^ is also provided with an ^ index. The value of the ^index indicates the particular target region ^^that the respective target region boundaries 107^define.Each target region ^^ comprises at least one target location of the plurality of target locations ^^.That is, each target region ^^comprises at least one target location on the target surface ^. A targetregion ^^ is considered to comprise a target location where that target location is within thetarget region ^^. A target region ^^ is considered to comprise a target location where that targetlocation is on the surface of the target region ^^. That is, a target region ^^ is considered tocomprise a target location where that target location is within the boundaries 107^ of the targetregion ^^. A target region ^^ is considered to comprise a target location where the boundaries107^ of the target region ^^ encircle the respective target location In other words, a targetregion ^^ is considered to comprise a target location where the coordinates of the targetlocation satisfy the boundary condition(s) of that target region ^^. Satisfying the boundaryconditions of a target region ^^ can mean being within the boundaries 107^ of the target region ^^.Therefore, a target region ^^ is considered to comprise a target location where the ^ and ^coordinates of the target location ^^,^ are within the ^ and ^ coordinate limits of the boundaries 107^ ofthe target region ^^.Each target region ^^ comprises a subset of the target locations defined at 202. A target location ^^,^of a particular target region ^^ is provided with a ^ index herein. The ^ index indicates the target surface^ that is associated with that target location ^^,^. That is, the ^ index indicates the ground clearance ofthe target location ^^,^. Each target location ^^,^ of a particular target region ^^ is provided with an ^index. The value of the ^ index indicates the particular target region ^^ of the plurality of target regions^^ that is being considered and that that target location ^^,^ is within. In some cases, each target locationof a particular target region ^^ may be provided with an ^ index. That is, the target locations of aparticular target region ^^ may be represented as ^^,^,^. The value of the ^ index indicates a particulartarget location ^^,^,^ out of the target locations ^^,^,^ of the respective target region ^^. That is, aparticular target region may have ^ target locations ^^,^,^, where 1 ≤ ^ ≤ ^. It will be appreciatedthat the value of ^ can be different between target regions ^^. For simplicity through the description, thetarget locations of a target region ^^ will be represented as ^^,^, unless reference to a specific targetlocation is useful, in which case they will be represented as ^^,^,^ (as well as with example values of ^, asrequired).One or more of the target regions ^^ may overlap with one or more other target region ^^ (see Figures 7-9). Therefore, two or more of the target regions ^^may both comprise a particular target location ^^,^. Inthis case, that target location ^^,^ may be referred to with a first ^ index value when a first of the two ormore of the target regions ^^ is being considered and a second ^ index value when a second of the twoor more of the target regions ^^is being considered.Part of a boundary 107^ of one or more target region ^^ may also be part of a boundary 107^ of anothertarget region ^^. In other words, one or more of the target regions ^^ may overlap at least part of aboundary 107^ of one or more other target region ^^.The boundaries 107^ of the target regions ^^ are referred to with an ^ index herein. The value of the ^index of a particular boundary 107^ is the same as the value of the ^ index of the target region ^^formed by that boundary 107^. The at least one processor 104 may define the target regions ^^based on computational resources available to the at least one processor 104. For example, the at least one processor 104 may determine the available computational resources (such as an available amount of memory, or the processing capability of the at least one processor 104 itself) and define the target regions ^^based on this determination. Forexample, a dimension of one or more of the target regions ^^ may be set based on the availablecomputational resources. In other words, the at least one processor 104 may set the value of a targetregion boundary parameter based on the available computational resources. Where less computationalresources are available, the at least one processor 104 may define smaller target regions ^^. Where morecomputational resources are available, the at least one processor 104 may define larger target regions ^^.In other words, a size of the target regions ^^ is related to the available computational capability of thecomputing system 102.The at least one processor 104 may define the target regions ^^by setting the values of the target region parameters.The value of one or more target region boundary parameter may define a minimum size of the targetregions ^^. In such a case, the at least one processor 104 will define target regions ^^ that are equal orgreater in size than the dimensions indicated by the relevant target region boundary parameter(s).Select a Target Region The method 200 involves the iterative determination of estimates of a characteristic of a magnetic field atthe target locations of the defined target regions ^^. The magnetic field characteristic is estimated atthe target locations of a first target region ^^, using the empirical magnetic field data ^(^).Following this, the magnetic field characteristic is estimated at the target locations ^^,^ of second,third etc. target regions ^^, ^^, using the empirical magnetic field data ^(^). A model of the magneticfield characteristic across the entire target surface ^ (i.e. at all of the target locations ^^) can then beconstructed from the estimated magnetic field characteristic determined for the respective targetregions ^^.At 206, a target region is selected from the plurality of target regions ^^. In particular, at 206, the atleast one processor 104 selects the target region ^^ from the plurality of target regions ^^ defined at 204.The particular target region ^^that is selected may be selected based on one or more selection criterion.For example, the at least one processor 104 may select a target region ^^ with the lowest value of the ^index, that has not previously been selected during performance of the method 200. Selecting the target region ^^may comprise selecting target locations within the target region ^^.That is, selecting the target region ^^ may comprise selecting the target locations of the targetregion ^^. Selecting the target region ^^ may comprise constructing a target region location matrix ^^,^.The target region location matrix ^^,^ comprises each target location of the particular targetregion ^^. The at least one processor 104 constructs the target region location matrix ^^,^. The at leastone processor 104 constructs the target region location matrix ^^,^ from the target locations ^^,^. The atleast one processor 104 stores the target region location matrix ^^,^ in memory 106. The target regionlocation matrix ^^,^ may be referred to as target region location data ^^,^. Thus, at 204, the at least oneprocessor 104 determines target region location data ^^,^ that is associated with the selected targetregion ^^.Define a Source Region 113^for the Selected Target Region ^^At 208, a source region 113^ is defined (see Figures 3 and 6). The at least one processor 104 defines thesource region 113^ . The source region 113^ is a three-dimensional region that is bound by source regionboundaries 115^ . The source region boundaries 115^ define the volume of the source region 113^ . Thesource region 113^ is associated with the target region ^^ selected at 206.The source region 113^ and source region boundaries 115^ are referred to herein with an ^ index. Thevalue of the ^ index of a particular source region 113^ or source region boundary 115^ is the same asthe value of the ^ index of the target region ^^ that the source region 113^ or source region boundary115^ is associated with. That is, the value of the ^ index of a particular source region 113^ or sourceregion boundary 115^ is the same as the value of the ^ index of the target region ^^ selected at 206, thatthe source region 113^ or source region boundary 115^ is defined for.The source region 113^ is a notional region within the survey area 103. The boundaries 115^ of thesource region 113^ define a shape of the source region 113^ . The boundaries 115^ of the sourceregion 113^ define a location of the source region 113^.The source region 113^ is associated with the target region ^^ selected at 206. In particular, a number ofthe characteristics of the source region 113^ are defined based on the particular target region ^^ selectedat 206.The location of the source region 113^ is related to the location of the target region ^^. The at least oneprocessor 104 defines the source region 113^ below the selected target region ^^. That is, the at leastone processor 104 defines the source region 113^ below the target locations ^^,^ of the selected targetregion ^^. The source region 113^ is vertically offset from the target locations ^^,^ of the selected targetregion ^^.The positions of the boundaries 115^ of the source region 113^ may be set by values of one or moresource region parameters. The values of the source region parameters may be related to the values of oneor more parameters of the target region ^^.The source region 113^ has an upper source region boundary 115^ . The at least one processor 104defines the upper source region boundary 115^. The upper source region boundary 115^defines anupper surface of the source region 113^ . The upper surface of the source region 113^ is a first sourceregion surface 117^. Therefore, at 208, the at least one processor 104 defines the first source region surface 117^. The upper source region boundary 115^is defined at ground level. That is, the at least one processor 104 defines the upper source region boundary 115^to be at a ground clearance of 0. A value of a source region parameter may set the ground clearance of the upper source region boundary115^ . The source region parameters comprise an upper source region boundary parameter. The value ofthe upper source region boundary parameter sets the location of the upper source region boundary 115^. That is, the value of the upper source region boundary parameter sets the ground clearance of the uppersource region boundary 115^ . The value of the upper source region boundary parameter of the describedembodiment defines the upper source region boundary 115^ to be at ground level. In this case, the valueof the upper source region boundary parameter is 0. It will be appreciated that in other embodiments, theupper source region boundary 115^ may be defined at a different ground clearance, or to extend across avariety of ground clearances.The source region 113^ has a lower source region boundary 115^ . The at least one processor 104defines the lower source region boundary 115^. The lower source region boundary 115^defines a lowersurface of the source region 113^. The lower surface of the source region 113^ is a second source regionsurface 119^. Therefore, at 208, the at least one processor 104 defines the second source region surface 119^. A value of a source region parameter may set the ground clearance of the lower source region boundary 115^. The source region parameters comprise a lower source region boundary parameter. The value of the lower source region boundary parameter sets the location of the lower source region boundary 115^. That is, the value of the lower source region boundary parameter defines the ground clearance of the lower source region boundary 115^. The lower source region boundary 115^is defined below ground level. That is, the at least one processor 104 defines the lower source region boundary 115^to be at a depth below ground level. A depth hereinis a distance below ground level measured along a vertical axis. A value of the lower source regionboundary parameter sets the depth of the lower source region boundary 115^ . The depth of the lowersource region boundary 115^ may be set to be equal to a lateral dimension of the source region 113^. Itwill be appreciated that in other embodiments, the lower source region boundary 115^may be defined at a different depth, or to extend across a variety of depths.The first source region surface 117^ is parallel to the second source region surface 119^ , across thesurfaces 117^ , 119^ . That is, points or regions of the first source region surface 117^ are parallel tovertically aligned points or regions on the second source region surface 119^ . A vertical distancebetween the first source region surface 117^ and the second source region surface 119^ is constant,across the surfaces 117^ , 119^ .The source region 113^ has one or more lateral source region boundary 115^ . The illustrated sourceregion 113^ has a plurality of lateral source region boundaries 115^ . In particular, the sourceregion 113^ illustrated in Figure 6 is a rectangular prism and has four planar lateral source regionboundaries 115^ . It will be appreciated that the source region 113^ may take another shape (such as acylinder or a polygonal prism) in other embodiments. The number of lateral source region boundaries115^ can therefore be different. The source region 113^ will be an irregular shape in some embodimentsof the method 200, as the ground level topology may not be flat.The lateral source region boundaries 115^ define respective lateral surfaces of the source region 113^ .The lateral source region boundaries 115^extend from the upper source region boundary 115^to the lower source region boundary 115^. Thus, a three-dimensional volume is formed within the source region boundaries 115^. The lateral source region boundaries 115^define lateral limits of the upper source region boundary115^ . The lateral source region boundaries 115^ define the ^ and ^ coordinate limits of the upper sourceregion boundary 115^. The lateral source region boundaries 115^define a perimeter of the upper source region boundary 115^. The lateral source region boundaries 115^define lateral limits of the lower source region boundary115^ . The lateral source region boundaries 115^ define the ^ and ^ coordinate limits of the lower sourceregion boundary 115^. The lateral source region boundaries 115^define a perimeter of the lower source region boundary 115^. The positions of the lateral source region boundaries 115^may be defined by values of one or moresource region parameter. The one or more source region parameters may comprise coordinate valuesindicating vertices of the source region 113^. Alternatively, the one or more source region parametersmay be associated with respective dimensions of the source region 113^ . For example, a value of asource region parameter may set a side length of the source region 113^ . In some embodiments, valuesof the source region parameters define the ^ and ^ coordinate limits of the source region 113^ . Therelevant source region parameters may therefore be referred to as source region boundary condition parameters. The values of the source region boundary condition parameters may be referred to asboundary conditions of the source region 113^ . Where a cylindrical source region is defined, a value of asource region parameter may set the radius of the cylindrical source region and a value of another sourceregion parameter may set the height of the cylindrical source region.The position of the source region 113^ is associated with the position of the target region ^^ for whichthe source region 113^ is defined. The source region 113^ is vertically aligned with the selected targetregion ^^. A vertical axis extending through a centre of the source region 113^ is colinear with a verticalaxis extending through a centre of the target region ^^. That is, the source region 113^ is defined suchthat centre of the source region 113^ is vertically aligned with the centre of the selected target region ^^.The source region 113^ is therefore also vertically aligned with the target locations of the selectedtarget region ^^.Lateral dimensions of the source region 113^ are greater than corresponding lateral dimensions of theselected target region ^^. In the described case, a distance between opposing lateral source regionboundaries 115^is about 2000m greater than a distance between opposing boundaries 107^of theselected target region ^^. It will be appreciated that this difference in lateral dimensions can be differentin other embodiments of the method 200. More generally, it may be said that a distance between opposinglateral boundaries 107^ of the selected target region ^^ is less than a corresponding distance betweenopposing lateral source region boundaries 115^ by a threshold distance. It will be appreciated that thedistance between opposing boundaries 107^ of the selected target region ^^ is measured along an axisthat is vertically aligned with the corresponding axis along which the distance between the opposinglateral source region boundaries 115^is measured. The distance threshold may be 1000m or greater. The distance threshold may be 2000m or greater. The distance threshold may be proportional to the distancebetween the opposing lateral boundaries 107^ of the selected target region ^^. The distance thresholdmay be proportional to the distance between the opposing lateral source region boundaries 115^ .The at least one processor 104 may define the source region 113^based on computational resourcesavailable to the at least one processor 104. The at least one processor 104 may determine the availablecomputational resources (such as an available amount of memory) and define the source region 113^based on this determination. For example, a size of the source region 113^may be determined based on the available computational resources. In other words, the at least one processor 104 may set the value of a source region parameter based on the available computational resources. Where less computationalresources are available, the at least one processor 104 may define a smaller source region 113^ . Wheremore computational resources are available, the at least one processor 104 may define a larger sourceregion 113^ . In other words, a size of the source region 113^ may be associated with the computationalcapability of the computing system 102. In this way, the at least one processor 104 may dynamicallyadjust the size of the defined source region 113^between iterations of the method 200.The at least one processor 104 may define the source region 113^ by setting the values of the sourceregion parameters.The value of one or more source region parameter may define a minimum size of the source region 113^ .In such a case, the at least one processor 104 will define a source region 113^ that is equal or greater insize than the dimensions indicated by the relevant source region parameter(s).A plurality of synthetic magnetic dipole source moments are positioned on the first source regionsurface 117^ and the second source region surface 119^ , as described in more detail herein.Define an Influence Region 105 for the Selected Target Region ^^At 210, an influence region 105^ is defined (see Figures 3 and 6). The at least one processor 104 definesthe influence region 105^ . The influence region 105^ is a three-dimensional region that is bound byinfluence region boundaries 109^ . The influence region boundaries 109^ define the volume of theinfluence region 105^ . The influence region 105^ is associated with the target region ^^ selected at 206.The influence region 105^ and influence region boundaries 109^ are referred to herein with an ^ index.The value of the ^ index of a particular influence region 105^ or influence region boundary 109^ is thesame as the value of the ^ index of the target region ^^ that the influence region 105^ or influenceregion boundary 109^ is associated with. That is, the value of the ^ index of a particular influenceregion 105^ or influence region boundary 109^ is the same as the value of the ^ index of the targetregion ^^ selected at 206, that the influence region 105^ or influence region boundary 109^ is definedfor.The influence region 105^ is a notional region of the survey area 103. The boundaries 109^ of theinfluence region 105^ define a shape of the influence region 105^ . The boundaries 109^ of theinfluence region 105^define a location of the influence region 105^. The influence region 105^is associated with the target region ^^selected at 206. The influence region 105^is a region around the target region ^^. In particular, a number of the characteristics of theinfluence region 105^ are defined based on the particular target region ^^ selected at 206.The location of the influence region 105^ is related to the location of the target region ^^. In particular,the at least one processor 104 defines the influence region 105^ around the target region ^^. That is, thetarget region ^^ is contained within the influence region 105^ . The influence region 105^ is a three-dimensional volume that contains the target region ^^. The at least one processor 104 defines theinfluence region 105^ around the target locations of the target region ^^ selected at 206. That is,the target locations ^^,^ of the target region ^^ selected at 206 are contained within the influence region105^ . The influence region 105^ may therefore be said to be associated with that target region ^^.Similarly, the influence region 105^may be said to be associated with the target locations of thetarget region ^^ selected at 206.The target region ^^ selected at 206 is within the boundaries 109^of the influence region 105^. That is,the target locations of the target region ^^ selected at 206 are locations that are within the influenceregion 105^.The influence region 105^ is above the source region 113^. That is, the influence region 105^ isvertically offset from the source region 113^ .The positions of the boundaries 109^of the influence region 105^may be set by values of one or more influence region parameters. The values of the influence region parameters may be related to the values of one or more parameters of the target region ^^.The influence region 105^ may have an upper influence region boundary 109^. The at least oneprocessor 104 defines the upper influence region boundary 109^. The upper influence region boundary109^ defines an upper surface of the influence region 109^ . The upper surface of the influenceregion 105^ is a first influence region surface. Therefore, at 210, the at least one processor 104 maydefine the first influence region surface. The upper influence region boundary 109^ above the targetregion ^^. That is, the upper influence region boundary 109^ above the target locations ^^,^ of thetarget region ^^ selected at 206.A value of an influence region parameter may set the ground clearance of the upper influence region boundary 109^. The influence region parameters comprise an upper influence region boundary parameter. The value of the upper influence region boundary parameter sets the location of the upperinfluence region boundary 109^ . That is, the value of the upper influence region boundary parameter setsthe ground clearance of the upper influence region boundary 109^ . In this case, the value of the upperinfluence region boundary parameter may be set to infinity, or a high number. It will be appreciated thatin other embodiments, the upper influence region boundary 109^ may be defined at a different groundclearance, or to extend across a variety of ground clearances.The influence region 105^ may have a lower influence region boundary 109^ . The at least one processor104 defines the lower influence region boundary 109^. The lower influence region boundary 109^defines a lower surface of the influence region 105^ . The lower surface of the influence region 105^ is asecond influence region surface. Therefore, at 210, the at least one processor 104 may define the secondinfluence region surface. The lower influence region boundary 109^ below the target region ^^. That is,the lower influence region boundary 109^ below the target locations ^^,^ of the target region ^^selected at 206.A value of an influence region parameter may set the ground clearance of the lower influence regionboundary 109^ . The influence region parameters comprise a lower influence region boundary parameter.The value of the lower influence region boundary parameter sets the location of the lower influenceregion boundary 109^ . That is, the value of the lower influence region boundary parameter sets theground clearance of the lower influence region boundary 109^ . In this case, the value of the lowerinfluence region boundary parameter may be set to 0, or a ground clearance that is lower than that of the lowest ground clearance of the second flight path 125. It will be appreciated that in other embodiments,the lower influence region boundary 109^ may be defined at a different ground clearance, or to extendacross a variety of ground clearances. The upper influence region boundary 109^is spaced apart from the lower influence region boundary109^ by a vertical offset. The vertical offset is greater than the maximum ground clearance differencebetween the first flight path 123 and the second flight path 125.The influence region 105^ has one or more lateral influence region boundary 109^ . The illustratedinfluence region 105^ has a plurality of lateral influence region boundaries 109^ . The influenceregion 105^ illustrated in Figure 6 is a rectangular prism and has four planar lateral influence regionboundaries 109^ . It will be appreciated that the influence region 105^ may take another shape (such as asphere, or a polygonal prism) in other embodiments. In such a case, the number of lateral influence regionboundaries 109^would be different.The lateral influence region boundaries 109^ define respective lateral surfaces of the influenceregion 105^ . The lateral influence region boundaries 109^ extend from the upper influence regionboundary 109^ to the lower influence region boundary 109^ . Thus, a three-dimensional volume isformed within the influence region boundaries 109^ .The positions of the lateral influence region boundaries 109^ may be defined by values of one or moreinfluence region parameter. The one or more influence region parameters may comprise coordinate valuesindicating vertices of the influence region 105^ . Alternatively, the one or more influence regionparameters may be associated with respective dimensions of the influence region 105^. For example, avalue of an influence region parameter may set a side length of the influence region 105^ . In someembodiments, the values of the influence region parameters define the ^ and ^ coordinate limits of theinfluence region 105^. The relevant influence region parameters may therefore be referred to as influence region boundary condition parameters. The values of the influence region boundary condition parameters may be said to be boundary conditions of the influence region 105^. Where a spherical influence region is defined, a value of an influence region parameter may set the radius of the sphericalinfluence region. Where a cylindrical influence region is defined, a value of an influence region parametermay set the radius of the cylindrical influence region and a value of another influence region parameter may set the height of the cylindrical influence region.The position of the influence region 105^ is associated with the position of the target region ^^ forwhich the influence region 105^ is defined. In particular, the influence region 105^ is defined to containthe portion of the empirical magnetic field data ^(^) that will have a material influence on the magneticfield characteristics at the target locations of the selected target region ^^. The influenceregion 105^ is smaller than the survey area 103. However, a lateral dimension of the influenceregion 105^ is equal to, or greater than, a corresponding lateral dimension of the source region 113^ . Inthe embodiment illustrated in Figure 6, a lateral dimension of the influence region 105^ is equal to acorresponding lateral dimension of the source region 113^ . It will be understood that in each case, therespective lateral dimension is a dimension of the particular region that is measured between twoopposing lateral boundaries.Where the influence region 105^ is described to contain a measurement location herein, it will beunderstood that that measurement location is within the boundaries 109^ of the influence region 105^ ,or on one or more of the boundaries 109^ of the influence region 105^ . That is, measurement locationsthat are on a boundary 109^ of an influence region 105^ are considered to be contained by that influenceregion 105^ . Similarly, measurement locations that are on a boundary 109^ of an influence region 105^are considered to be within that influence region 105^ in the present disclosure.The influence region 105^ is vertically aligned with the selected target region ^^. A vertical axisextending through a centre of the influence region 105^ is colinear with a vertical axis extending througha centre of the target region ^^. This is the case illustrated in Figure 6. In the illustrated embodiment, theinfluence region 105^ is centred on the target region ^^. That is, a centre of the influence region 105^lies on the target region ^^. The centre of the influence region 105^ may lie on a target location ofthe target region ^^. The centre of the influence region 105^ may be the centroid of the volume definedby the boundaries 109^ of the influence region 105^ .While the illustrated influence region 105^ is a rectangular prism, in some cases, the influenceregion 105^ may be defined as a sphere with an influence region radius. The spherical influence regionmay be centred on a target location of the target region ^^. Alternatively, the spherical influenceregion may be centred on a centre of the target region ^^. Alternatively, the influence region 105^ maybe defined as a cylinder with an influence region radius and an influence region height. The cylindricalinfluence region 105^ may be centred on a target location ^^,^ of the target region ^^. Alternatively, thecylindrical influence region may be centred on a centre of the target region ^^.The at least one processor 104 may define the influence region 105^ based on computational resourcesavailable to the at least one processor 104. The at least one processor 104 may determine the availablecomputational resources (such as an available amount of memory) and define the influence region 105^based on this determination. For example, a size of the influence region 105^ may be determined basedon the available computational resources. In other words, the at least one processor 104 may set the value of an influence region parameter based on the available computational resources. Where lesscomputational resources are available, the at least one processor 104 may define a smaller influenceregion 105^ . Where more computational resources are available, the at least one processor 104 maydefine a larger influence region 105^ . In other words, a size of the influence region 105^ may beassociated with the computational capability of the computing system 102. In this way, the at least oneprocessor 104 may dynamically adjust the size of the defined influence region 105^ between iterations ofthe method 200.The at least one processor 104 may define the influence region 105^ by setting the values of theinfluence region parameters.The value of one or more influence region parameter may define a minimum size of the influenceregion 105^ . In such a case, the at least one processor 104 will define the influence region 105^ suchthat it is equal or greater in size than the dimensions indicated by the relevant influenceregion parameter(s).Select Empirical Magnetic Field Data ^ Obtained Within the Influence Region 105^ At 212, the at least one processor 104 selects empirical magnetic field data ^(^^). The empiricalmagnetic field data ^(^^) selected at 212 is selected from the empirical magnetic field data ^(^)obtained in the aeromagnetic survey. In other words, at 212, the at least one processor 104 selectsempirical magnetic field data ^(^^) from the empirical magnetic field data ^(^) obtained in theaeromagnetic survey.The at least one processor 104 selects the empirical magnetic field data ^(^^) based on one or moreselection criterion. In particular, the at least one processor 104 selects the empirical magnetic field data^(^^) that is within the influence region 105^ defined at 210. In other words, the at least one processor104 selects the empirical magnetic field data ^(^^) that is contained by the influence region 105^defined at 210. That is, the at least one processor 104 selects the empirical magnetic field data ^(^^) thatsatisfies the boundary conditions of the influence region 105^. As mentioned herein, this includesempirical magnetic field data ^(^^) that is on the influence region boundaries 109^ . The at least oneprocessor 104 may therefore select the empirical magnetic field data ^(^^) that is within the boundaries109^of the influence region 105^defined at 210. The selected empirical magnetic field data ^(^^)may be said to be empirical magnetic field data ^(^^) that is associated with the influence region 105^ .That is, empirical magnetic field data ^(^^) may be said to be associated with the influence region 105^where the measurement locations of that empirical magnetic field data ^(^^) are within the respectiveinfluence region 105^ . The selected empirical magnetic field data ^(^^) may be said to be empiricalmagnetic field data ^(^^) of the influence region 105^ .The measurement locations of the empirical magnetic field data ^(^^) selected at 212 may bedenoted ^^,^. Each measurement location ^^,^ is provided with an ^ index. The value of the ^ indexindicates the particular influence region 105^ that contains that measurement location ^^,^. That is, thevalue of the ^ index is the same as that of the influence region 105^ defined at 210. The value of the ^index also indicates the particular target region ^^ for which that measurement location ^^,^ is selected.Each measurement location ^^,^ is provided with an ^ index. At 212, the at least one processor 104selects ^ measurement locations ^^,^. That is, there is ^ measurement locations within the relevantinfluence region 105^ . ^ is an integer. The value of the ^ index indicates the particular measurementlocation being referred to, within the set of ^ measurement locations selected at 212. Thus 1 ≤^ ≤ ^. It will be appreciated that the value of ^ may vary depending on the particular target region ^^that is selected at 206.The measurement locations ^^,^ associated with the selected empirical magnetic field data ^(^^) areeach within a common influence region 105^. In other words, a common influence region 105^contains the measurement locations ^^,^of the selected empirical magnetic field data ^(^^).The at least one processor 104 stores the selected empirical magnetic field data ^(^^) in memory 106. Amatrix comprising each measurement location ^^,^ of the selected empirical magnetic field data ^(^^) isdenoted ^^herein. The at least one processor 104 constructs this measurement location matrix ^^. Thematrix ^^ is denoted with an ^ index. The value of the ^ index indicates the particular influence region105^for which the measurement locations ^^,^of the matrix ^^were selected. That is, the value of the^ index indicates the particular influence region 105^ defined at 210.The empirical magnetic field data ^(^^) selected at 212 comprises a first set of empirical magnetic fielddata ^(^^,^). The first set of empirical magnetic field data ^(^^,^) is associated with the first flight path123. The first set of empirical magnetic field data ^(^^,^) includes magnetic field measurements ^(^^,^)taken at measurement locations ^^,^ along the first flight path 123. In particular, the first set of empiricalmagnetic field data ^(^^,^) includes magnetic field measurements ^(^^,^) taken at measurementlocations ^^,^ along the first flight path 123, within the relevant influence region 105^ . This is the reasonfor the measurement locations ^^,^ being denoted herein with both an ^ index, indicating the relevantinfluence region 105^ , and an ^ index, indicating the association with the first flight path 123. Amatrix ^^,^ includes the measurement locations of the measurements ^^^^,^^ taken along the firstflight path 123 within the relevant influence region 105^ . The first set of magnetic field data ^(^^,^)was obtained using the sensor system 124 of the first aerial system 122.The empirical magnetic field data ^(^^) comprises a second set of empirical magnetic fielddata ^(^^,^). The second set of empirical magnetic field data ^(^^,^) is associated with the secondflight path 125. The second set of empirical magnetic field data ^(^^,^) includes magnetic fieldmeasurements ^(^^,^) taken at measurement locations ^^,^ along the second flight path 125. Inparticular, the second set of empirical magnetic field data ^(^^,^) includes magnetic field measurementstaken at measurement locations ^^,^ along the second flight path 125, within the relevantinfluence region 105^ . This is the reason for the measurement locations ^^,^ having both a ^ index(indicating the relevant influence region 105^) and a ^ index, indicating the association with the secondflight path 125. The matrix ^^,^ includes the measurement locations of the measurements ^^^^,^^taken along the second flight path 125 within the relevant influence region 105^. The second set ofmagnetic field data ^(^^,^) was obtained using the sensor system 144 of the second aerial system 142.The empirical magnetic field data ^(^^) selected at 212 includes the first set of empirical magnetic fielddata ^(^^,^) and the second set of empirical magnetic field data ^(^^,^). That is:^(^^,^) ∈ ^(^^); and^(^^,^) ∈ ^(^^)The at least one processor 104 stores the selected empirical magnetic field data ^(^^) in memory 106. Itwill be understood that in selecting the empirical magnetic field data ^ , the at least one processor104 retrieves the empirical magnetic field data ^(^^) to construct the appropriate matrix ^(^^) to storein memory 106. In retrieving the selected empirical magnetic field data ^ , the at least one processor104 may be said to receive the empirical magnetic field data ^(^^).Fitting Synthetic Magnetic Dipole Source Moments ^^^^,^^ to the Selected Empirical Magnetic FieldData ^(^^)At 214, the computing system 102 creates a mathematical model that approximates the observed magneticfield data using a set of synthetic magnetic dipole sources. The computing system 102 attempts to find aconfiguration of synthetic magnetic dipole sources that, when considered together, produce a magneticfield that closely matches the empirical magnetic field data ^(^^) selected at 212. That is, the computing system 102 attempts to find a configuration of synthetic magnetic dipole sources that, when consideredtogether, produce a magnetic field that closely matches the empirical magnetic field data ^(^^) of theinfluence region 105^ under consideration. Each synthetic magnetic dipole source is characterised by itslocation and magnetic dipole moment. Thus, each synthetic magnetic dipole source may be referred to asa synthetic magnetic dipole source moment. These synthetic magnetic dipole source moments aremathematical constructs used to model the magnetic field characteristics of the surveyed region 103. Asimulated magnetic field of these synthetic magnetic dipole source moments is calculated using principlesof magnetostatics. The synthetic magnetic dipole source moments are optimised through computationalfitting processes to best match the empirical magnetic field data obtained during the aeromagnetic survey. This optimisation may involve adjusting one or more of the magnitude, orientation, and spatial distribution of the synthetic magnetic dipole source moments until the simulated magnetic fields they generate closely correspond to the measured magnetic field values at the measurement locations. Simulated magnetic fields of the synthetic magnetic dipole source moments can be referred to as syntheticmagnetic fields. These synthetic magnetic fields are modelled based on the characteristics of the syntheticmagnetic dipole source moments. The fitting process involves an optimisation procedure. The computing system 102 aims to adjustcharacteristics of the synthetic magnetic dipole source moments to minimise the difference between asimulated magnetic field produced by the synthetic magnetic dipole source moments and the observedempirical magnetic field data ^(^^). Each synthetic magnetic dipole source moment is a magnetic sourcemoment that is positioned at a respective dipole source location. The magnetic source moment is a vectorthat encodes the strength and direction of the respective synthetic magnetic dipole source moment.Characteristics of a synthetic magnetic dipole source moment that are optimised can include one or moreof the synthetic magnetic dipole source moment’s magnetic source moment and orientation. Bydetermining the fitted synthetic magnetic dipole source moments, the computing system 102 creates asimplified model of the subsurface magnetisation near the target region ^^. This model can reproduce theobserved magnetic field with high fidelity. The fitted synthetic magnetic dipole source moments encapsulate information about the strength, orientation, and distribution of magnetic sources in the survey area 103, serving as a compact and useful representation of the magnetic field for further analysis. The field of a magnetic dipole is proportional to its dipole moment, so fields of a plurality of dipoles atrespective source locations can be written as a matrix time vector product. Given that the measurementlocations ^^,^ along the first flight path 123 and the measurement locations ^^,^ along the second flightpath 125 are tracked and recorded, the magnetic field measurements ^^^^,^^, at thosemeasurement locations ^^,^, ^^,^ can be simulated using the magnetic dipole formula.Permeable materials such as certain minerals placed in an external field such as the Earth’s magnetic field, acquire an induced magnetism whose strength is determined by the magnetic susceptibility of the material. Other substances exhibit a permanent magnetism unrelated to environment. In either case, itmay be supposed that a material that fills a volume ^ in the ground has a continuously distributedmagnetic dipole moment per unit volume which is given by ^(^^).The magnetic field at a point ^ outside the source volume is: where: ^(^) is the magnetic field at point ^;^^is a magnetic constant; ^is a differentiation operator;^ is the source volume;^(^^) is the continuously distributed magnetic dipole moment per unit volume or magnetisation;^^ is the integration variable for the integral over the source volume; and^ is the point outside the source volume.Evaluating the integral in Equation 1 over a small source volume around location ^^ results in thesynthetic magnetic field ^(^^), at location ^^, for a single source dipole moment ^(^^) at location ^^: where: ^(^^) is the synthetic magnetic field at location ^^;^^ is a measurement location ^^ of the empirical magnetic field data ^(^) or a target location ^^;^(^^) is a magnetic dipole source moment for the small volume around ^^; and^^is a location of the magnetic dipole source moment ^(^^).The magnetic dipole source moment ^(^^) at a particular dipole source location ^^ has three componentslocated at the dipole source location ^^: Equation 2 can be abbreviated as: where: The method 200 involves approximating the volume distribution with a finite number of syntheticmagnetic dipole source moments ^^^^,^^. Each synthetic magnetic dipole source moment islocated at a respective source location The source locations are within the source region 113^ .Where a source region 113^ is described to contain a source location herein, it will be understoodthat the source location is within the boundaries 115^ of the source region 113^ , or on one or moreof the boundaries 115^ of the source region 113^ . That is, source locations that are on a boundary115^ of a source region 113^ are considered to be contained by that source region 113^ . Similarly,source locations that are on a boundary 115^ of a source region 113^ are considered to be withinthat source region 113^ in the present disclosure.The synthetic magnetic dipole source moments are located within the source region 113^. Thesynthetic magnetic dipole source moments are distributed in two layers, one at ground surface^^ and one at a depth below ground surface ^^ − ^. In particular, the synthetic magnetic dipole sourcemoment are distributed at source locations ^^,^ that are on the upper source region boundary115^ and the lower source region boundary 115^ of the source region 113^ . That is, the syntheticmagnetic dipole source moments are distributed at dipole source locations ^^,^ that are on thefirst source region surface 117^ and the second source region surface 119^ . As described herein, alocation that is on or inside the boundaries 115^ of a source region 113^ is considered to be containedby that source region 113^ . The dipole source locations are therefore contained by the sourceregion 113^ defined at 208.A dipole source location matrix ^^,^ includes the magnetic dipole source locations of the two sourcelayers, over the horizontal region {^, ^} of the source region 113^:In this case, ^^,^ is a matrix comprising each dipole source location ^^,^. ^^ is a matrix comprising alldipole source locations ^^ of the source region 113^ that are at ground level. ^^ is a matrix comprisingall dipole source locations ^^ of the source region 113^ that are at the depth below ground surface. So^^,^ is a matrix comprising all dipole source locations of the source region 113^ that are at ground level^^and the depth below ground surface ^^.The expression for the single source dipole moment allows calculating the magnetic field of a sourcedistribution by summing the contributions of all dipole source moments. Where magnetic field gradientsare being considered (refer to the Alternative Embodiments section), the expressions for the partial derivatives allow calculating the magnetic gradients by summing the contribution of all dipoles. Approximations include: -An extent of the source distribution to consider for a given field point; and- How fine the source region needs to be discretised for a reliable result.A horizontal discretisation is performed on a detailed grid whereas the vertical discretisation comprises the two layers of equivalent sources. These two layers are used to sample the source volume that isresponsible for the empirical magnetic field data ^(^^).This principle is used at 214 of the method 200 to enable synthetic magnetic dipole sourcemoments to be fitted to the empirical magnetic field data ^(^^) of the influence region 103^ . Itwill be appreciated that the same principle may be adapted to the entire set of empirical magnetic fielddata ^(^) given the availability of sufficient computing resources.At 214, the at least one processor 104 fits synthetic magnetic dipole source moments to theempirical magnetic field data ^(^^). In particular, the at least one processor 104 fits synthetic magneticdipole source moments to the empirical magnetic field data ^(^^) selected at 212. This may besaid to comprise determining fitted synthetic magnetic dipole source moments ^^^^,^^. Therefore, at214, the at least one processor determines fitted synthetic magnetic dipole source moments ^^^^,^^.As described herein, each synthetic magnetic dipole source moment may be associated with arespective synthetic magnetic field. This synthetic magnetic field is the magnetic field that would be generated by a magnetic dipole source with the same characteristics as the respective synthetic magneticdipole source moment ^^^^,^^. Thus, each synthetic magnetic dipole source moment may besaid to generate a respective synthetic magnetic field. Fitting the synthetic magnetic dipole sourcemoments ^^^^,^^ to the empirical magnetic field data ^(^^) comprises fitting the synthetic magneticfields of the synthetic magnetic dipole source moment to the empirical magnetic fielddata ^(^^).In order to fit the synthetic magnetic dipole source moments to the empirical magnetic fielddata ^(^^), the at least one processor 104 defines a plurality of dipole source locations That is, at214, the at least one processor 104 defines the dipole source locations It will be understood that indefining a dipole source location the at least one processor 104 sets values of ^, ^ and ^ coordinatesof that dipole source location Each synthetic magnetic dipole source moment is associated with a respective dipole sourcelocation In particular, each synthetic magnetic dipole source moment is located at arespective one of the dipole source locations The dipole source locations may be stored in a dipole source location matrix ^^,^. In other words, a dipole source location matrix ^^,^comprises eachdipole source location Defining the dipole source locations may be referred to as generatingdipole source location data ^^,^.Each dipole source location is provided with an ^ index. As described herein, at 212, the at least oneprocessor 104 selects empirical magnetic field data ^(^^) for measurement locations ^^ within theinfluence region 105^ defined for the target region ^^ selected at 206. The value of the ^ indexindicates the particular selected target region ^^for which the dipole source locations ^^,^are being defined.Each dipole source location ^^,^ is provided with an ^ index. The at least one processor 104 defines ^dipole source locations ^^,^ at 214. ^ is an integer. The value of the ^ index of a particular dipole sourcelocation ^^,^identifies that dipole source location ^^,^within the set of dipole source locations ^^,^defined at 214. The at least one processor 104 defines the dipole source locations ^^,^based on one or more dipolesource location conditions. The one or more dipole source location conditions specify the locations atwhich the dipole source locations ^^,^ are defined, within the source region 113^ . In other words, theone or more dipole source location conditions specify the ^, ^ and ^ coordinates that are defined for eachdipole source location ^^,^.The at least one processor 104 defines a first group of dipole source locations ^^,^. The first group ofdipole source locations form a layer of dipole source locations Therefore, the at least oneprocessor 104 defines a first layer of dipole source locations The dipole source locations of the first group of dipole source locations are each defined at arespective ground clearance. The ground clearance at which the dipole source locations of the firstgroup of dipole source locations are defined is the same. That is, each dipole source location of the first group is defined at a common ground clearance. The dipole source locations of the first group are defined on the upper source region boundary 115^of the source region 113^. In other words,the dipole source locations of the first group are defined on the first source region surface 117^ . Thedipole source locations of the first group are therefore defined within the source region 113^ .Each dipole source location of the first group has a ground clearance of 0. That is, each dipole sourcelocation of the first group is located at ground level. The first ground clearance is therefore 0. Thus,at 214, the at least one processor 104 defines a first group of dipole source locations at ground levelwithin the source region 113^ . In other words, at 214, the at least one processor 104 defines a first layerof dipole source locations at ground level.The dipole source locations of the first group are arranged in a first dipole source arrangement. Oneor more dipole source location condition defines how the dipole source locations of the first groupare arranged. That is, one or more dipole source location condition defines how the dipole sourcelocations of the first layer are arranged, at ground level. For example, one or more dipole sourcelocation condition may define a minimum allowable distance between dipole source locations of thefirst group. In the illustrated embodiment, the dipole source locations of the first group are arrangedin a grid. That is, the first dipole source arrangement is a grid. The at least one processor 104 thereforedefines a plurality of rows and a plurality of columns of dipole source locations The dipole sourcelocations of the first group are arranged in a plurality of rows and a plurality of columns.The at least one processor 104 defines a second group of dipole source locations The second groupof dipole source locations form a second layer of dipole source locations Therefore, the at leastone processor 104 defines a second layer of dipole source locations The dipole source locations of the second group of dipole source locations are each defined at arespective depth. The depth at which the dipole source locations of the second group of dipole sourcelocations are defined is the same. That is, each dipole source location of the second group isdefined at a common depth. The depth is a depth below ground level. The dipole source locations ^^,^ ofthe second group are defined on the lower source region boundary 115^ of the source region 113^ . Inother words, the dipole source locations of the second group are defined on the second source regionsurface 119^ of the source region 113^ . The dipole source locations ^^,^ of the second group aretherefore defined within the source region 113^.Each dipole source location ^^,^of the second group has a positive depth. That is, each dipole sourcelocation ^^,^ of the second group is located below ground level. In the illustrated embodiment, the at leastone processor 104 defines the dipole source locations ^^,^ of the second group at a depth of 2000m belowground level. The vertical position of the dipole source locations ^^,^of the second group is related to a lateral dimension of the source region 113^.The dipole source locations ^^,^of the second group of dipole source locations ^^,^ are defined at a depththat is equal to a lateral dimension of the source region 113^ . The lateral dimension of the source region113^ is a straight-line distance between a point on a lateral boundary 115^ of the source region 113^and a second, opposing point on an opposite lateral boundary 115^ of the source region 113^ . Thelateral dimension of the source region 113^ may be a minimum distance between two opposing lateralboundaries 115^of the source region 113^. Therefore, at 214, the at least one processor 104 defines a second group of dipole source locations ^^,^below ground level, within the source region 113^. The second group of dipole source locations ^^,^ arethe dipole source locations ^^,^of the second layer. The dipole source locations ^^,^of the second group are arranged in a second dipole source arrangement.One or more dipole source location condition defines how the dipole source locations ^^,^ of the secondgroup are arranged. That is, one or more dipole source location condition defines how the dipole sourcelocations ^^,^ of the second layer are arranged, below ground level. For example, one or more dipolesource location condition may define a minimum allowable distance between dipole source locations ^^,^of the second group. In the illustrated embodiment, the dipole source locations ^^,^ of the second groupare arranged in a grid. That is, the second dipole source arrangement is a grid. The at least one processor104 therefore defines a plurality of rows and a plurality of columns of dipole source locations ^^,^ in thesecond layer. The dipole source locations ^^,^ of the second group are arranged in a plurality of rows anda plurality of columns.Each dipole source location ^^,^ of the second group is vertically aligned with a corresponding dipolesource location ^^,^ of the first group. The ^ and ^ coordinates of a particular dipole source location ^^,^of the second group are the same as the ^ and ^ coordinates of the corresponding dipole sourcelocation ^^,^ of the first group. The rows of the dipole source locations ^^,^ of the first group are parallelwith the rows of the dipole source locations ^^,^ of the second group. The rows of the dipole sourcelocations ^^,^ of the first group are vertically aligned with the rows of the dipole source locations ^^,^ ofthe second group. The columns of the dipole source locations ^^,^ of the first group are parallel with thecolumns of the dipole source locations ^^,^ of the second group. The columns of the dipole sourcelocations ^^,^ of the first group are vertically aligned with the columns of the dipole source locations ^^,^of the second group.The at least one processor 104 locates a synthetic magnetic dipole source moment ^^^^,^^ at each dipolesource location ^^,^. In particular, the at least one processor 104 sets the location of each syntheticmagnetic dipole source moment ^^^^,^^ to a corresponding dipole source location Therefore, eachsynthetic magnetic dipole source moment may be said to be associated with a dipole sourcelocation The dipole source location ^^,^ that a synthetic magnetic dipole source moment isassociated with is the dipole source location at which that synthetic magnetic dipole sourcemoment located.In order to fit the synthetic magnetic dipole source moments to the empirical magnetic fielddata ^ , the at least one processor 104 constructs a forward modelling matrix ^^. In other words,fitting the synthetic magnetic dipole source moments to the empirical magnetic field data ^(^^)comprises constructing the forward modelling matrix ^^. Therefore, at 214, the at least one processor104 constructs the forward modelling matrix ^^.The forward modelling matrix ^^ relates the synthetic magnetic dipole source moments disposed at the dipole source locations to the empirical magnetic field data ^(^^). The forwardmodelling matrix ^^encapsulates a mathematical relationship between the synthetic magnetic dipolesource moments and the observed magnetic field, as represented by the empirical magnetic fielddata ^(^^) selected at 212. The forward modelling matrix ^^ therefore enables the calculation of thesynthetic magnetic dipole source moments using the selected empirical magnetic fielddata ^(^^).The forward modelling matrix ^^ is provided with an ^ index herein. The ^ index of the forwardmodelling matrix ^^indicates the target region ^^for which the forward modelling matrix ^^is beingconstructed. That is, the ^ index of the forward modelling matrix ^^ corresponds to the ^ index of thetarget region ^^ for which the related dipole source locations are defined.At 214, the at least one processor 104 calculates a plurality of outputs of a forward modellingfunction ^^,^. The forward modelling function ^^,^ calculates magnetic field components induced by aunit dipole located at a specific dipole source location ^^,^, for a given measurement location ^^,^ withinthe influence region 105^ . In this case, the magnetic field components are components of magnetic fieldintensity. As described herein, different forward modelling functions can be used for other magnetic field characteristics (e.g. gradient etc.).The forward modelling function ^^,^ provides a mathematical framework to relate the synthetic magneticdipole source moments to the empirical magnetic field data ^(^^) selected at 212. Each outputof the forward modelling function ^^,^ is calculated using a dipole source location as an input of theforward modelling function ^^,^. Each output of the forward modelling function ^^,^ is calculated usinga measurement location ^^,^ of the influence region 105^ as an input of the forward modellingfunction ^^,^. In other words, each output of the forward modelling function ^^,^ is calculated using ameasurement location ^^,^and a dipole source location ^^,^as inputs of the forward modellingfunction ^^,^.The forward modelling function is: where: ^^,^is the forward modelling function; ^^is a magnetic constant; ^^,^ is a measurement location of the influence region 103^ ;is a dipole source location; This forward modelling function ^^,^ is used where the characteristic of the magnetic field that is beingestimated across the target surface ^ is magnetic field intensity |^|. As described herein, differentforward modelling functions are used for estimating different characteristics of the magnetic field.The at least one processor 104 computes an output of the forward modelling function ^^,^ for everydipole source location – measurement location ^^,^ pair that can be formed from the dipole sourcelocations ^^,^ and measurement locations ^^,^. It will be appreciated that these are the dipole sourcelocation ^^,^ of the defined source region 113^ and the measurement location ^^,^ within the definedinfluence region 103^ .It can be seen from these equations that as part of this process, the at least one processor 104 calculates adifference between each measurement location ^^,^of the influence region 103^, and each dipole sourcelocation In other words, the at least one processor 104 determines a vector ^^^,^ − ^^,^^ for eachdipole source location and each measurement location ^^,^ of the influence region 103^ . Incomputing the forward modelling matrix ^^, the at least one processor 104 calculates, for each measurement location ^^,^, magnetic field components induced by three orthogonal unit dipoles at eachdipole source location ^^,^. The at least one processor 104 stores the magnetic field components in theforward modelling matrix ^^. In other words, the at least one processor 104 assembles the forwardmodelling matrix ^^from the calculated outputs of the forward modelling function ^^,^. The at least one processor 104 applies a smoothness constraint to the forward modelling matrix ^^. Insome embodiments, applying a smoothness constraint to the forward modelling matrix ^^ comprisesappending one smoothness constraint row to the forward modelling matrix ^^. In some embodiments,applying a smoothness constraint to the forward modelling matrix ^^ comprises appending a plurality ofsmoothness constraint rows to the forward modelling matrix ^^. In some embodiments, a smoothingparameter can be used to control the amount of smoothing. A user of the system may set or adjust a value of the smoothing parameter to control the amount of smoothing. This may be done for a given noiseregime of the empirical magnetic field data ^(^^) selected at 212. The smoothness constraint(s) areapplied to the forward modelling matrix ^^to mitigate the effects of noise and instability.Fitting the synthetic magnetic dipole source moments to the empirical magnetic fielddata ^(^^) comprises determining synthetic magnetic dipole source data The syntheticmagnetic dipole source data is in the form of a magnetic dipole source momentsvector The synthetic magnetic dipole source data is determined using the empiricalmagnetic field data ^(^^) and the forward modelling matrix ^^. That is, the synthetic magnetic dipolesource moments vector is determined using the empirical magnetic field data ^(^^) and theforward modelling matrix ^^.At 214, the at least one processor 104 determines the synthetic magnetic dipole source datausing the empirical magnetic field data ^(^^) and the forward modelling matrix ^^. The syntheticmagnetic dipole source data ^^^^,^^ comprises the synthetic magnetic dipole source moments ^(^^,^).Therefore, at 214, the at least one processor 104 determines the synthetic magnetic dipole sourcemoments ^(^^,^) using the empirical magnetic field data ^(^^) and the forward modelling matrix ^^.The synthetic magnetic dipole source data may be expressed as: where each of is a synthetic magnetic dipole source moment.Determining the synthetic magnetic dipole source data comprises solving a weighted leastsquares problem in which the empirical magnetic field data ^(^^) and the forward modelling matrix ^^are inputs. Specifically, the vector of the synthetic magnetic dipole source moments ^(^^,^)(for which the values are still unknown), is arranged so that it is a single column, with the ^ dipole sourcemoments ^^(^^,^) followed by the ^ dipole source moments ^^(^^,^), followed by the ^ dipole sourcemoments . That is, the vector is arranged such that: This enables the establishment of the linear system of equations that relates the empirical magnetic fielddata ^(^^) to the synthetic magnetic dipole source data in which synthetic magnetic dipolesource moments ^(^^,^) are the unknowns. It will be understood that the measurement noise ^ will beknown, and associated with the particular sensor system(s) that are used to obtain the empirical magneticfield data ^(^^). This system of linear equations is: where: ^^is the forward modelling matrix; ^^^^,^^ is the vector of the synthetic magnetic dipole source moments^(^^) is the vector of empirical magnetic field data associated with the relevant influenceregion 103^ ; and^ is a noise vector associated with the first sensor system 124 and the second sensor system 144.The at least one processor 104 solves for the synthetic magnetic dipole source moments of thesynthetic magnetic dipole source data using this system of linear equations. That is, the at leastone processor 104 solves for the synthetic magnetic dipole source data thereby determining thesynthetic magnetic dipole source moments ^^^^,^^.When the measurement noise is known, it may be used to construct weighted linear equations for which more accurate solutions may be obtained than for un-weighted equations. In determining the syntheticmagnetic dipole source moments ^(^^,^), the at least one processor 104 has fitted the synthetic magneticdipole source moments to the empirical magnetic field data ^(^^). That is, in determining thesynthetic magnetic dipole source moments ^(^^,^), the at least one processor 104 has fitted the syntheticmagnetic fields of the synthetic magnetic dipole source moments to the empirical magnetic fielddata ^(^^). Further, by determining the synthetic magnetic dipole source moments ^(^^,^), the at leastone processor 104 has determined the synthetic magnetic dipole source data Determining Target Magnetic Field Data The method 200 involves the estimation of the characteristic of the local magnetic field across the targetregion That is, the method 200 involves the estimation of the characteristic of the local magneticfield at the target locations ^^,^ of the target region ^^,^. This may be referred to as determining targetmagnetic field data with the target magnetic field data comprising estimates of thecharacteristic of the local magnetic field at each measurement location of the target region ^^,^.At 216, the at least one processor 104 determines the target magnetic field data The targetmagnetic field data is indicative of the characteristic of the local magnetic field at the targetlocations of the target region ^^,^. In particular the target magnetic field data is indicativeof the characteristic of the local magnetic field at each target location of the target region ^^,^selected at 206.The at least one processor 104 calculates the target magnetic field data using the syntheticmagnetic dipole source moments ^(^^,^). That is, the at least one processor 104 determines the targetmagnetic field data using the synthetic magnetic dipole source data It may thereforebe said that at 216, the at least one processor 104 calculates target magnetic field data indicativeof the characteristic of the magnetic field, at one or more target location ^^,^, using the fitted equivalentmagnetic dipole source moments ^(^^,^).To determine the target magnetic field data a target surface forward modelling matrix ^^,^ isconstructed. The at least one processor 104 constructs the target surface forward modelling matrix ^^,^.The target surface forward modelling matrix ^^,^ relates the synthetic magnetic field generated by thesynthetic magnetic dipole source moments ^(^^,^) to the target locations ^^,^. That is, the target surfaceforward modelling matrix ^^,^ enables one or more characteristic of a synthetic magnetic field generatedby the synthetic magnetic dipole source moments ^(^^,^) to be determined at the target locations ^^,^.As the synthetic magnetic dipole source moments ^(^^,^) have been fitted to the selected empiricalmagnetic field data ^(^^), the synthetic magnetic field generated by the synthetic magnetic dipolesource moments ^(^^,^) is an estimation of the local magnetic field at and around the measurementlocations ^^,^ of the influence region 103^ , including at the target region ^^,^ selected at 206. Acharacteristic of the synthetic magnetic field generated by the synthetic magnetic dipole sourcemoments ^(^^,^), such as magnetic field intensity, can therefore be calculated across the target regionselected at 206, and the calculated characteristic will be an estimate of that characteristic of the localmagnetic field at that location of the survey area 103.The target surface forward modelling matrix ^^,^ is analogous to the forward modelling matrix usedin the fitting process, but it is calculated for target locations ^^,^ rather than the original measurementlocations ^^,^. That is, the target surface forward modelling matrix ^^,^ maps a characteristic of themagnetic field generated by the synthetic magnetic dipole source moments ^(^^,^), to targetlocations ^^,^.The target surface forward modelling matrix ^^,^ is provided with a ^ index. The ^ index of the targetsurface forward modelling matrix ^^,^ indicates the particular target surface ^ that the selected targetregion ^^,^ is on. That is, the ^ index of the target surface forward modelling matrix ^^,^ indicates theground clearance of the target location ^^,^. The target surface forward modelling matrix ^^,^ isprovided with an ^ index. The ^ index of the target surface forward modelling matrix ^^,^ indicates theselected target region ^^,^for which the target surface forward modelling matrix ^^,^is constructed. The calculation of the magnetic field components generated by the synthetic magnetic dipole sourcemoments ^(^^,^) at each target location ^^,^ takes into account the spatial relationships between thedipole source locations ^^,^ and the target locations ^^,^. The process uses the same fundamentalprinciples of magnetostatics employed in the initial fitting process. The target surface forward modelling matrix ^^,^encapsulates these calculations, providing a mathematical framework to determine thecharacteristics of the local magnetic field on the target surface ^ that is generated by the syntheticmagnetic dipole source moments ^(^^,^).Once the target surface forward modelling matrix ^^,^ is constructed, it is multiplied by the syntheticmagnetic dipole source data This multiplication operation effectively propagates syntheticmagnetic fields from the synthetic magnetic dipole source moments ^^^^,^^ to the target locations ^^,^,resulting in the target magnetic field data Constructing the target surface forward modelling matrix ^^,^comprises calculating a plurality of outputs of a target surface forward modelling function ^^,^. At 216, the at least one processor 104calculates the outputs of the target surface forward modelling function ^^,^. The target surface forwardmodelling function ^^,^calculates the magnetic field components induced by a unit dipole at a specificdipole source location ^^,^, for a given target location ^^,^.Each output of the target surface forward modelling function ^^,^is calculated using a dipole source location ^^,^as an input of the target surface forward modelling function ^^,^. Each output of the targetsurface forward modelling function ^^,^ is calculated using a target location ^^,^ as an input of the targetsurface forward modelling function ^^,^. In other words, each output of the target surface forwardmodelling function ^^,^ is calculated using a target location ^^,^ and a dipole source location ^^,^ asinputs of the target surface forward modelling function ^^,^. In some embodiments of the present disclosure, the target surface forward modelling function is: where: ^^,^is the target surface forward modelling function; ^^is a magnetic constant; ^^,^is a target location of the selected target region ^^,^; ^^,^is a dipole source location; ^^^^ =is a first sub-matrix of the target surface forward modelling function;^^^^ =is a second sub-matrix of the target surface forward modelling function; is a third sub-matrix of the target surface forward modelling function; and This target surface forward modelling function ^^,^is used where the characteristic of the magnetic fieldthat is being estimated across the target surface ^ is magnetic field intensity |^|. As described herein,different target surface forward modelling functions are used for estimating different characteristics of the magnetic field. The at least one processor 104 computes an output of the target surface forward modelling function ^^,^for every dipole source location ^^,^ – target location ^^,^ pair that can be formed from the dipole sourcelocations ^^,^ and target locations ^^,^. It will be appreciated that these are the target locations ^^,^ ofthe selected target region ^^,^and the dipole source locations ^^,^defined for that selected targetregion ^^,^.It can be seen from these equations that as part of this process, the at least one processor 104 calculates adifference between each target location ^^,^ and each dipole source location ^^,^. In other words, the atleast one processor 104 determines a vector for each dipole source location and eachtarget location ^^,^ pair. In computing the target surface forward modelling matrix ^^,^, the at least oneprocessor 104 calculates, for each target location ^^,^ of the selected target region ^^,^, magnetic fieldcomponents induced by three orthogonal unit dipoles at each dipole source location ^^,^. The at least one processor 104 stores the magnetic field components in the target surface forward modelling matrix ^^,^.In other words, the at least one processor 104 assembles the target surface forward modelling matrix ^^,^from the calculated outputs of the target surface forward modelling function ^^,^.At 216, the at least one processor 104 determines the target magnetic field data The at least oneprocessor 104 determines the target magnetic field data ^^^^,^^ using the synthetic magnetic dipolesource moments Determining the target magnetic field data comprises multiplying thetarget surface forward modelling matrix ^^,^ with the synthetic magnetic dipole source data Therefore, at 226, the at least one processor 104 multiplies the target surface forward modellingmatrix ^^,^ with the synthetic magnetic dipole source data ^^^^,^^, thereby determining the targetmagnetic field data ^ . The target magnetic field data ^ may be said to be associated withthe selected target region ^^on which the target locations are located. The synthetic magneticdipole source data comprises the synthetic magnetic dipole source moments ^(^^,^), so the atleast one processor 104 determines the target magnetic field data using the synthetic magneticdipole source moments . The target magnetic field data comprises a magnetic fieldintensity value ^^^^^^^ for each target location ^^,^. The target magnetic field data comprisesthe magnetic field intensity values ^^^^^^^. In some embodiments, the target magnetic fielddata comprises the target location ^^,^.The at least one processor 104 iterates through steps 206-216 for each target region ^^,^defined at 204.In doing so, the at least one processor 104 determines target magnetic field data for each targetregion ^^. In particular, the at least one processor 104 determines target magnetic field data forthe target locations ^^,^ of each target region ^^. Examples of these iterations are shown in Figures 7-9.Figure 7 shows an example a first target region ^^ and an influence region 105^ and a source region113^ that are defined for the first target region ^^. Figure 8 shows an example a second target region ^^that is different to the first target region ^^, and an influence region 105^ and a source region 113^ thatare defined for the second target region ^^. The second target region ^^ is laterally offset with respect tothe first target region ^^. There is an overlap between the first target region ^^and the second target region ^^. There is also a portion of the second target region ^^that does not overlap with the first targetregion ^^. Figure 9 shows example a third target region ^^ that is different to the first target region ^^and the second target region ^^, and an influence region 105^ and a source region 113^ that are definedfor the third target region ^^. The third target region ^^ is laterally offset with respect to the secondtarget region ^^. There is an overlap between the third target region ^^ and the second target region ^^.There is an overlap between the third target region ^^ and the first target region ^^. There is also aportion of the third target region ^^that does not overlap with the first target region ^^and the second target region ^^. It will be appreciated that in some embodiments, the target regions ^^can be defined such that there is no overlap between them. For example, the target regions ^^can be defined to sharepart of their boundaries 107^ with other target regions ^^. The at least one processor 104 iteratesthrough steps 206-216 for target regions ^^ across the entire survey area 103.It will be appreciated that for target regions ^^ at or near the lateral boundaries of the survey area 103,empirical magnetic field data ^(^) is not available for some of the defined influence region 105^ . Themethod 200 can be performed none-the-less, with the results being acceptable representations of the localmagnetic field in those target regions ^^. In these cases, the relevant boundaries 109^ of the influenceregion 105^may correspond to one or more boundary of the survey area 103. Thus, the influence region105^ will not be centred on the relevant target region ^^. This may also be the case for the sourceregion 113^ .Construct a Target Surface Magnetic Field Model ^ By iteratively performing steps 206 to 216 of the method 200, the at least one processor 104 hasdetermined target magnetic field data ^(^^) for the target locations ^^,^ of each target region ^^. The atleast one processor 104 has therefore determined an estimate of the characteristic of the local magneticfield at each target location ^^. At 218, the at least one processor 104 constructs a target surface magneticfield model ^. The target surface magnetic field model ^ is a dataset that comprises an estimate of thecharacteristic of the local magnetic field at each target location ^^. The target surface magnetic fieldmodel ^ is a matrix that includes vectors that each include the value of the characteristic of the localmagnetic field at a target location ^^, and the ^, ^ and ^ coordinates of that target location ^^. In thedescribed case, the target surface magnetic field model ^ will comprise a plurality of vectors, eachincluding the ^, ^ and ^ coordinates of a respective target location ^^, and the estimate of the magneticfield intensity ^^^^^^^ at that target location ^^ that was determined at an iteration of step 216.The target magnetic field data ^(^^) may be displayed on a display of a computing system to provide agraphical representation of the magnetic field characteristic at the target locations ^^. In someembodiments, the method 200 comprises generating a graphical representation of the target magnetic field data ^(^^). The computing system 102 may generate the graphical representation of the target magnetic field data ^(^^). The at least one processor 104 may render the graphical representation of thetarget magnetic field data ^(^^) on a display. For example, the at least one processor 104 may render thegraphical representation of the target magnetic field data ^(^^) on a display of the user interface 111.Alternatively, the at least one processor 104 may transmit the target magnetic field data ^(^^) to anothercomputing system that displays the target magnetic field data ^(^^). and the Empirical Magnetic Field Data ^(^^)The misfit between the simulated magnetic fields of the fitted synthetic magnetic dipole sourcemoments ^(^^,^) and the empirical magnetic field data ^(^^) of a particular influence region 105^may be computed as the weighted root mean square error: ∑ ^^ ^|^ (^ )| − ^^ ^^ ^^^^^^^^ ^ ^ ^ ^,^^^^^ =∑^ ^^^^^where:^^^^ is the weighted root mean square error;^^is a weights matrix; ^is an index indicating the particular weights matrix of an iteration of the sum, where 1 ≤ ^ ≤^; is the empirical magnetic field data ^(^^); and^^^^^^,^^^ is a magnetic field calculated using the forward modelling matrix ^^ and themagnetic dipole source moments , where ^^^^^^,^^^ = ^^^ ∙ ^^^^,^^^.It will be noted that the noise term ^ is ignored in this instance.While the above describes comparing fitted magnetic field intensity values, it will be appreciated that the same method could be used for magnetic field gradients, where the empirical magnetic field data ^(^^)is gradient data. Further, as described herein, the empirical magnetic field data ^(^^) can include bothmagnetic field intensity and gradient measurements. In such a case, the RMSE for the empirical magneticfield data ^(^^) could be computed in a similar manner. It will be appreciated that in such a case,suitable weighting would be used as the gradients would have different noise characteristics than the magnetic field intensity values.Misfit grids can be graphically represented when the target magnetic field data is ultimatelyrendered for display.Experimental Validation of the Method 200 Using Survey Data Evidence of the effectiveness of the method 200 can be provided by implementing the method 200 onexisting survey data. Figures 10A to 14D relate to a computational validation of the method 200 usingsurvey data over a survey area.A first dataset, ^^^^, for “helicopter mag”, was obtained from a sensor system mounted below ahelicopter. The first dataset has an average ground clearance around 40m, but includes many parts withvery large ground clearance. A second dataset, ^^^^, for “fixed-wing mag”, was obtained using asensor system that forms part of a fixed-wing aircraft sensor installation. The second dataset has anaverage ground clearance of 80m. The first dataset, ^^^^, and the second dataset, ^^^^, can, together,be considered empirical magnetic field data within the context of the method 200.The experimental validation of the method 200 included fitting synthetic magnetic dipole sources to theempirical magnetic field data (i.e. the first dataset, ^^^^, and the second dataset, ^^^^) and thenreconstructing magnetic fields of the synthetic magnetic dipole sources at the desired constant groundclearance (the target ground clearance). In this case, the desired constant ground clearance was 60m.Therefore, the target surface ^ of the method 200, in the context of this validation, is a surface with aground clearance of 60m. As described with reference to the method 200, the processing was done for small groups of points thatoccupy certain lengths along the flightlines of the original data, i.e. the described influence regions 105^ .For each data group, a square source region is constructed with a lateral dimension (i.e. a side length)given in the control file. Synthetic magnetic field source moments are then fitted to a selected subset ofthe original data within the defined influence region. The magnetic fields emanating from those syntheticmagnetic field source moments are displayed, together with the original data, using natural neighbourinterpolation for plotting.Using only the helicopter data, Figures 10A to 10F show screen displays after collocating the data to aconstant ground clearance of 60m. This is higher than the roughly 40 m ground clearance in most parts ofthe area and as a result, the collocated data resembles the simple grid.Figure 10A shows a graphical representation of the first dataset ^^^^ which was obtained over part of asurvey area. The target flight path ground clearance at which this data was obtained was 40m. It will beunderstood that this dataset is an example of empirical magnetic field data ^(^) as described herein.More specifically, this data may be considered to have been obtained on a first flight path at a first target flight path ground clearance. Figure 10B shows a graphical representation of the actual ground clearance of the sensor system of thehelicopter that obtained the first dataset ^^^^, over the relevant part of the survey area. In Figure 10B,the colour blue indicates near-nominal ground clearance and warmer colours (yellow and red) indicate higher ground clearances. Figure 10C shows a graphical representation of the elevation of the ground, over the relevant part of the survey area. Again, the colour blue indicates near-nominal ground elevation and warmer colours (yellow and red) indicating higher ground elevations.Figure 10D shows a graphical representation of target magnetic field data determined, for a targetground clearance, in accordance with the method 200. However, the target magnetic field data ^^^^,^^,in this case, was determined without empirical magnetic field data ^(^) at a different target flight pathground clearance to that of the first dataset ^^^^. It will therefore be understood that the method 200may be performed without using two empirical magnetic field data ^(^) datasets collected at differentground clearances. In some embodiments, empirical magnetic field data ^(^) collected at only one targetflight path ground clearance can be sufficient. Figure 10E shows a graphical representation of a difference between the empirical magnetic field data^(^) of Figure 10A and the target magnetic field data of Figure 10D. In Figure 10E, whiteindicates no difference, blue indicates a negative difference and red indicates a positive difference.Figure 10F shows a graphical representation of a misfit (RMSE) between the empirical magnetic fielddata ^(^) of Figure 10A and the target magnetic field data Figure 10D. In Figure 10E, whiteindicates no difference and red indicates a difference.Figure 11A shows a graphical representation of the second dataset ^^^^ which was obtained over thepart of a survey area also shown in Figures 10A to 10F. The target flight path ground clearance at whichthis data was obtained was 80m. It will be understood that this dataset is an example of empiricalmagnetic field data ^(^) as described herein. More specifically, this data may be considered to have beenobtained on a second flight path at a second target flight path ground clearance. Figure 11B shows a graphical representation of the actual ground clearance of the sensor system of thefixed-wing aircraft that obtained the second dataset ^^^^, over the relevant part of the survey area. InFigure 11B, the colour blue indicates near-nominal ground clearance and warmer colours (yellow and red) indicating higher ground clearances.Figure 11C shows a graphical representation of target magnetic field data for a target groundclearance, determined in accordance with the method 200, using the second dataset ^^^^. However, thetarget magnetic field data in this case, was determined without empirical magnetic fielddata ^(^) at a different target flight path ground clearance to that of the second dataset ^^^^. Asmentioned elsewhere herein, it will therefore be understood that the method 200 may be performedwithout using two empirical magnetic field data ^(^) datasets collected at different ground clearances. Insome embodiments, empirical magnetic field data ^(^) collected at only one target flight path groundclearance can be sufficient.Figure 11D shows a graphical representation of a difference between the empirical magnetic field data^(^) of Figure 11A and the target magnetic field data Figure 11C. In Figure 11D, whiteindicates no difference, blue indicates a negative difference and red indicates a positive difference.Figure 12A shows a graphical representation of the first dataset ^^^^ and the second dataset ^^^^. Itwill be understood that this dataset is an example of empirical magnetic field data ^(^) as describedherein.Figure 12B shows a graphical representation of the actual ground clearance of the fixed-wing aircraft thatobtained the second dataset ^^^^, and the helicopter that obtained the first dataset ^^^^, over therelevant part of the survey area. In Figure 12B, the colour blue indicates near-nominal ground clearanceand warmer colours (yellow and red) indicate higher ground clearances.Figure 12C shows a graphical representation of target magnetic field data determined for a surfaceat a target ground clearance of 60m, in accordance with the method 200, using the data represented inFigure 12A. In this case, the second dataset ^^^^ obtained by the fixed-wing aircraft can be consideredthe first set of empirical magnetic field data ^(^^) described in the method 200. The first dataset ^^^^obtained by the helicopter at a lower ground clearance can be considered the second set of empiricalmagnetic field data ^(^^) described in the method 200. The combination of these to datasets is theempirical magnetic field data ^(^) of the method 200.Figure 12D shows a graphical representation of a difference between the empirical magnetic field data^(^) of Figure 12A and the target magnetic field data Figure 12C. In Figure 12D, whiteindicates no difference, blue indicates a negative difference and red indicates a positive difference. Figure 13A shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 12A, for a target surface at a 60m ground clearance. The flightlines shownin Figure 12C are hidden in Figure 13A. Figure 13B shows a graphical representation of a difference between the empirical magnetic field data of Figure 12A and the target magnetic field data of Figure 13A, where white indicates no difference, blue indicates a negative difference and red indicates a positive difference.Figure 13C shows a graphical representation of target magnetic field data determined using the empiricalmagnetic field data of Figure 12A, for a t...

Claims

CLAIMS1. A method (200) comprising:selecting empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103);fitting synthetic magnetic dipole source momentsto the empirical magnetic fielddata ^(^^); anddetermining target magnetic field datathat is indicative of a magnetic field characteristicof a local magnetic field at one or more target locationusing the synthetic magnetic dipole sourcemoments2. The method (200) of claim 1, wherein:the empirical magnetic field data ^(^^) comprises:a first set of magnetic field datathat is associated with a first flight path (123); anda second set of magnetic field datathat is associated with a second flight path (125)that is different from the first flight path (123); andthe method (200) further comprises defining the one or more target locationat a target groundclearance that is different to a target flight path ground clearance of the first flight path (123) and a targetflight path ground clearance of the second flight path (125).

3. The method (200) of claim 2, further comprising defining a plurality of target regions ^^ at thetarget ground clearance.

4. The method (200) of claim 3, further comprising selecting a target region ^^ of the plurality oftarget regions ^^, the one or more target locationbeing within the selected target region ^^.

5. The method (200) of claim 4, further comprising defining a source region 113^ for the selectedtarget region ^^, the source region 113^ being a three-dimensional region that is bound by one or moresource region boundary 115^ .

6. The method (200) of claim 5, wherein:the source region 113^ is defined below the selected target region ^^; andthe source region 113^is bound by: an upper source region boundary 115^ defining a first source region surface 117^ ;a lower source region boundary 115^ defining a second source region surface 119^; andone or more lateral source region boundaries 115^, the one or more lateral source region boundaries 115^extending from the upper source region boundary 115^to the lower source region boundary 115^.

7. The method (200) of claim 6, wherein:the upper source region boundary 115^is defined at or near ground level; and the lower source region boundary 115^is defined at a depth below ground level.

8. The method (200) of any one of claims 4 to 7, further comprising defining the influenceregion 105^ around the target region ^^, the influence region 105^ being a three-dimensional regionthat is bound by one or more influence region boundary 109^.

9. The method (200) of claim 8 when dependent on claim 5, wherein the influence region 105^ isabove the source region 113^.

10. The method (200) of claim 8 or claim 9, when dependent on claim 5, wherein a lateral dimensionof the influence region 105^ is greater than, or equal to, a lateral dimension of the source region 113^ .

11. The method (200) of any one of claims 1 to 10, wherein the selected empirical magnetic field data^(^^) is empirical magnetic field data ^(^^) obtained at measurement locations ^^,^ within theinfluence region 105^ .

12. The method (200) of any one of claims 1 to 10, wherein the empirical magnetic field data ^(^^)comprises at least one of: a magnetic field intensity value |^|; a horizontal gradient value |^|; a transverse horizontal gradient valueand ^^ a longitudinal horizontal gradient value ^ ^^ ^; associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^.

13. The method (200) of any one of claims 1 to 12, further comprising defining each syntheticmagnetic dipole source moment ^^^^,^^ at a respective dipole source location14. The method (200) of any one of claims 1 to 13, wherein fitting the synthetic magnetic dipole sourcemoments to the empirical magnetic field data ^(^^) comprises constructing a forwardmodelling matrix ^^.

15. The method (200) of claim 14, wherein the forward modelling matrix ^^ relates the syntheticmagnetic dipole source momentsto the empirical magnetic field data ^(^^).

16. The method (200) of claim 14 or claim 15, when dependent on claim 13 and claim 11, wherein:the dipole source locationscomprise:a first group of dipole source locationsat or near ground level; anda second group of dipole source locationsat a defined depth below ground level;the dipole source locationsof the first group being above the dipole source locations^^,^of the second group; and fitting the synthetic magnetic dipole source momentsto the empirical magnetic fielddata ^(^^) comprises:calculating a plurality of outputs of a forward modelling function ^^,^, each output of theforward modelling function ^^,^ being calculated using a respective dipole source locationand arespective measurement location ^^,^as inputs of the forward modelling functionand assembling the forward modelling matrix ^^from the calculated outputs of the forward modelling function17. The method (200) of claim 16, wherein a difference between ground level and the defined depth isabout half of a lateral dimension of the source region 113^ .

18. The method (200) of claim 16 or claim 17, wherein calculating an output of the forward modellingfunction comprises calculating:where: is the forward modelling function; ^^is a magnetic constant; ^^,^ is a measurement location ^^,^ within the relevant influence region 103^ ;^^,^is a dipole source location;d19. The method (200) of any one of claims 14 to 18, further comprising applying a smoothnessconstraint to the forward modelling matrix ^^.

20. The method (200) of claim 19, wherein applying the smoothness constraint comprises appendingsmoothness constraint rows to the forward modelling matrix ^^.

21. The method (200) of any one of claims 14 to 20, wherein fitting the synthetic magnetic dipolesource momentsto the empirical magnetic field data ^(^^) comprises determining the syntheticmagnetic dipole source momentsusing the empirical magnetic field data ^(^^) and the forwardmodelling matrix ^^.

22. The method (200) of claim 21, wherein determining the synthetic magnetic dipole source momentscomprises solving a weighted least squares problem in which the empirical magnetic field data^(^^) and the forward modelling matrix ^^ are inputs.

23. The method (200) of claim 21 or claim 22, wherein determining the synthetic magnetic dipolesource momentscomprises solving:where:is a vector of the synthetic magnetic dipole source moments ^^^^,^^ that are to be solvedfor; ^^is the forward modelling matrix; ^(^^) is a vector of the empirical magnetic field data; and^ is a noise vector associated with a first sensor system (124) and a second sensor system (144).

24. The method (200) of any one of claims 1 to 23, wherein determining the target magnetic fielddata comprises constructing a target surface forward modelling matrix ^^,^ that is associatedwith the one or more target location ^^,^.

25. The method (200) of claim 24, wherein the target surface forward modelling matrix ^^,^ relates asynthetic magnetic field generated by the synthetic magnetic dipole source moments ^(^^,^) to the oneor more target location ^^,^.

26. The method (200) of claim 24 or claim 25, wherein constructing the target surface forwardmodelling matrix ^^,^comprises: calculating a plurality of outputs of a target surface forward modelling function ^^,^, each output of the target surface forward modelling function ^^,^being calculated using a respective dipole sourcelocation ^^,^ and a respective target location ^^,^ as inputs of the target surface forward modellingfunction ^^,^; and assembling the target surface forward modelling matrix ^^,^from the calculated outputs of the target surface forward modelling function ^^,^.

27. The method (200) of claim 26, wherein calculating an output of the target surface forwardmodelling function ^^,^comprises calculating:where: ^^,^is the target surface forward modelling function; ^^is a magnetic constant; ^^,^is a target location; ^^,^is a dipole source location; is a first sub-matrix of the target surface forward modellingfunction; ^^^^ =is a second sub-matrix of the target surface forwardmodelling function; ^^^^ =is a third sub-matrix of the target surface forwardmodelling function; and ^^,^=is a unit vector pointing from a source location to a target location ^^,^.

28. The method (200) of any one of claims 24 to 27, wherein determining the target magnetic fielddata comprises multiplying the target surface forward modelling matrix ^^,^ and syntheticmagnetic dipole source datacomprising the synthetic magnetic dipole sourcemoments ^^^^,^^.

29. The method (200) of claim 28, wherein the target magnetic field datacomprises amagnetic field intensity value for each target location ^^,^.

30. The method (200) of any one of claims 1 to 29, wherein the magnetic field characteristic ismagnetic field intensity.

31. The method (200) of any one of claims 1 to 17, or any one of claims 21 to 26 when dependent onany one of claims 1 to 17, wherein the magnetic field characteristic is a magnetic field gradient.

32. The method (200) of claim 2, further comprising:defining a plurality of target regions ^^ at the target ground clearance; anditeratively: selecting a target region ^^ of the plurality of target regions ^^;defining a source region 113^ for the selected target region ^^, the source region 113^being a three-dimensional region that is bound by one or more source region boundary 115^; defining the influence region 105^ around the target region ^^, the influence region 105^being a three-dimensional region that is bound by one or more influence region boundary 109^; defining the synthetic magnetic dipole source moments ^^^^,^^ at respective dipole sourcelocations ^^,^ of the source region 113^ ; anddetermining the target magnetic field datathe target magnetic field databeing indicative of the magnetic field characteristic of the local magnetic field at one or more targetlocation ^^,^ of the selected target region ^^;for each target region ^^.

33. The method (200) of any one of claims 1 to 32, further comprising constructing a model ^ of themagnetic field characteristic at the target ground clearance, across the survey area (103), using the targetmagnetic field data ^^^^,^^.

34. A system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at leastone processor (104), cause the at least one processor (104) to perform the method (200) of any one ofclaims 1 to 33.

35. A system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at leastone processor (104), cause the at least one processor (104) to:select empirical magnetic field data ^(^^) that is associated with an influence region 105^of a survey area (103);fit synthetic magnetic dipole source momentsto the empirical magnetic fielddata ^(^^); anddetermine target magnetic field datathat is indicative of a magnetic fieldcharacteristic of a local magnetic field at one or more target locationusing the synthetic magneticdipole source moments ^^^^,^^.

36. The system (100) of claim 35, wherein:the empirical magnetic field data ^(^^) comprises:a first set of magnetic field datathat is associated with a first flight path (123); anda second set of magnetic field data ^^^^,^^ that is associated with a second flight path (125)that is different from the first flight path (123); andthe computer-executable instructions (108), when executed by the at least one processor (104), arefurther configured to cause the at least one processor (104) to:define the one or more target locationat a target ground clearance that is different to atarget flight path ground clearance of the first flight path (123) and a target flight path ground clearance ofthe second flight path (125).

37. The system (100) of claim 35 or claim 36, wherein the computer-executable instructions (108),when executed by the at least one processor (104), are further configured to cause the at least oneprocessor (104) to define a plurality of target regions ^^ at the target ground clearance.

38. The system (100) of claim 37, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) toselect a target region ^^ of the plurality of target regions ^^, the one or more target locationbeingwithin the selected target region ^^.

39. The system (100) of claim 38, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) todefine a source region 113^ for the selected target region ^^, the source region 113^ being a three-dimensional region that is bound by one or more source region boundary 115^.

40. The system (100) of claim 39, wherein:the source region 113^ is defined below the selected target region ^^; andthe source region 113^is bound by: an upper source region boundary 115^ defining a first source region surface 117^ ;a lower source region boundary 115^ defining a second source region surface 119^; andone or more lateral source region boundaries 115^, the one or more lateral source region boundaries 115^extending from the upper source region boundary 115^to the lower source region boundary 115^.

41. The system (100) of claim 40, wherein:the upper source region boundary 115^is defined at or near ground level; and the lower source region boundary 115^is defined at a depth below ground level.

42. The system (100) of any one of claims 38 to 41, wherein the computer-executableinstructions (108), when executed by the at least one processor (104), are further configured to cause theat least one processor (104) to define the influence region 105^ around the target region ^^, theinfluence region 105^ being a three-dimensional region that is bound by one or more influence regionboundary 109^.

43. The system (100) of claim 42, when dependent on claim 39, wherein the influence region 105^ isabove the source region 113^.

44. The system (100) of claim 42 or claim 43, when dependent on claim 39, wherein a lateraldimension of the influence region 105^ is greater than, or equal to, a lateral dimension of the sourceregion 113^.

45. The system (100) of any one of claims 35 to 44, wherein the selected empirical magnetic field data^(^^) is empirical magnetic field data ^(^^) obtained at measurement locations ^^,^ within theinfluence region 105^ .

46. The system (100) of any one of claims 35 to 45, wherein the empirical magnetic field data ^(^^)comprises at least one of: a magnetic field intensity value |^|;a horizontal gradient value |^|; a transverse horizontal gradient value and ^^ a longitudinal horizontal gradient value ^ ^^ ^; associated with a measurement location ^^,^ of a plurality of measurement locations ^^,^, eachmeasurement location ^^,^being within the influence region 105^.

47. The system (100) of any one of claims 35 to 46, wherein the computer-executableinstructions (108), when executed by the at least one processor (104), are further configured to cause theat least one processor (104) to define each synthetic magnetic dipole sourceat arespective dipole source location ^^,^.

48. The system (100) of any one of claims 35 to 47, wherein fitting the synthetic magnetic dipolesource moments ^^^^,^^ to the empirical magnetic field data ^(^^) comprises constructing a forwardmodelling matrix ^^.

49. The system (100) of claim 48, wherein the forward modelling matrix ^^ relates the syntheticmagnetic dipole source momentsto the empirical magnetic field data ^(^^).

50. The system (100) of claim 48 or claim 49, when dependent on claim 47 and claim 45, wherein:the dipole source locationscomprise:a first group of dipole source locationsat or near ground level; and a second group of dipole source locationsat a defined depth below ground level; the dipole source locationsof the first group being above the dipole source locationsthe second group; and fitting the synthetic magnetic dipole source moments ^^^^,^^ to the empirical magnetic fielddata ^(^^) comprises:calculating a plurality of outputs of a forward modelling function ^^,^, each output of theforward modelling function ^^,^ being calculated using a respective dipole source locationand arespective measurement location ^^,^as inputs of the forward modelling functionand assembling the forward modelling matrix ^^from the calculated outputs of the forward modelling function51. The system (100) of claim 50, wherein a difference between ground level and the defined depth isabout half of a lateral dimension of the source region 113^ .

52. The system (100) of claim 50 or claim 51, wherein calculating an output of the forward modellingfunction ^^,^comprises calculating:where: ^^,^is the forward modelling function; ^^is a magnetic constant; ^^,^is a measurement location ^^,^within the relevant influence region 103^; ^^,^is a dipole source location;53. The system (100) of any one of claims 48 to 52, wherein the computer-executableinstructions (108), when executed by the at least one processor (104), are further configured to cause theat least one processor (104) to apply a smoothness constraint to the forward modelling matrix ^^.

54. The system (100) of claim 53, wherein applying the smoothness constraint comprises appendingsmoothness constraint rows to the forward modelling matrix ^^.

55. The system (100) of any one of claims 48 to 54, wherein fitting the synthetic magnetic dipolesource momentsto the empirical magnetic field data ^(^^) comprises determining the syntheticmagnetic dipole source momentsusing the empirical magnetic field data ^(^^) and the forwardmodelling matrix ^^.

56. The system (100) of claim 55, wherein determining the synthetic magnetic dipole source momentscomprises solving a weighted least squares problem in which the empirical magnetic field data^(^^) and the forward modelling matrix ^^ are inputs.

57. The system (100) of claim 55 or claim 56, wherein determining the synthetic magnetic dipolesource momentscomprises solving:where:is a vector of the synthetic magnetic dipole source moments ^^^^,^^ that are to be solvedfor; ^^is the forward modelling matrix; ^(^^) is a vector of the empirical magnetic field data; and^ is a noise vector associated with a first sensor system (124) and a second sensor system (144).

58. The system (100) of any one of claims 35 to 57, wherein determining the target magnetic fielddata comprises constructing a target surface forward modelling matrix ^^,^ that is associatedwith the one or more target location ^^,^.

59. The system (100) of claim 58, wherein the target surface forward modelling matrix ^^,^ relates asynthetic magnetic field generated by the synthetic magnetic dipole source moments ^(^^,^) to the oneor more target location ^^,^.

60. The system (100) of claim 58 or claim 59, wherein constructing the target surface forwardmodelling matrix ^^,^comprises: calculating a plurality of outputs of a target surface forward modelling function ^^,^, each output of the target surface forward modelling function ^^,^being calculated using a respective dipole sourcelocation ^^,^ and a respective target location ^^,^ as inputs of the target surface forward modellingfunction ^^,^; and assembling the target surface forward modelling matrix ^^,^from the calculated outputs of the target surface forward modelling function ^^,^.

61. The system (100) of claim 60, wherein calculating an output of the target surface forwardmodelling function ^^,^comprises calculating:where:^^,^is the target surface forward modelling function; ^^is a magnetic constant; ^^,^is a target location; ^^,^is a dipole source location; ^^^^ =is a first sub-matrix of the target surface forward modellingfunction; is a second sub-matrix of the target surface forwardmodelling function; ^^^^ =is a third sub-matrix of the target surface forwardmodelling function; and is a unit vector pointing from a source location^,^to a target location ^ .

62. The system (100) of any one of claims 58 to 61, wherein determining the target magnetic fielddata comprises multiplying the target surface forward modelling matrix ^^,^ and syntheticmagnetic dipole source datacomprising the synthetic magnetic dipole source moments63. The system (100) of claim 62, wherein the target magnetic field datacomprises amagnetic field intensity value for each target location ^^,^.

64. The system (100) of any one of claims 35 to 63, wherein the magnetic field characteristic ismagnetic field intensity.

65. The system (100) of any one of claims 35 to 51, or any one of claims 55 to 60, when dependent onany one of claims 35 to 51, wherein the magnetic field characteristic is a magnetic field gradient.

66. The system (100) of claim 36, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) to:define a plurality of target regions ^^ at the target ground clearance; anditeratively: select a target region ^^ of the plurality of target regions ^^;define a source region 113^ for the selected target region ^^, the source region 113^ beinga three-dimensional region that is bound by one or more source region boundary 115^;define the influence region 105^ around the target region ^^, the influence region 105^being a three-dimensional region that is bound by one or more influence region boundary 109^; define the synthetic magnetic dipole source momentsat respective dipole sourcelocations ^^,^ of the source region 113^ ; anddetermine the target magnetic field datathe target magnetic field databeing indicative of the magnetic field characteristic of the local magnetic field at one or more targetlocation of the selected target region ^^;for each target region ^^.

67. The system (100) of any one of claims 35 to 66, wherein the computer-executableinstructions (108), when executed by the at least one processor (104), are further configured to cause theat least one processor (104) to construct a model ^ of the magnetic field characteristic at the targetground clearance, across the survey area (103), using the target magnetic field data68. The system (100) of claim 36, or any one of claims 37 to 67 when dependent on claim 36, wherein:the system (100) comprises:a first aerial system (122); anda second aerial system (142);the first flight path (123) is a flight path of the first aerial system (122); andthe second flight path (125) is a flight path of the second aerial system (142).

69. The system (100) of claim 68, wherein the first aerial system (122) propels the second aerialsystem (142).

70. The system (100) of claim 68 or claim 69, wherein:the first aerial system (122) comprises a first sensor system (124) that obtains the first set ofmagnetic field datathe second aerial system (142) comprises a second sensor system (144) that obtains the second setof magnetic field data ^^^^,^^.

71. The system (100) of claim 70, wherein the first sensor system (124) comprises at least one of:a magnetometer; a Global Navigation Satellite System module; aheight measurement system;an Inertial Measurement Unit; anda gradiometer.

72. The system (100) of claim 70 or claim 71, wherein the second sensor system (144) comprises atleast one of: a magnetometer; a Global Navigation Satellite System module; aheight measurement system;an Inertial Measurement Unit; anda gradiometer.

73. A method (300) comprising:selecting empirical magnetic field data ^(^^) that is associated with an influence region 105^ of asurvey area (103);determining empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determining an optimised value of one or more parameter of a covariance function ^ by fitting thecovariance function ^ to the empirical covariance data ^^, the covariance function ^ defining acovariance between a magnetic field characteristic of a local magnetic field and locations ^ within theinfluence region 105^ ; anddetermining target magnetic field datathat is indicative of the magnetic fieldcharacteristic at one or more target locationusing the covariance function ^.

74. The method (300) of claim 73, wherein:the empirical magnetic field data ^(^^) comprises:a first set of magnetic field datathat is associated with a first flight path (123); anda second set of magnetic field datathat is associated with a second flight path (125)that is different from the first flight path (123); and the method (300) further comprises defining the one or more target locationat a target groundclearance that is different to a target flight path ground clearance of the first flight path (123) and a targetflight path ground clearance of the second flight path (125).

75. The method (300) of claim 74, further comprising defining a plurality of target regions ^^ at thetarget ground clearance.

76. The method (300) of claim 75, further comprising selecting a target region ^^ of the plurality oftarget regions ^^, the one or more target locationbeing within the selected target region ^^.

77. The method (300) of claim 76, further comprising defining the influence region 105^ around thetarget region ^^, the influence region 105^ being a three-dimensional region that is bound by one ormore influence region boundary 109^.

78. The method (300) of claim 76 or claim 77, wherein a lateral dimension of the influenceregion 105^ is greater than a lateral dimension of the target region ^^.

79. The method (300) of any one of claims 73 to 78, wherein the selected empirical magnetic fielddata ^(^^) is empirical magnetic field data ^(^^) obtained at measurement locations ^^,^ within theinfluence region 105^ .

80. The method (300) of any one of claims 73 to 79, wherein the empirical magnetic field data ^(^^)comprises at least one of: a magnetic field intensity value |^|; a horizontal gradient value |^|; a transverse horizontal gradient valueand ^^ a longitudinal horizontal gradient value ^ ^^ ^; associated with each measurement location ^^,^of a plurality of measurement locations ^^,^, each measurement location ^^,^being within the influence region 105^.

81. The method (300) of any one of claims 73 to 80, wherein determining the empirical covariancedata ^^comprises: defining a plurality of measurement location pairs ^^^,^, ^^,^^, each measurement location pair^^,^^ comprising a first measurement locationof the influence region 105^ and a secondmeasurement location ^^,^of the influence region 105^; determining a value of a first spatial parameter ^ for each measurement location pairthe value of the first spatial parameter ^ indicating a lateral distance between the first measurementlocation ^^,^and the second measurement location ^^,^of a respective measurement location pair determining a value of a second spatial parameter ^ for each measurement locationpair ^^^,^, ^^,^^, the value of the second spatial parameter ^ being a sum of a ground clearance of thefirst measurement location ^^,^ and a ground clearance of the second measurement location ^^,^ of arespective measurement location pair ^^^,^,grouping the measurement location pairs ^^^,^, ^^,^^ based on the value of their associated firstspatial parameter ^ and second spatial parameter ^, thereby determining a plurality of groupsofmeasurement location pairs ^^^,^,defining groups ^^,^ of magnetic field measurement pairs ^^^^^,^,^^, ^^^^,^,^^^, each magneticfield measurement pair^^^^,^,^^^ corresponding to a respective measurement location pair^^,^^ and being defined based on the corresponding measurement location pairandcalculating a variance ^^^^^^,^^ of each group ^^,^ of magnetic field measurementthe empirical covariance data ^^ comprising the calculated varianceof magnetic field measurement pairs82. The method (300) of claim 81, wherein each group ^^ of measurement location pairsisassociated with a respective first group threshold ^^ ^ and a respective second group threshold ^^^^^.

83. The method (300) of claim 81 or claim 82, wherein each group ^^ of measurement location pairscomprises:measurement location pairswith first spatial parameter ^ values that are between thefirst group thresholdand the second group thresholdof that group ^^; andmeasurement location pairswith second spatial parameter ^ values that are betweenthe first group thresholdand the second group thresholdof that group ^^.

84. The method (300) of any one of claims 81 to 83, wherein calculating the varianceof aparticular group ^^,^ of magnetic field measurement pairscomprises calculating:where:is the variance of a group ^^,^ of magnetic field measurementpairs^^,^ is the particular group ^^,^ of magnetic field measurement pairsof acalculation; ^and ^ are indices indicating the measurement locations^^,^,^ at which the magnetic fieldmeasurements ^^^^,^,^^, ^^^^,^,^^ of a magnetic field measurement pairwereobtained; ^is an expectation value function;^^is the set of all measurement locations in the influence region 105^;^^,^,^is a first measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^; and^^,^,^is a second measurement location ^^,^of a magnetic field measurement pair85. The method (300) of any one of claims 73 to 84, wherein the covariance function ^ is a planarlogarithmic covariance function.

86. The method (300) of any one of claims 73 to 85, wherein determining the target magnetic fielddata comprises computing a Hilbert space vector ^ by solving a system of linear equations thatrelate the empirical magnetic field data ^(^^) to the Hilbert space vector ^ and an auto covariance^^^(^^, ^^) between measurement locations ^^,^.

87. The method (300) of any one of claims 73 to 86, wherein determining the target magnetic fielddata comprises computing an auto covariance matrix ^^^(^^, ^^), using the covariancefunction ^.

88. The method (300) of claim 87, wherein computing the auto covariance matrix ^^^(^^, ^^)comprises computing:

89. The method (300) of any one of claims 73 to 88, wherein determining the target magnetic fielddata comprises determining a target cross covariance matrix ^^^^^^,^, ^^^, the target crosscovariance matrix^^^ indicating a covariance between the magnetic field characteristic at thetarget locationsand the measurement locations ^^,^.

90. The method (300) of claim 89, wherein the target cross covariance matrix ^^^^^^,^, ^^^ isdetermined using the covariance function ^.

91. The method (300) of claim 89 or claim 90, wherein determining the target cross covariance matrix^^^^^^,^, ^^^ comprises calculating:

92. The method (300) of any one of claims 89 to 91, when dependent on claim 86, wherein the method(300) comprises determining the target magnetic field data ^^^^,^ ^ using the target cross covariancematrix ^^^^^^,^,and the Hilbert space vector ^.

93. The method (300) of claim 74, further comprising:defining a plurality of target regions ^^ at the target ground clearance; anditeratively: selecting a target region ^^ of the plurality of target regions ^^;defining the influence region 105^ around the target region ^^, the influence region 105^being a three-dimensional region that is bound by one or more influence region boundary 109^; determining the empirical covariance data ^^based on the empirical magnetic fielddata ^(^^);determining the optimised value of one or more parameter of the covariance function ^ byfitting the covariance function ^ to the empirical covariance data ^^; anddetermining target magnetic field datathe target magnetic field databeing indicative of the magnetic field characteristic of the local magnetic field at one or more targetlocation ^^,^ on the selected target region ^^;for each target region ^^.

94. The method (300) of any one of claims 73 to 93, further comprising constructing a model ^ of themagnetic field characteristic at the target ground clearance, across the survey area (103), using the targetmagnetic field data ^^^^,^^.

95. A system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at leastone processor (104), cause the at least one processor (104) to perform the method (300) of any one ofclaims 73 to 94.

96. A system (100) comprising:at least one processor (104); andmemory (106) storing computer-executable instructions (108) that, when executed by the at leastone processor (104), cause the at least one processor (104) to:select empirical magnetic field data ^(^^) that is associated with an influence region 105^of a survey area (103); determine empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determine an optimised value of one or more parameter of a covariance function ^ by fittingthe covariance function ^ to the empirical covariance data ^^, the covariance function ^ defining acovariance between a magnetic field characteristic of a local magnetic field and locations ^ within theinfluence region 105^ ; anddetermine target magnetic field datathat is indicative of the magnetic fieldcharacteristic at one or more target locationusing the covariance function ^.

97. The system (100) of claim 96, wherein:the empirical magnetic field data ^(^^) comprises:a first set of magnetic field datathat is associated with a first flight path (123); anda second set of magnetic field data ^^^^,^^ that is associated with a second flight path (125) that isdifferent from the first flight path (123); and the computer-executable instructions (108), when executed by the at least one processor (104), arefurther configured to cause the at least one processor (104) to:define the one or more target locationat a target ground clearance that is different to atarget flight path ground clearance of the first flight path (123) and a target flight path ground clearance ofthe second flight path (125).

98. The system (100) of claim 96 or claim 97, wherein the computer-executable instructions (108),when executed by the at least one processor (104), are further configured to cause the at least oneprocessor (104) to define a plurality of target regions ^^ at the target ground clearance.

99. The system (100) of claim 98, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) toselect a target region ^^ of the plurality of target regions ^^, the one or more target locationbeingwithin the selected target region ^^.

100. The system (100) of claim 99, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) todefine the influence region 105^ around the target region ^^, the influence region 105^ being a three-dimensional region that is bound by one or more influence region boundary 109^.

101. The system (100) of claim 99 or claim 100, wherein a lateral dimension of the influenceregion 105^ is greater than a lateral dimension of the target region ^^.

102. The system (100) of any one of claims 95 to 101, wherein the selected empirical magnetic fielddata ^(^^) is empirical magnetic field data ^(^^) obtained at measurement locations ^^,^ within theinfluence region 105^ .

103. The system (100) of any one of claims 95 to 102, wherein the empirical magnetic field data ^(^^)comprises at least one of: a magnetic field intensity value |^|; a horizontal gradient value |^|; a transverse horizontal gradient valueand ^^ a longitudinal horizontal gradient value ^ ^^ ^; associated with each measurement location ^^,^of a plurality of measurement locations ^^,^, each measurement location ^^,^being within the influence region 105^.

104. The system (100) of any one of claims 95 to 103, wherein determining the empirical covariancedata ^^comprises: defining a plurality of measurement location pairs ^^^,^, ^^,^^, each measurement location pair^^,^^ comprising a first measurement locationof the influence region 105^ and a secondmeasurement location ^^,^of the influence region 105^; determining a value of a first spatial parameter ^ for each measurement location pairthe value of the first spatial parameter ^ indicating a lateral distance between the first measurementlocation ^^,^and the second measurement location ^^,^of a respective measurement location pair^^^,^, ^^,^^;determining a value of a second spatial parameter ^ for each measurement locationpair ^^^,^, ^^,^^, the value of the second spatial parameter ^ being a sum of a ground clearance of thefirst measurement location ^^,^ and a ground clearance of the second measurement location ^^,^ of arespective measurement location pair ^^^,^,grouping the measurement location pairs ^^^,^, ^^,^^ based on the value of their associated firstspatial parameter ^ and second spatial parameter ^, thereby determining a plurality of groupsofmeasurement location pairs ^^^,^,defining groups ^^,^ of magnetic field measurement pairseach magneticfield measurement pair^^^^,^,^^^ corresponding to a respective measurement location pair^^^,^, ^^,^^ and being defined based on the corresponding measurement location pair andcalculating a variance ^^^^^^,^^ of each group ^^,^ of magnetic field measurementpairs^^^^,^,^^^, the empirical covariance data ^^ comprising the calculated varianceof each group ^^,^ of magnetic field measurement pairs^^^^,^,^^^.

105. The system (100) of claim 104, wherein each group ^^ of measurement location pairsis associated with a respective first group threshold ^^^ and a respective second group threshold ^^^^^.

106. The system (100) of claim 104 or claim 105, wherein each group ^^ of measurement location pairscomprises:measurement location pairswith first spatial parameter ^ values that are between thefirst group threshold ^^^ and the second group thresholdof that group ^^; andmeasurement location pwith second spatial parameter ^ values that are betweenthe first group threshold ^^^ and the second group thresholdof that group ^^.

107. The system (100) of any one of claims 104 to 106, wherein calculating the varianceof aparticular group ^ of magnetic field measurement pairscompriseswhere:is the variance of a group ^^,^ of magnetic fieldpairs^is the particular group ^^,^ of magnetic field measurement pairsofcalculation; ^and ^ are indices indicating the measurement locations, ^^,^,^ at which the magneticmeasurements ^^^ ^, ^^^^,^,^^ of a magnetic field measurement pairobtained; ^is an expectation value function;^ is the set of all measurement locationsin the influence region 105^^^,^,^is a first measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^; and^^,^,^is a second measurement location ^^,^of a magnetic field measurement pair^^^^^,^,^^, ^^^^,^,^^^.

108. The system (100) of any one of claims 95 to 107, wherein the covariance function ^ is a planarlogarithmic covariance function.

109. The system (100) of any one of claims 95 to 108, wherein determining the target magnetic fielddata comprises computing a Hilbert space vector ^ by solving a system of linear equations thatrelate the empirical magnetic field data ^(^^) to the Hilbert space vector ^ and an auto covariance^^^(^^, ^^) between measurement locations ^^,^.

110. The system (100) of any one of claims 95 to 109, wherein determining the target magnetic fielddata comprises computing an auto covariance matrix ^^^(^^, ^^), using the covariancefunction ^.

111. The system (100) of claim 110, wherein computing the auto covariance matrix ^^^(^^, ^^)comprises computing:

112. The system (100) of any one of claims 95 to 111, wherein determining the target magnetic fielddata comprises determining a target cross covariance matrix ^^^^^^,^, ^^^, the target crosscovariance matrix^^^ indicating a covariance between the magnetic field characteristic at thetarget locationsand the measurement locations ^^,^.

113. The system (100) of claim 112, wherein the target cross covariance matrix ^^^^^^,^, ^^^ isdetermined using the covariance function ^.

114. The system (100) of claim 112 or claim 113, wherein determining the target cross covariancematrix ^^^^^^,^, ^^^ comprises calculating:

115. The system (100) of any one of claims 112 to 114, when dependent on claim 109, wherein thecomputer-executable instructions (108), when executed by the at least one processor (104), are furtherconfigured to cause the at least one processor (104) to determine the target magnetic field datausing the target cross covariance matrix ^^^ and the Hilbert space vector ^.

116. The system (100) of claim 96, wherein the computer-executable instructions (108), when executedby the at least one processor (104), are further configured to cause the at least one processor (104) to:define a plurality of target regionsat the target ground clearance; anditeratively: select a target region ^^ of the plurality of target regions ^^;define the influence region 105^ around the target region ^^, the influence region 105^ being athree-dimensional region that is bound by one or more influence region boundary 109^; determine the empirical covariance data ^^ based on the empirical magnetic field data ^(^^);determine the optimised value of one or more parameter of the covariance function ^ by fitting thecovariance function ^ to the empirical covariance data ^^; anddetermine target magnetic field datathe target magnetic field databeingindicative of the magnetic field characteristic of the local magnetic field at one or more target location^ on the selected target region ^^;for each target region ^^.

117. The system (100) of any one of claims 95 to 116, wherein the computer-executableinstructions (108), when executed by the at least one processor (104), are further configured to cause theat least one processor (104) to construct a model ^ of the magnetic field characteristic at the targetground clearance, across the survey area (103), using the target magnetic field data118. The system (100) of claim 96, or any one of claims 97 to 117 when dependent on claim 96,wherein: the system (100) comprises:a first aerial system (122); anda second aerial system (142);the first flight path (123) is a flight path of the first aerial system (122); andthe second flight path (125) is a flight path of the second aerial system (142).

119. The system (100) of claim 118, wherein the first aerial system (122) propels the second aerialsystem (142).

120. The system (100) of claim 118 or claim 119, wherein:the first aerial system (122) comprises a first sensor system (124) that obtains the first set of magnetic field datathe second aerial system (142) comprises a second sensor system (144) that obtains the second setof magnetic field data ^^^^,^^.

121. The system (100) of claim 120, wherein the first sensor system (124) comprises at least one of:a magnetometer; a Global Navigation Satellite System module; aheight measurement system;an Inertial Measurement Unit; and a gradiometer.

122. The system (100) of claim 120 or claim 121, wherein the second sensor system (144) comprises atleast one of: a magnetometer; a Global Navigation Satellite System module; aheight measurement system;an Inertial Measurement Unit; and a gradiometer.

123. A non-transitory computer-readable storage medium storing instructions that, when executed by acomputer, cause the computer to perform the method (200) of any one of claims 1 to 33.

124. A non-transitory computer-readable storage medium storing instructions that, when executed by acomputer, cause the computer to perform the method (300) of any one of claims 73 to 94.

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