Method for obtaining precise aeromagnetic survey data based on analytical signal modeling method

Abstract

The application discloses a method for obtaining accurate aeromagnetic survey data based on an analytic signal modulus method, which comprises the following steps: obtaining the actual aeromagnetic survey data T of a test area; calculating the horizontal gradient in the x direction, the horizontal gradient in the y direction and the vertical gradient in the z direction according to the aeromagnetic survey data T; calculating the original analytic signal modulus ASM0 according to the horizontal gradient in the x direction of the aeromagnetic survey data T, the horizontal gradient in the y direction of the aeromagnetic survey data T and the vertical gradient in the z direction of the aeromagnetic survey data T; calculating the weak signal enhancement function B according to the horizontal gradient in the x direction of the aeromagnetic survey data T and the horizontal gradient in the y direction of the aeromagnetic survey data T; and obtaining the accurate aeromagnetic survey data according to the original analytic signal modulus ASM0 and the weak signal enhancement function B. The method for obtaining accurate aeromagnetic survey data based on the analytic signal modulus method can improve the lateral resolution of the geological body boundary, thereby improving the quality of the aeromagnetic data processing conversion and the geological body boundary recognition effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airborne magnetic survey, and particularly relates to a method for obtaining accurate airborne magnetic survey data based on an analytic signal module method. BACKGROUND

[0002] Airborne magnetic survey is to install an airborne magnetometer (such as an optical pumping type, a nuclear spin type and a fluxgate type) system on an aircraft, and to observe the geomagnetic field parameters (such as the total geomagnetic field intensity T or the total magnetic field anomaly ΔT or the gradient thereof) to find magnetic or magnetically related ore bodies, to understand geological structures, to conduct magnetic mapping, to solve urban and engineering stability and archaeological problems and the like.

[0003] Airborne magnetic survey data is a comprehensive reflection of the magnetic field information of magnetic geological bodies of different depths, different forms and different scales on an observation surface. However, due to the errors of the measurement data or the superposition of the magnetic field, the measurement data is difficult to distinguish, which brings difficulty to the geological interpretation work.

[0004] In order to improve the resolution capability of the airborne magnetic anomaly and highlight more useful information, the analytic signal module method can be used to identify the edge of the magnetic body. In the existing technology, the analytic signal module method has the advantages of being least affected by the magnetic anomaly component and the magnetization direction, but has the disadvantage of low lateral resolution capability of the identification result, which affects the actual application effect.

[0005] Therefore, in order to improve the quality of the airborne magnetic data processing and conversion and the effect of the geological body boundary identification, it is necessary to study and improve the analytic signal module method in the actual detection of the airborne magnetic data geological body boundary capability technology.

[0006] The information disclosed in this BACKGROUND section is only for the purpose of increasing the understanding of the background of the present application and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art that is publicly known. SUMMARY

[0007] To solve the above problems, the purpose of the embodiment of the present application is to provide a method for obtaining accurate airborne magnetic survey data based on an analytic signal module method, so as to improve the lateral resolution capability of the geological body boundary, thereby improving the quality of the airborne magnetic data processing and conversion and the effect of the geological body boundary identification.

[0008] To achieve the above purpose, the present application provides a method for obtaining accurate airborne magnetic survey data based on an analytic signal module method, comprising:

[0009] obtaining the actually measured airborne magnetic survey data T of a test area; calculating the horizontal gradient of the airborne magnetic survey data T in the x direction the horizontal gradient of the airborne magnetic survey data T in the y direction and the vertical gradient of the airborne magnetic survey data T in the z direction calculating horizontal gradient of the aerial magnetic survey data T in x direction calculating horizontal gradient of the aerial magnetic survey data T in y direction calculating vertical gradient of the aerial magnetic survey data T in z direction calculating original analytic signal module ASM0; calculating horizontal gradient of the aerial magnetic survey data T in x direction calculating horizontal gradient of the aerial magnetic survey data T in y direction calculating weak signal enhancement function B; obtaining accurate aerial magnetic survey data according to original analytic signal module ASM0 and weak signal enhancement function B.

[0010] In an embodiment, before the calculating horizontal gradient of the aerial magnetic survey data T in x direction calculating horizontal gradient of the aerial magnetic survey data T in y direction calculating vertical gradient of the aerial magnetic survey data T in z direction the method further comprises: pre-processing the obtained aerial magnetic survey data T of the test area.

[0011] In an embodiment, the pre-processing comprises coordinate conversion, normal field correction, daily variation correction, lag correction and magnetic field horizontal adjustment.

[0012] In an embodiment, the calculating horizontal gradient of the aerial magnetic survey data T in x direction calculating horizontal gradient of the aerial magnetic survey data T in y direction calculating vertical gradient of the aerial magnetic survey data T in z direction comprises: calling geological body feature distribution data of the test area through network; evaluating the test area according to the geological body feature distribution data, and hierarchically decomposing the test area according to the evaluation result to obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity average value identifiers; referring to the M hierarchical scale region decomposed pre-processed aerial magnetic survey data T to obtain M hierarchical magnetic survey data t; determining horizontal gradient of the M hierarchical magnetic survey data t in x direction M horizontal gradient of the M hierarchical magnetic survey data t in y direction M horizontal gradient of the M hierarchical magnetic survey data t in z direction M weighting and fusing the M hierarchical complexity average values in x direction horizontal gradient M outputting horizontal gradient in x direction weighting and fusing the M hierarchical complexity average values in y direction horizontal gradient M outputting horizontal gradient in y direction According to the M-level complexity average weighted fusion in the y direction horizontal gradient M Output in the z direction horizontal gradient

[0013] In an embodiment, the test area is evaluated according to the geological body feature distribution data, and the test area is hierarchically decomposed according to the evaluation result to obtain M hierarchical scale regions, which includes: after the test area is segmented into a plurality of grid regions based on a preset grid scale, the geological body feature distribution data is segmented using the plurality of grid regions to obtain a plurality of grid geological body feature data; terrain complexity evaluation is performed according to the plurality of grid geological body feature data, and a plurality of regional terrain complexities of the plurality of grid regions are output; a preset complexity threshold is set, and a plurality of rounds of merging of the plurality of grid regions are mapped according to whether the adjacent difference values of the plurality of regional terrain complexities satisfy the complexity threshold, to obtain the M hierarchical scale regions.

[0014] In an embodiment, the accurate aeromagnetic survey data is obtained according to the original analytical signal module ASM0 and the weak signal enhancement function B, which includes: the accurate aeromagnetic survey data is obtained according to the following formula, which includes:

[0015]

[0016] wherein, Max(ASM0) represents the maximum value of the analytical signal module ASM0, T is the measured aeromagnetic survey data, x, y and z are three directions of the spatial coordinates, and Δ is an adjustment parameter, and the value of Δ is 0-1.

[0017] In an embodiment, it also includes: according to the M-level complexity average weighted fusion in the x direction horizontal gradient M The first average and the first square deviation are calculated and output; the M-level complexity average weighted fusion in the x direction horizontal gradient M The first horizontal extreme value is extracted; in this way, the data processing of the M-level complexity average weighted fusion in the y direction horizontal gradient M The second average, the second square deviation and the second horizontal extreme value are output; in this way, the data processing of the M-level complexity average weighted fusion in the z direction horizontal gradient M The third average, the third square deviation and the third horizontal extreme value are output; after the average burial depth of the test area is obtained, the average burial depth, the first average, the first square deviation, the first horizontal extreme value, the second average, the second square deviation and the second horizontal extreme value, the third average, the third square deviation and the third horizontal extreme value are input into a pre-constructed adjustment parameter disturbance model for disturbance analysis, and real-time adjustment parameters are output.

[0018] In one implementation, the method further includes: connecting to a network to call multiple sets of sample perturbation variables and multiple sample adjustment parameters, wherein each set of sample perturbation variables includes sample burial depth, first sample mean, first sample variance, first sample extreme value, second sample mean, second sample variance, second sample extreme value, third sample mean, third sample variance, and third sample extreme value; storing the multiple sets of sample perturbation variables and multiple sample adjustment parameters based on a knowledge graph to complete the construction of the adjustment parameter perturbation model; inputting the average burial depth, first water average value, first horizontal variance, first horizontal extreme value, second water average value, second horizontal variance, second horizontal extreme value, third water average value, third horizontal variance, and third horizontal extreme value into the adjustment parameter perturbation model; evaluating the data similarity of the multiple sets of sample perturbation variables based on Euclidean distance ratio; and extracting the sample adjustment parameters corresponding to the similarity extreme values ​​as real-time adjustment parameters.

[0019] To achieve the above objectives, the present invention also provides an apparatus for acquiring accurate airborne magnetic measurement data based on the analytical signal mode method, comprising: an acquisition module for acquiring measured airborne magnetic measurement data T in a test area; and a gradient calculation module for calculating the horizontal gradient of the airborne magnetic measurement data T in the x-direction. The horizontal gradient of the airborne magnetic measurement data T in the y direction and the vertical gradient of the airborne magnetic measurement data T in the z-direction The original analytical signal modulus calculation module is used to calculate the horizontal gradient of the airborne magnetic measurement data T in the x-direction. The horizontal gradient of the airborne magnetic measurement data T in the y direction and the vertical gradient of the airborne magnetic measurement data T in the z-direction The original analytical signal modulus ASM0 is calculated; the weak signal enhancement function calculation module is used to calculate the horizontal gradient of the airborne magnetic measurement data T in the x-direction. The horizontal gradient of the airborne magnetic measurement data T in the y direction The module calculates the weak signal enhancement function B; the precise airborne magnetic survey data acquisition module is used to acquire precise airborne magnetic survey data based on the original analytical signal modulus ASM0 and the weak signal enhancement function B.

[0020] To achieve the above objectives, the present invention also provides a storage medium storing computer-executable instructions for executing the method for acquiring accurate airborne magnetic measurement data based on the analytical signal mode method described above.

[0021] Compared with the prior art, the method, device and storage medium for acquiring precise aeromagnetic survey data based on the analytic signal modulus method according to the present application can better detect the boundary of the aeromagnetic data target geological body, make the boundary identification result more convergent, solve the problem of low resolution of the prior analytic signal modulus method, and thus improve the quality of aeromagnetic data processing conversion and the boundary identification effect of the geological body. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 A flow chart of the method for acquiring precise aeromagnetic survey data based on the analytic signal modulus method provided by the embodiment of the present application is shown;

[0024] Figure 2 A structural schematic diagram of the device for acquiring precise aeromagnetic survey data based on the analytic signal modulus method provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] The specific embodiments of the present application will be described in detail below with reference to the drawings, but it should be understood that the protection scope of the present application is not limited by the specific embodiments.

[0026] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0027] Unless otherwise explicitly indicated, in the entire specification and claims, the term "comprise" or its variants such as "contain" or "include" and the like will be understood to include the stated element or component, without excluding other elements or components.

[0028] In addition, the terms "first", "second", etc. are used only for descriptive purposes and are not to be construed as indicating or implying relative importance or an indicated number of technical features. Thus, features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality" is two or more, unless otherwise explicitly and specifically limited.

[0029] The embodiment of the present application provides a flowchart of a method for obtaining accurate aeromagnetic survey data based on signal analysis method, referring to Figure 1 , comprising steps S1-S4.

[0030] Step 1, obtaining the aeromagnetic survey data T actually measured in the test area.

[0031] Wherein, the test area is analyzed for geological body characteristics such as burial depth, scale, structural complexity, etc. to divide the test area according to terrain complexity, obtain the distribution of terrain complexity, and further optimize the flight trajectory based on the terrain complexity distribution to generate a dynamic flight trajectory, and further collect original magnetic survey data based on the dynamic flight trajectory. Wherein, the spacing and flight height of the flight curve change with the terrain complexity, and the aeromagnetic survey data T is the additional magnetic field generated by the iron-containing magnetic geological body in the crust under the action of the geomagnetic field.

[0032] In an implementation manner, before step S2, after step S1, the method can further include: preprocessing the obtained aeromagnetic survey data T actually measured in the test area to obtain the aeromagnetic survey data T actually measured in the test area after preprocessing. The preprocessing includes one or more of coordinate conversion, normal field correction, diurnal variation correction, lag correction and magnetic field level adjustment. Through preprocessing, the noise and error in the aeromagnetic survey data caused by various factors can be eliminated.

[0033] In an implementation manner, the selection order of preprocessing can be, for example, coordinate conversion→normal field correction→diurnal variation correction→lag correction.

[0034] Further, the above methods can be used in combination, such as coordinate conversion and diurnal variation correction are simultaneously processed by configuring two containers, and then the registered spatial data and the time-corrected magnetic field data are time-spatially synchronized to unify the time stamp and position label. Through parallel processing of specific preprocessing steps, and through result alignment superposition to improve efficiency.

[0035] Step 2, calculating the horizontal gradient of the aeromagnetic survey data T in the x direction The horizontal gradient of the aeromagnetic survey data T in the y direction And the vertical gradient of the aeromagnetic survey data T in the z direction

[0036] In an implementation manner, the step S2 can further include:

[0037] According to the correspondence between the two-dimensional Fourier transform in the frequency domain and the Hilbert transform, the horizontal gradient of the preprocessed airborne magnetic survey data T in the x direction The horizontal gradient of the preprocessed airborne magnetic survey data T in the y direction And the vertical gradient of the z direction

[0038] The correspondence between the two-dimensional Fourier transform in the frequency domain and the Hilbert transform can include:

[0039]

[0040] Specifically, the two-dimensional Hilbert transform, the Fourier domain transform factor is:

[0041] DH(u,v)=-i·sign(u,v),

[0042]

[0043] Where i 2 =-1, u, v are the circular wave numbers in the x and y directions. The components of the Hilbert transform in the x and y directions can be expressed as:

[0044]

[0045] Where F[], F -1 [] represent the Fourier forward transform and inverse transform respectively; H x And H y represent the components of the two-dimensional Hilbert transform in the x and y directions.

[0046] Therefore, the horizontal gradient of the x direction The horizontal gradient of the y direction And the vertical gradient of the z direction The Hilbert transform can be used to calculate the equivalence relationship between them:

[0047]

[0048] Using the correspondence between the Hilbert and Fourier transforms, the Hilbert transform is directly used to replace the Fourier transform to calculate the three-direction gradients of x, y and z. The above equivalence relationship is used, that is, the Hilbert transform is used to replace the Fourier transform, and the advantage is that the direct Hilbert transform will not amplify the noise interference in the airborne magnetic survey data.

[0049] In an implementation manner, the step S2 can further include:

[0050] obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity average identifiers; reference the M hierarchical scale regions to decompose the preprocessed aerial magnetic survey data T to obtain M hierarchical magnetic survey data t; determine, according to a corresponding relationship between two-dimensional Fourier transform and Hilbert transform in a frequency domain, M M hierarchical horizontal gradients in a y direction M hierarchical horizontal gradients in a z direction M hierarchical horizontal gradients in the x direction output the horizontal gradient in the x direction M hierarchical horizontal gradients in the y direction output the horizontal gradient in the Y direction M hierarchical horizontal gradients in the y direction output the horizontal gradient in the z direction

[0051] In an implementation manner, the evaluating the test region according to the geologic body feature distribution data and hierarchically decomposing the test region according to an evaluation result to obtain M hierarchical scale regions comprises:

[0052] After the test region is segmented into a plurality of grid regions based on a preset grid scale, the geologic body feature distribution data is segmented by using the plurality of grid regions to obtain a plurality of grid geologic body feature data; terrain complexity is evaluated according to the plurality of grid geologic body feature data, and a plurality of regional terrain complexities of the plurality of grid regions are output; a preset complexity threshold is set, and whether an adjacent difference value of the plurality of regional terrain complexities satisfies the complexity threshold is mapped to perform a plurality of rounds of merging of the plurality of grid regions to obtain the M hierarchical scale regions.

[0053] It should be understood that, in a complex geological structure scene, gradient calculation of a single scale may have a problem that gradient values of a strong magnetic anomaly region are too high, resulting in deep weak gradients being ignored. Based on this, the embodiment performs multi-scale decomposition on the magnetic survey data to obtain geologic body signals corresponding to different buried depths, then performs the above horizontal gradient calculation on each scale layer to obtain horizontal gradients of each scale layer, and further performs multi-scale gradient fusion by using weight superposition.

[0054] Specifically, in the present embodiment, geological body feature data covering the test region is called from a geological information platform, the geological body feature data including but not limited to geological structure density information, lithology distribution information, burial depth feature information and historical geomagnetic data, and the geological body feature distribution data provides a basic geological background support for subsequent regional evaluation and hierarchical decomposition.

[0055] The test region is divided into a plurality of grid regions according to a preset grid scale (such as 1 km x 1 km), and then the obtained geological body feature distribution data is segmented according to the grid region division, to obtain a plurality of grid geological body feature data.

[0056] The terrain complexity of each grid is calculated by inputting the plurality of grid geological body feature data into a quantitative model, the evaluation indexes of the quantitative model including structure density (based on the distribution density of fault zones or folds), lithology complexity (reflecting the distribution heterogeneity of different lithologies), burial depth coefficient of variation (characterizing the degree of spatial variation of the burial depth of geological bodies), and magnetic anomaly gradient (combining historical geomagnetic data to calculate the first derivative of the magnetic field strength, describing the boundary characteristics of the magnetic body).

[0057] A complexity threshold is set, and the complexity threshold is used as a judgment condition for whether adjacent grid regions can be merged, if the complexity difference of adjacent grids is lower than the threshold, they are merged into a region of the same hierarchical scale; otherwise, they are kept as independent regions. Through multiple rounds of merging algorithm, the adjacent grid regions are dynamically iteratively adjusted, and finally M hierarchical scale regions are obtained.

[0058] According to the grid region composition of each hierarchical scale region, the corresponding terrain complexity is extracted for mean value calculation and marked on the corresponding hierarchical scale region.

[0059] Further, the M hierarchical scale region decomposition pretreated aerial magnetic survey data T is obtained, according to the corresponding relationship between the two-dimensional Fourier transform and Hilbert transform in the frequency domain, the M horizontal gradient M in the y direction, and the M horizontal gradient M in the z direction. The x direction horizontal gradient is output. The y direction horizontal gradient is output.

[0060] In step, according to the correspondence between the two-dimensional Fourier transform in frequency domain and Hilbert transform, the M-level magnetic survey data t in the x direction horizontal gradient M In the y direction horizontal gradient M And in the z direction horizontal gradient M Among them, the correspondence between the two-dimensional Fourier transform in frequency domain and Hilbert transform can include:

[0061]

[0062] Then, according to the M-level complexity average weighted fusion in x direction horizontal gradient M Output x direction horizontal gradient According to the M-level complexity average weighted fusion in y direction horizontal gradient M Output y direction horizontal gradient According to the M-level complexity average weighted fusion in z direction horizontal gradient M Output y direction horizontal gradient

[0063] Step 3, according to the x direction horizontal gradient of the aerial magnetic survey data T And the y direction horizontal gradient of the aerial magnetic survey data T Calculate the original total horizontal gradient H xy .

[0064] Among them, the horizontal gradient It can be the x direction horizontal gradient M Output after the M-level complexity average weighted fusion in x direction The horizontal gradient It can be the y direction horizontal gradient M Output after the M-level complexity average weighted fusion in y direction

[0065] This embodiment is based on the M-level complexity average to calculate the M-level weight of the scale area, which is used for weighted fusion to calculate the x direction horizontal gradient And Y direction horizontal gradient

[0066] Step 3, according to the x direction horizontal gradient of the aerial magnetic survey data T The y direction horizontal gradient of the aerial magnetic survey data T And the z direction vertical gradient of the aerial magnetic survey data T Calculate the original analytical signal module ASM0.

[0067] The horizontal gradient in the x direction can be obtained by averaging and weighting the M horizontal gradients in the x direction according to the M level complexity values. The horizontal gradient in the y direction can be obtained by averaging and weighting the M horizontal gradients in the y direction according to the M level complexity values. The horizontal gradient in the x direction outputted later can be obtained by averaging and weighting the M horizontal gradients in the x direction according to the M level complexity values. The horizontal gradient in the y direction outputted later can be obtained by averaging and weighting the M horizontal gradients in the y direction according to the M level complexity values. The horizontal gradient in the z direction can be obtained by averaging and weighting the M horizontal gradients in the z direction according to the M level complexity values. The horizontal gradient in the z direction outputted later can be obtained by averaging and weighting the M horizontal gradients in the z direction according to the M level complexity values. The horizontal gradient in the x direction can be obtained by averaging and weighting the M horizontal gradients in the x direction according to the M level complexity values. The horizontal gradient in the y direction can be obtained by averaging and weighting the M horizontal gradients in the y direction according to the M level complexity values. The horizontal gradient in the z direction can be obtained by averaging and weighting the M horizontal gradients in the z direction according to the M level complexity values.

[0068] The M weights of the M level scale regions are calculated based on the M level complexity values, and are used for averaging and weighting the M horizontal gradients in the x direction, the M horizontal gradients in the y direction, and the M horizontal gradients in the z direction. The M weights of the M level scale regions are calculated based on the M level complexity values, and are used for averaging and weighting the M horizontal gradients in the x direction, the M horizontal gradients in the y direction, and the M horizontal gradients in the z direction. The M weights of the M level scale regions are calculated based on the M level complexity values, and are used for averaging and weighting the M horizontal gradients in the x direction, the M horizontal gradients in the y direction, and the M horizontal gradients in the z direction.

[0069] Specifically, the original analytic signal module ASM0 can be calculated by the following formula, which includes:

[0070]

[0071] wherein, Txis the horizontal gradient in the x direction of the aerial magnetic survey data T, Txis the horizontal gradient in the x direction of the aerial magnetic survey data T, Tyis the horizontal gradient in the y direction of the aerial magnetic survey data T, Tzis the vertical gradient in the z direction of the aerial magnetic survey data T.

[0072] Step 4, calculating the weak signal enhancement function B according to the horizontal gradient in the x direction of the aerial magnetic survey data T, the horizontal gradient in the y direction of the aerial magnetic survey data T, the horizontal gradient in the z direction of the aerial magnetic survey data T.

[0073] Specifically, the weak signal enhancement function B can be calculated by the following formula, which includes:

[0074]

[0075] It should be noted that the original analytic signal module ASM0 and the weak signal enhancement function can be calculated according to the fused gradient, or can be directly calculated according to the gradient without fusion.

[0076] Step 5, obtaining the accurate aerial magnetic survey data according to the original analytic signal module ASM0 and the weak signal enhancement function B.

[0077] Specifically, the accurate aeromagnetic survey data can be obtained according to the following formula, which comprises:

[0078]

[0079] wherein, Max(ASM0) represents the maximum value of the analytical signal module ASM0, T represents the measured aeromagnetic survey data, x, y and z represent three directions of the spatial coordinates, and Δ represents an adjustment parameter, and the value of Δ is 0-1. By setting the adjustment parameter, the denominator is prevented from being 0, and the expression is prevented from appearing “analytical singular point”.

[0080] In step 5, the weighted fused gradient values obtained through the M hierarchical scale regions are used for calculation, so that the obtained magnetic survey data is more stable in a complex geological structure scene.

[0081] In an implementation manner, the adjustment parameter can be obtained in the following manner: according to the data processing of the M horizontal gradient in the x direction, the first average value and the first variance are calculated and output; the M horizontal gradient in the y direction is extracted in turn, and the second average value, the second variance and the second horizontal extreme value are output; and the M horizontal gradient in the z direction is extracted in turn, and the third average value, the third variance and the third horizontal extreme value are output. After the average burial depth of the test region is obtained in interaction, the average burial depth, the first average value, the first variance, the first horizontal extreme value, the second average value, the second variance, the second horizontal extreme value, the third average value, the third variance and the third horizontal extreme value are input into the pre-constructed adjustment parameter disturbance model for disturbance analysis, and the real-time adjustment parameter is output.

[0082] In an implementation manner, a plurality of groups of sample disturbance variables and a plurality of sample adjustment parameters are called in networking, wherein each group of sample disturbance variables comprises a sample burial depth, a first sample mean value, a first sample variance, a first sample extreme value, a second sample mean value, a second sample variance, a second sample extreme value, a third sample mean value, a third sample variance and a third sample extreme value; the plurality of groups of sample disturbance variables and the plurality of sample adjustment parameters are associatedly stored based on a knowledge graph, so as to complete construction of the adjustment parameter disturbance model; the average burial depth, the first average value, the first variance, the first horizontal extreme value, the second average value, the second variance, the second horizontal extreme value, the third average value, the third variance and the third horizontal extreme value are input into the adjustment parameter disturbance model, the plurality of groups of sample disturbance variables are evaluated in data similarity based on the Euclidean distance, and the sample adjustment parameter corresponding to the extreme value of the similarity is extracted as the real-time adjustment parameter.

[0083] Therefore, the method for obtaining accurate aeromagnetic data based on the analytic signal modulus method can better detect the boundary of the aeromagnetic data target geological body, and the boundary identification result is more convergent, the resolution of the existing analytic signal modulus method is improved, and the quality of the aeromagnetic data processing and conversion and the boundary identification effect of the geological body are improved. In addition, the false aeromagnetic data geological body boundary interference is eliminated, the signal-to-noise ratio is enhanced, and the boundary position enhancement and extraction capability of the geological body with different depths is improved, and the resolution and accuracy are higher.

[0084] Therefore, the boundary position enhancement and extraction capability of the geological body with different depths is improved, and the resolution and accuracy are higher.

[0085] The analytic signal modulus method expression provided in the embodiment is:

[0086]

[0087] Compared with the existing analytic signal modulus method ASM0 expression:

[0088]

[0089] The analytic signal modulus method expression provided in the embodiment is that, in view of the characteristics that the analytic signal modulus method is insufficient in boundary enhancement of a large depth field source, the maximum value of the analytic signal modulus method is used to normalize ASM0·B, the purpose is to limit the amplitude of the aeromagnetic anomaly with large difference in strength and weakness within a certain range, balance the strong and weak aeromagnetic anomalies, avoid distortion caused by too strong or too weak magnetic anomaly signals, and improve the stability and noise resistance of the magnetic anomaly signals.

[0090] By introducing the weak signal enhancement function B and the Hilbert transform, a reasonable equalization filter is established, and the analytic signal modulus method is redefined. The boundary of the aeromagnetic data target geological body can be better detected, the boundary identification result is more convergent, the resolution of the existing analytic signal modulus method is improved, and the boundary position enhancement and extraction capability of the geological body with different depths is improved, and the resolution and accuracy are higher.

[0091] The position of the aeromagnetic data geological target body is obtained by using the analytic signal modulus method provided in the embodiment, so as to further accurately infer the boundary, depth, occurrence, scale, field distribution rule and physical properties of the structure field source, which is of great significance for dividing tectonic units, conducting tectonic zoning, determining the position of the fault structure zone, distinguishing the distribution of different lithology and strata, and conducting physical property mapping.

[0092] The embodiment of the present application also provides a device for obtaining accurate aeromagnetic survey data based on an analytic signal model, which comprises an obtaining module 1, a gradient calculation module 2, an original analytic signal model calculation module 3, a weak signal enhancement function calculation module 4 and an accurate aeromagnetic survey data obtaining module 5.

[0093] Specifically, the obtaining module 1 is used for obtaining the aeromagnetic survey data T actually measured in a test area. The gradient calculation module 2 is used for calculating the horizontal gradient of the aeromagnetic survey data T in the x direction the horizontal gradient of the aeromagnetic survey data T in the y direction and the vertical gradient of the aeromagnetic survey data T in the z direction The original analytic signal model calculation module 3 is used for calculating the original analytic signal model ASM0 according to the horizontal gradient of the aeromagnetic survey data T in the x direction the horizontal gradient of the aeromagnetic survey data T in the y direction and the vertical gradient of the aeromagnetic survey data T in the z direction The weak signal enhancement function calculation module 4 is used for calculating the weak signal enhancement function B according to the horizontal gradient of the aeromagnetic survey data T in the x direction the horizontal gradient of the aeromagnetic survey data T in the y direction The accurate aeromagnetic survey data obtaining module 5 is used for obtaining the accurate aeromagnetic survey data according to the original analytic signal model ASM0 and the weak signal enhancement function B.

[0094] The embodiment of the present application also provides a storage medium, which stores computer executable instructions, and the computer executable instructions contain programs for executing the method for obtaining accurate aeromagnetic survey data based on an analytic signal model.

[0095] Therefore, the boundary of the aeromagnetic data target geological body can be better detected, the boundary identification result is more convergent, the problem of low resolution of the existing analytic signal model is solved, and thus the quality of aeromagnetic data processing conversion and the geological body boundary identification effect are improved.

[0096] The storage medium can be any available medium or data storage device accessible by a computer, including but not limited to a magnetic storage (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO) and the like), an optical storage (such as a CD, a DVD, a BD, a HVD and the like), and a semiconductor storage (such as a ROM, an EPROM, an EEPROM, a non-volatile memory (NAND FLASH), a solid state disk (SSD)) and the like.

[0097] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for obtaining accurate aeromagnetic data based on analytical signal modeling, characterized in that, The method comprises: acquiring the measured airborne magnetic survey data T of the test area; calculating the horizontal gradient of the aeromagnetic data T in the x direction calculating the horizontal gradient of the aeromagnetic data T in the y direction calculating the vertical gradient of the aeromagnetic data T in the z direction According to the horizontal gradient of the aerial magnetic survey data T in the x direction According to the horizontal gradient of the aerial magnetic survey data T in the y direction According to the vertical gradient of the aerial magnetic survey data T in the z direction Calculate the original analytical signal module ASM0; According to the aerial magnetic survey data T in the x direction horizontal gradient The aerial magnetic survey data T in the y direction horizontal gradient Calculate the weak signal enhancement function B; acquiring the accurate airborne magnetic survey data according to the original analytical signal model ASM0 and the weak signal enhancement function B; wherein the weak signal enhancement function B is calculated by the following formula, which comprises: wherein the acquiring the accurate airborne magnetic survey data according to the original analytical signal model ASM0 and the weak signal enhancement function B comprises: acquiring the accurate airborne magnetic survey data according to the following formula, which comprises: wherein, Max(ASM0) represents the maximum value of the original analytical signal module ASM0, ASM is the analytical signal module, T is the measured airborne magnetic survey data, x, y, and z are the three directions of the spatial coordinates, and Δ is an adjustment parameter, and the value of Δ is 0-1.

2. The method of claim 1, wherein, after the acquiring the airborne magnetic survey data T actually measured in the test area, the calculating the horizontal gradient of the airborne magnetic survey data T in x direction the horizontal gradient of the airborne magnetic survey data T in y direction and the vertical gradient of the airborne magnetic survey data T in z direction before the acquiring the airborne magnetic survey data T actually measured in the test area, the method further comprises: preprocessing the acquired measured airborne magnetic survey data T of the test area.

3. The method of claim 2, wherein, The preprocessing comprises one or more of coordinate conversion, normal field correction, daily variation correction, lag correction and magnetic field level adjustment.

4. The method of claim 2, wherein, said computing a horizontal gradient of the airborne magnetic survey data T in the x-direction said computing a horizontal gradient of the airborne magnetic survey data T in the y-direction and said computing a vertical gradient of the airborne magnetic survey data T in the z-direction comprising: networking calling the geological body feature distribution data of the test area; evaluating the test area according to the geological body feature distribution data, and hierarchically decomposing the test area according to the evaluation result to obtain M hierarchical scale areas, wherein the M hierarchical scale areas have M hierarchical complexity average identifiers; referencing the M hierarchical scale area decomposed preprocessed airborne magnetic survey data T to obtain M hierarchical magnetic survey data t; According to the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the M-level magnetic measurement data t in the x direction horizontal gradient M In the y direction horizontal gradient M And in the z direction horizontal gradient M According to the M-level complexity mean weighted fusion, M Output in the x-direction horizontal gradient According to the M-level complexity mean weighted fusion in the y direction horizontal gradient M Output in the y direction horizontal gradient According to the M-level complexity mean weighted fusion, M Output in the z-direction horizontal gradient 5. The method of claim 4, wherein, the evaluating the test area according to the geological body feature distribution data, and hierarchically decomposing the test area according to the evaluation result to obtain M hierarchical scale areas comprises: after segmenting the test area into a plurality of grid areas based on a preset grid scale, segmenting the geological body feature distribution data using the plurality of grid areas to obtain a plurality of grid geological body feature data; performing terrain complexity evaluation according to the plurality of grid geological body feature data, and outputting a plurality of regional terrain complexities of the plurality of grid areas; presetting a complexity threshold, and according to whether the adjacent difference values of the plurality of regional terrain complexities satisfy the complexity threshold, performing a plurality of rounds of merging of the plurality of grid areas to obtain the M hierarchical scale areas.

6. The method of claim 4, wherein, Further comprising: According to the horizontal gradient M in the x direction calculating an output first mean and a first variance; arranged in ascending order in the x-direction horizontal gradient M to extract the first horizontal extreme value; Similarly, the data processing in the y direction horizontal gradient M is carried out The second horizontal average, the second horizontal deviation and the second horizontal extreme value are output. Similarly, the data processing in the z direction horizontal gradient M is carried out The third horizontal average, the third horizontal square difference and the third horizontal extreme value are output. after interactively acquiring the average buried depth of the test area, inputting the average buried depth, the first average, the first variance, the first horizontal extreme value, the second average, the second variance, the second horizontal extreme value, the third average, the third variance and the third horizontal extreme value into a pre-constructed adjustment parameter perturbation model to perform perturbation analysis, and outputting real-time adjustment parameters.

7. The method of claim 6, wherein, Further comprising: networking calling a plurality of groups of sample perturbation variables and a plurality of sample adjustment parameters, wherein each group of sample perturbation variables comprises a sample buried depth, a first sample average, a first sample variance, a first sample extreme value, a second sample average, a second sample variance, a second sample extreme value, a third sample average, a third sample variance and a third sample extreme value; based on the knowledge graph, associatively storing the plurality of groups of sample perturbation variables and the plurality of sample adjustment parameters to complete construction of the adjustment parameter perturbation model; The average burial depth, the first horizontal average, the first horizontal deviation, the first horizontal extreme value, the second horizontal average, the second horizontal deviation, the second horizontal extreme value, the third horizontal average, the third horizontal deviation and the third horizontal extreme value are input into the adjustment parameter disturbance model, the multiple sets of sample disturbance variables are evaluated in data similarity based on Euclidean distance ratio, and the sample adjustment parameters corresponding to the similarity extreme value are extracted as real-time adjustment parameters.

8. A device for acquiring accurate airborne magnetic measurement data based on the analytical signal mode method, characterized in that, Comprise: The acquisition module acquires the aerial magnetic survey data T measured in the test area; a gradient calculation module, configured to calculate a horizontal gradient of the airborne magnetic survey data T in x direction a horizontal gradient of the airborne magnetic survey data T in y direction and a vertical gradient of the airborne magnetic survey data T in z direction A primary analytic signal module is configured to calculate a primary analytic signal module ASM0 based on the airborne magnetic survey data T in an x-direction horizontal gradient the airborne magnetic survey data T in a y-direction horizontal gradient and the airborne magnetic survey data T in a z-direction vertical gradient the primary analytic signal module ASM0; a weak signal enhancement function calculation module, configured to calculate a weak signal enhancement function B according to the horizontal gradient of the airborne magnetic survey data T in the x direction the horizontal gradient of the airborne magnetic survey data T in the y direction calculate a weak signal enhancement function B; Wherein, the weak signal enhancement function B is calculated by the following formula, which includes: The accurate aerial magnetic survey data acquisition module is used for acquiring accurate aerial magnetic survey data according to the original analytical signal mode ASM0 and the weak signal enhancement function B; Wherein, the accurate aerial magnetic survey data is acquired according to the original analytical signal mode ASM0 and the weak signal enhancement function B, which includes: The accurate aerial magnetic survey data is acquired according to the following formula, which includes: wherein, Max(ASM0) represents the maximum value of ASM0, ASM is the analytical signal module, T is the measured airborne magnetic survey data, x, y, z are three directions of the spatial coordinates, and Δ is an adjustment parameter, the value of Δ being 0-1.

9. A storage medium, characterized by The storage medium stores computer executable instructions, and the computer executable instructions are used for executing the method for acquiring accurate aerial magnetic survey data based on the analytical signal mode method in any one of claims 1-7.

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