Method for obtaining precise aeromagnetic survey data based on total horizontal gradient method

Abstract

This invention discloses a method for obtaining accurate airborne magnetic survey data based on the total horizontal gradient method, comprising: acquiring measured airborne magnetic survey data T in a test area; calculating the horizontal gradient of the airborne magnetic survey data T in the x-direction and the horizontal gradient of the airborne magnetic survey data T in the y-direction; and calculating the original total horizontal gradient H based on the horizontal gradient of the airborne magnetic survey data T in the x-direction and the horizontal gradient of the airborne magnetic survey data T in the y-direction. xy According to the original total horizontal gradient H xy Calculate the weak signal enhancement function B; based on the original total horizontal gradient H... xy The method for obtaining accurate airborne magnetic survey data based on the total horizontal gradient method, provided by this invention, can improve the boundary resolution of deeper geological bodies, thereby improving the quality of airborne magnetic data processing and conversion, and the effectiveness of geological body boundary identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of airborne magnetic survey, and particularly relates to a method for obtaining precise airborne magnetic survey data based on total horizontal gradient 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 find a magnetic or magnetic-related ore body by observing a geomagnetic field parameter (such as total geomagnetic field intensity T or total magnetic field anomaly ΔT or a gradient thereof), so as to understand geological structure, conduct magnetic mapping, solve urban and engineering stability and archaeology and the like.

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

[0004] At present, the resolution capability of the airborne magnetic anomaly can be improved by using the total horizontal gradient method NSTD.

[0005] The total horizontal gradient method has the advantage that, when detecting the boundary of a geological body of the airborne magnetic data, the influence of the magnetization direction and the magnetic anomaly component is smaller than that of the vertical derivative processing result. However, the method has the disadvantage that small-scale linear structures are easily covered by large structures and cannot be identified. In other words, the total horizontal gradient method can only give the boundary of a shallow geological body, and has low resolution for the boundary of a deep geological body, and is easy to produce a false boundary of a geological body of the airborne magnetic data, which affects the actual application effect.

[0006] Therefore, in order to improve the quality of the airborne magnetic data processing and conversion and the boundary identification effect of the geological body, it is necessary to research and improve the ability of the total horizontal gradient method to detect the boundary of a geological body of the airborne magnetic data.

[0007] 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 already known in this field. SUMMARY

[0008] To solve the above problems, the purpose of the embodiments of the present application is to provide a method for obtaining precise airborne magnetic survey data based on the total horizontal gradient method, so as to improve the resolution of the boundary of a deep geological body, thereby improving the quality of the airborne magnetic data processing and conversion and the boundary identification effect of the geological body.

[0009] To achieve the above object, the application provides a method for obtaining accurate aeromagnetic survey data based on total horizontal gradient method, comprising: obtaining aeromagnetic survey data T measured in a test area; calculating horizontal gradient of the aeromagnetic survey data T in x direction and horizontal gradient of the aeromagnetic survey data T in y direction According to the horizontal gradient of the aeromagnetic survey data T in x direction and the horizontal gradient of the aeromagnetic survey data T in y direction calculating original total horizontal gradient H xy ; calculating weak signal enhancement function B according to the original total horizontal gradient H xy ; and obtaining accurate aeromagnetic survey data according to the original total horizontal gradient H xy and the weak signal enhancement function B.

[0010] In an embodiment, after obtaining the aeromagnetic survey data measured in the test area, the method further comprises: preprocessing the aeromagnetic survey data T measured in the test area before calculating the horizontal gradient of the aeromagnetic survey data T in x direction and the horizontal gradient of the aeromagnetic survey data T in y direction

[0011] In an embodiment, the preprocessing comprises one or more of coordinate conversion, normal field correction, daily variation correction, lag correction and magnetic field horizontal adjustment.

[0012] In an embodiment, the calculating the horizontal gradient of the aeromagnetic survey data T in x direction and the horizontal gradient of the aeromagnetic survey data T in y direction comprises: calling geological body feature distribution data of the test area through networking; 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 value identifiers; referring to the M hierarchical scale area decomposed preprocessed aeromagnetic survey data T to obtain M hierarchical magnetic survey data t; determining M and M in x direction horizontal gradient of the M hierarchical magnetic survey data t according to the corresponding relationship between two-dimensional Fourier transform and Hilbert transform in frequency domain; and determining M in x direction horizontal gradient of the M hierarchical magnetic survey data t according to the corresponding relationship between two-dimensional Fourier transform and Hilbert transform in frequency domain. ​​​

[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 areas, which includes: after the test area is segmented into a plurality of grid areas based on a preset grid scale, the geological body feature distribution data is segmented using the plurality of grid areas to obtain a plurality of grid geological body feature data; terrain complexity is evaluated according to the plurality of grid geological body feature data, and a plurality of regional terrain complexities of the plurality of grid areas are output; a preset complexity threshold is set, and a plurality of rounds of merging of the plurality of grid areas 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 areas.

[0014] In an embodiment, the accurate airborne magnetic survey data is obtained according to the original total horizontal gradient H xy and a weak signal enhancement function B, which includes:

[0015] The accurate airborne magnetic survey data is obtained according to the following formula, which includes:

[0016]

[0017] Wherein, Max(H xy ) represents the maximum value of the total horizontal gradient H Xy , T is the measured airborne magnetic survey data, x and y are two directions of spatial coordinates, and Δ is an adjustment parameter, and the value range of Δ is 0-1.

[0018] In an embodiment, the method further includes: according to the M horizontal gradient in the x direction, a first average value and a first square error are calculated and output; the M horizontal gradient in the x direction is arranged in ascending order, and a first horizontal extreme value is extracted; in the same way, the M horizontal gradient in the y direction is processed, and a second average value, a second square error and a second horizontal extreme value are output; after the average burial depth of the test area is obtained, the average burial depth, the first average value, the first square error, the first horizontal extreme value, the second average value, the second square error and the second horizontal extreme value are input into a pre-constructed adjustment parameter disturbance model for disturbance analysis, and real-time adjustment parameters are output.

[0019] In an implementation, the method further comprises: calling a plurality of sets of sample disturbance variables and a plurality of sample adjustment parameters through networking, wherein each set of sample disturbance variables comprises a sample burial depth, a first sample mean, a first sample variance, a first sample extreme value, a second sample mean, a second sample variance, and a second sample extreme value; storing the plurality of sets of sample disturbance variables and the plurality of sample adjustment parameters based on a knowledge graph association, to complete construction of the adjustment parameter disturbance model; inputting the average burial depth, the first average, the first average variance, the first horizontal extreme value, the second average, the second average variance, and the second horizontal extreme value into the adjustment parameter disturbance model, performing data similarity evaluation on the plurality of sets of sample disturbance variables based on the Euclidean distance, and extracting the sample adjustment parameter corresponding to the similarity extreme value as a real-time adjustment parameter.

[0020] To achieve the above object, the application further provides a device for obtaining precise aeromagnetic survey data based on total horizontal gradient method, comprising: an acquisition module for acquiring the aeromagnetic survey data T actually measured in a test area; a gradient calculation module for calculating the horizontal gradient of the aeromagnetic survey data T in x direction and the horizontal gradient of the aeromagnetic survey data T in y direction a primary total horizontal gradient calculation module for calculating the primary total horizontal gradient H based on the horizontal gradient of the aeromagnetic survey data T in x direction and the horizontal gradient of the aeromagnetic survey data T in y direction Xy a weak signal enhancement function calculation module for calculating the weak signal enhancement function B based on the primary total horizontal gradient H x y a precise aeromagnetic survey data acquisition module for obtaining the precise aeromagnetic survey data based on the primary total horizontal gradient H xy and the weak signal enhancement function B.

[0021] To achieve the above object, the application further provides a storage medium storing computer executable instructions for executing the method for obtaining precise aeromagnetic survey data based on total horizontal gradient method.

[0022] Compared with the prior art, the method, device and storage medium for obtaining precise aeromagnetic survey data based on total horizontal gradient method according to the 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 art total horizontal gradient method, and thus improve the quality of aeromagnetic data processing conversion and the effect of geological body boundary identification.

[0023] In addition, the method eliminates the generation of false aeromagnetic data geological body boundary interference, enhances the signal-to-noise ratio, and improves the boundary position enhancement and extraction capability of geological bodies of different burial depths, with higher resolution and accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying 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 from these drawings without creative labor.

[0025] Figure 1 The flow chart of the method for obtaining precise airborne magnetic survey data based on the total horizontal gradient method provided by the embodiment of the present application is shown;

[0026] Figure 2 The structural schematic diagram of the device for obtaining precise airborne magnetic survey data based on the total horizontal gradient method provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0027] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings, but it should be understood that the protection scope of the present application is not limited by the specific embodiments.

[0028] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "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 therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0029] Unless otherwise explicitly indicated, throughout the specification and claims, the term "comprise" or its variants such as "contain" or "include" and the like will be understood to include the stated elements or components, but not exclude other elements or components.

[0030] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the 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 "multiple" is two or more, unless otherwise explicitly specified.

[0031] The flow chart of the method for obtaining precise airborne magnetic survey data based on the total horizontal gradient method provided by the embodiment of the present application is shown in Figure 1 , comprising steps S1-S5.

[0032] Step 1, obtaining the aerial magnetic survey data T measured in the test area.

[0033] Wherein, the geological body feature analysis is performed on the test area, such as buried depth, scale, structural complexity, etc., to divide the terrain complexity of the test area, obtain the distribution of terrain complexity, and then perform flight trajectory optimization 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 flight curve interval and flight height change with the terrain complexity, and the aerial magnetic 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.

[0034] In an implementation manner, before step S2, step S1 can further include: pre-processing the obtained aerial magnetic survey data T measured in the test area to obtain the aerial magnetic survey data T measured in the test area after pre-processing. The pre-processing includes one or more of coordinate conversion, normal field correction, diurnal variation correction, lag correction, and magnetic field level adjustment. Through pre-processing, the noise and error in the aerial magnetic field measurement data caused by various factors can be eliminated.

[0035] In an implementation manner, the selection order of pre-processing can be, for example, coordinate conversion→normal field correction→diurnal variation correction→lag correction.

[0036] 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 pre-processing steps, and through result alignment superposition to improve efficiency.

[0037] Step 2, calculating 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

[0038] In an implementation manner, step S2 can further include:

[0039] According to the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the x-direction horizontal gradient of the pre-processed aerial magnetic survey data T and the y-direction horizontal gradient of the pre-processed aerial magnetic survey data T are determined.

[0040] Specifically, the original total horizontal gradient H can be calculated according to the following formula xy ;

[0041]

[0042] Specifically, the two-dimensional Hilbert transform can be directly calculated in the frequency domain, and its Fourier domain transform factor is:

[0043] DH(u,v) = -i·sign(u,v),

[0044]

[0045] Among them, i 2 = -1, where u and v are the circular wave numbers in the x and y directions, respectively. The Hilbert transform components in the x and y directions can be expressed as:

[0046]

[0047] Among them, F[], F -1 [] denotes the forward and inverse Fourier transforms, respectively; H x With H y These represent the components of the two-dimensional Hilbert transform in the x and y directions, respectively.

[0048] Therefore, the horizontal gradient in the x-direction Horizontal gradient in the y-direction It can be calculated using the Hilbert transform, and the two are equivalent:

[0049]

[0050] By utilizing the correspondence between Hilbert and Fourier transforms, the gradients in the x, y, and z directions can be directly obtained using the Hilbert transform instead of the Fourier transform. Applying this equivalence relationship, i.e., replacing the Fourier transform with the Hilbert transform, has the advantage that the direct Hilbert transform does not amplify noise interference in the airborne magnetic survey data.

[0051] In one implementation, step S2 may further include:

[0052] The system retrieves geological feature distribution data of the test area from the network; evaluates the test area based on the geological feature distribution data, and decomposes the test area hierarchically according to the evaluation results to obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity mean identifiers; referring to the preprocessed aeromagnetic data T after decomposing the M hierarchical scale regions, M hierarchical magnetic data t are obtained; based on the correspondence between the two-dimensional Fourier transform and Hilbert transform in the frequency domain, the horizontal gradients of the M hierarchical magnetic data t in the x-direction are determined to be M. and M horizontal gradients in the y-direction Based on the weighted average of the M level complexities, the M horizontal gradients in the x-direction are fused together. Output horizontal gradient in the x-direction The M-level complexity average weighted fusion in the y direction horizontal gradient M Output in the Y direction horizontal gradient

[0053] In an implementation, 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 areas.

[0054] After the test area is segmented into a plurality of grid areas based on a preset grid scale, the geological body feature distribution data is segmented using the plurality of grid areas to obtain a plurality of grid geological body feature data; terrain complexity is evaluated according to the plurality of grid geological body feature data, and a plurality of regional terrain complexities of the plurality of grid areas are output; a preset complexity threshold is set, and a plurality of rounds of merging of the plurality of grid areas are mapped according to whether adjacent differences of the plurality of regional terrain complexities satisfy the complexity threshold, to obtain the M hierarchical scale areas.

[0055] It should be understood that in a complex geological structure scene, a single scale gradient calculation may have a problem of over-high gradient value of a strong magnetic anomaly area, resulting in neglect of a deep weak gradient. Based on this, the magnetic survey data is subjected to multi-scale decomposition to obtain geological body signals corresponding to different depths, and then the above horizontal gradient calculation is performed on each scale layer to obtain the horizontal gradient of each scale layer, and further multi-scale gradient fusion is performed using weight superposition.

[0056] Specifically, in the embodiment, geological body feature data covering the test area is called from a geological information platform, and the geological body feature data includes but is not limited to geological structure density information, lithology distribution information, buried depth feature information, and historical geomagnetic data. The geological body feature distribution data provides a basic geological background support for subsequent regional evaluation and hierarchical decomposition.

[0057] According to a preset grid scale (such as 1 km x 1 km), the test area is segmented into a plurality of grid areas, and then the obtained geological body feature distribution data is segmented according to the grid area division to obtain a plurality of grid geological body feature data.

[0058] 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 include structure density (based on the distribution density of fault zones or folds), lithology complexity (reflecting the distribution heterogeneity of different lithologies), buried depth coefficient of variation (characterizing the degree of spatial variation of the buried depth of the geological body), and magnetic anomaly gradient (combining the historical geomagnetic data to calculate the first derivative of the magnetic field intensity, describing the boundary characteristics of the magnetic body).

[0059] A complexity threshold is set and 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 the same hierarchical scale region; otherwise, they are kept as independent regions. Through dynamic iterative adjustment of adjacent grid regions by multiple rounds of merging algorithm, M hierarchical scale regions are finally obtained.

[0060] According to the grid region composition of each hierarchical scale region, the corresponding terrain complexity is extracted, averaged, and marked in the corresponding hierarchical scale region.

[0061] Further, the M hierarchical scale regions are used to preprocess the aerial magnetic survey data T to obtain M hierarchical magnetic survey data t. According to the corresponding relationship between two-dimensional Fourier transform and Hilbert transform in the frequency domain, the M horizontal gradients of the M hierarchical magnetic survey data t in the x direction are determined. The M horizontal gradients of the M hierarchical magnetic survey data t in the y direction are determined. The x-direction horizontal gradient is output. The y-direction horizontal gradient is output.

[0062] In step, according to the corresponding relationship between two-dimensional Fourier transform and Hilbert transform in the frequency domain, the M horizontal gradients of the M hierarchical magnetic survey data t in the x direction are determined. The corresponding relationship between two-dimensional Fourier transform and Hilbert transform in the frequency domain can include:

[0063]

[0064] Then, the M horizontal gradients of the M hierarchical magnetic survey data t in the x direction are determined. The x-direction horizontal gradient is output. The y-direction horizontal gradient is output.

[0065] 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, the original total horizontal gradient H xy is calculated.

[0066] The horizontal gradient The M gradient values in the x direction can be fused according to the M level complexity mean weighted fusion The x direction horizontal gradient outputted later Horizontal gradient The M gradient values in the y direction can be fused according to the M level complexity mean weighted fusion The y direction horizontal gradient outputted later

[0067] The M weight values of the M level scale regions are calculated based on the M level complexity mean, and are used for weighted fusion calculation of the x direction horizontal gradient And the y direction horizontal gradient

[0068] Specifically, the original total horizontal gradient H is calculated according to the following formula xy ;

[0069]

[0070] Step 4, the weak signal enhancement function is calculated according to the original total horizontal gradient H xy .

[0071] Specifically, step 4 can be calculated by the following formula:

[0072] The resolution capability of boundary recognition is improved through the weak signal enhancement function B, and its expression is:

[0073]

[0074] It should be noted that the original total horizontal gradient H xy And the weak signal enhancement function can be calculated according to the gradient fused according to the M level complexity mean, or can be directly calculated according to the gradient without fusion.

[0075] Step 5, the accurate airborne magnetic survey data is obtained according to the original total horizontal gradient H xy And the weak signal enhancement function B.

[0076] Specifically, the accurate airborne magnetic survey data can be obtained according to the following formula, which includes:

[0077]

[0078] Wherein, Max(H xy ) represents the maximum value of the total horizontal gradient H xy , T is the measured airborne magnetic survey data, x and y are two directions of spatial coordinates, and Δ represents an adjustment parameter to prevent the denominator from being 0 and avoid "analytical singular point" in the expression, and the value of Δ is 0-1.

[0079] In step 5, the weighted fused gradient values obtained by M hierarchical scale regions are used for calculation, which can make the obtained magnetic survey data more stable in complex geological structure scenes.

[0080] In an implementation manner, the adjustment parameter can be obtained by the following manner: according to the M The first horizontal average and the first horizontal deviation are calculated; the M The first horizontal extreme value is extracted; and the M data processing is performed, and the second horizontal average, the second horizontal deviation and the second horizontal extreme value are output; after the average buried depth of the test region is obtained, the average buried depth, the first horizontal average, the first horizontal deviation, the first horizontal extreme value, the second horizontal average, the second horizontal deviation and the second horizontal extreme value are input into the pre-constructed adjustment parameter perturbation model for perturbation analysis, and the real-time adjustment parameter is output.

[0081] In an implementation manner, a plurality of groups of sample perturbation variables and a plurality of sample adjustment parameters are called in a network, wherein each group of sample perturbation variables includes 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 and a second sample extreme value; the plurality of groups of sample perturbation variables and the plurality of sample adjustment parameters are stored based on a knowledge graph, and the construction of the adjustment parameter perturbation model is completed; the average buried depth, the first horizontal average, the first horizontal deviation, the first horizontal extreme value, the second horizontal average, the second horizontal deviation and the second horizontal extreme value are input into the adjustment parameter perturbation model, the plurality of groups of sample perturbation variables are evaluated for 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.

[0082] For example, part of the knowledge graph is stored as the following 3 groups of sample perturbation variables and corresponding adjustment parameters Δ.

[0083]

[0084] Therefore, the method for obtaining accurate airborne magnetic survey data based on the total horizontal gradient method provided by the embodiment can better detect the boundary of the target geological body of the airborne magnetic data, make the boundary recognition result more convergent, solve the problem of low resolution of the existing total horizontal gradient method, and thus improve the quality of the airborne magnetic data processing and conversion and the boundary recognition effect of the geological body. In addition, the method eliminates the interference of the false airborne magnetic data geological body boundary, enhances the signal-to-noise ratio, improves the boundary position enhancement and extraction capability of the geological body with different buried depths, has higher resolution and accuracy, and has other advantages.

[0085] Therefore, the boundary position enhancement and extraction capability of geological bodies with different depths is improved, and higher resolution and accuracy are achieved.

[0086] The total horizontal gradient method (THDR) expression provided in the embodiment is as follows:

[0087]

[0088] Compared with the existing total horizontal gradient method expression, the total horizontal gradient method expression provided in the embodiment is as follows:

[0089]

[0090] The total horizontal gradient method expression provided in the embodiment is as follows: the maximum value of the total horizontal gradient is used to normalize H xy B, the purpose is to limit the amplitude of the magnetic anomaly with large differences in strength and weakness within a certain range, balance the strong and weak magnetic anomalies, avoid distortion caused by too strong or too weak magnetic anomaly signals, and thus improve the stability and noise resistance of the magnetic anomaly signals.

[0091] The total horizontal gradient method expression provided in the embodiment introduces a weak signal enhancement function B and a Hilbert transform, establishes a reasonable equalization filter, and redefines the total horizontal gradient method. The position of the geological target body of the aeromagnetic data obtained by using the total horizontal gradient method provided in the embodiment can further accurately infer the boundary, depth, occurrence, scale, field distribution rule and physical properties of the structure field source, and has important significance for dividing tectonic units, conducting tectonic zoning, determining the position of fault structures, distinguishing the distribution of different lithology and strata, and conducting physical property mapping.

[0092] The embodiment of the application further provides a device for obtaining accurate aeromagnetic survey data based on the total horizontal gradient method, which comprises: an acquisition module 1, a gradient calculation module 2, an original total horizontal gradient calculation module 3, a weak signal enhancement function calculation module 4, and an accurate aeromagnetic survey data acquisition module 5.

[0093] The acquisition module 1 is used to acquire the aeromagnetic survey data T actually measured in a test area. The gradient calculation module 2 is used to calculate 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 according to the aeromagnetic survey data T. The original total horizontal gradient calculation module 3 is used to calculate the original total horizontal gradient H according to the horizontal gradient xyThe weak signal enhancement function calculating module 4 is configured to calculate a weak signal enhancement function B according to the original total horizontal gradient H xy The weak signal enhancement function B is calculated. The precise aeromagnetic survey data obtaining module 5 is configured to obtain precise aeromagnetic survey data according to the original total horizontal gradient H xy 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 precise aeromagnetic survey data based on a total horizontal gradient method.

[0095] The storage medium can be any available medium or data storage device accessible by a computer, including but not limited to a magnetic memory (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical memory (such as a CD, a DVD, a BD, a HVD, etc.), and a semiconductor memory (such as a ROM, an EPROM, an EEPROM, a non-volatile memory (NAND FLASH), a solid state disk (SSD), etc.), etc.

[0096] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, and all of these should be covered within 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 airborne magnetic survey data based on the total horizontal gradient method, characterized in that, include: Obtain the measured airborne magnetic data T in the test area; Calculate the horizontal gradient of the airborne magnetic measurement data T in the x-direction. and the horizontal gradient of the airborne magnetic measurement data T in the y direction Based on the aforementioned airborne magnetic survey data, the horizontal gradient of T in the x-direction... and the horizontal gradient of the airborne magnetic measurement data T in the y direction Calculate the original total horizontal gradient H xy ; According to the original total horizontal gradient H xy The weak signal enhancement function B is calculated using the following formula: Based on the original total horizontal gradient H xy Using the weak signal enhancement function B, accurate airborne magnetic measurement data can be obtained; Among them, the step of using the original total horizontal gradient H xy Using the weak signal enhancement function B, accurate airborne magnetic measurement data is obtained, including: Accurate airborne magnetic survey data are obtained using the following formula, which includes: Among them, Max(H) xy ) represents the original total horizontal gradient H xy Maximum value, T represents the measured airborne magnetic survey data, x and y represent the two directions of the spatial coordinates, and Δ is the adjustment parameter, with a value range of 0-1.

2. The method according to claim 1, characterized in that, After obtaining the measured airborne magnetic data T in the test area, the horizontal gradient of the airborne magnetic data T in the x-direction is calculated. and the horizontal gradient of the airborne magnetic measurement data T in the y direction Previously, the method also included: preprocessing the measured airborne magnetic data T of the acquired test area.

3. The method according to claim 2, characterized in that, The preprocessing includes one or more of the following: coordinate transformation, normal field correction, diurnal variation correction, hysteresis correction, and magnetic field level adjustment.

4. The method according to claim 2, characterized in that, The calculation of the horizontal gradient of the airborne magnetic measurement data T in the x-direction is described. and the horizontal gradient of the airborne magnetic measurement data T in the y direction include: The geological feature distribution data of the test area are accessed via network connection. The test area is evaluated based on the geological body feature distribution data, and the test area is decomposed hierarchically according to the evaluation results to obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity mean identifiers. Referring to the preprocessed airborne magnetic measurement data T after decomposition of the M-level scale regions, M-level magnetic measurement data t are obtained; Based on the correspondence between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the horizontal gradients of the M level magnetic measurement data t in the x-direction are determined as M. and M horizontal gradients in the y-direction Based on the weighted average of the M level complexities, the M horizontal gradients in the x-direction are fused together. Output horizontal gradient in the x-direction Based on the weighted average of the M level complexities, the M horizontal gradients in the y-direction are fused together. Output horizontal gradient in the y-direction 5. The method according to claim 4, characterized in that, The evaluation of the test area based on the geological body characteristic distribution data, and the hierarchical decomposition of the test area according to the evaluation results, yields M hierarchical scale regions, including: After dividing the test area into multiple grid regions based on a preset grid scale, the geological body feature distribution data is divided using the multiple grid regions to obtain multiple grid geological body feature data. Based on the feature data of the multiple raster geological bodies, the terrain complexity is evaluated, and the terrain complexity of multiple regions of the multiple raster areas is output. A complexity threshold is preset, and the multiple raster regions are merged in multiple rounds based on whether the adjacent differences in the terrain complexity of the multiple regions meet the complexity threshold, to obtain the M hierarchical scale regions.

6. The method according to claim 4, characterized in that, Also includes: Based on the horizontal gradient M in the x-direction Calculate and output the first water average and the first level variance; M items arranged in ascending order of horizontal gradient in the x-direction To extract the first level extreme value; And so on, performing M horizontal gradients in the y-direction. The data processing outputs the second level mean, the second level variance, and the second level extreme value. After obtaining the average burial depth of the test area interactively, the average burial depth, the first water average value, the first horizontal variance, the first horizontal extreme value, the second water average value, the second horizontal variance, and the second horizontal extreme value are input into a pre-constructed adjustment parameter disturbance model for disturbance analysis, and the real-time adjustment parameters are output.

7. The method according to claim 6, characterized in that, Also includes: The system calls multiple sets of sample perturbation variables and multiple sample adjustment parameters via the network. 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, and second sample extreme value. Based on the knowledge graph, the perturbation variables of the multiple sets of samples and the adjustment parameters of the multiple samples are associated and stored to complete the construction of the adjustment parameter perturbation model; The average burial depth, first water average value, first horizontal variance, first horizontal extreme value, second water average value, second horizontal variance, and second horizontal extreme value are input into the adjustment parameter perturbation model. Based on the Euclidean distance ratio, the data similarity of the perturbation variables of the multiple groups of samples is evaluated, and the sample adjustment parameters corresponding to the extreme similarity values ​​are extracted as real-time adjustment parameters.

8. A device for acquiring accurate airborne magnetic survey data based on the total horizontal gradient method, characterized in that, include: The acquisition module acquires the measured aeromagnetic data T from the test area. The gradient calculation module is used to calculate the horizontal gradient of the airborne magnetic measurement data T in the x-direction. and the horizontal gradient of the airborne magnetic measurement data T in the y direction The original total horizontal gradient calculation module is used to calculate the horizontal gradient in the x-direction based on the airborne magnetic measurement data T. and the horizontal gradient of the airborne magnetic measurement data T in the y direction Calculate the original total horizontal gradient H xy ; The weak signal enhancement function calculation module is used to calculate the original total horizontal gradient H. xy The weak signal enhancement function B is calculated using the following formula: The precise airborne magnetic survey data acquisition module is used to acquire data based on the original total horizontal gradient H. xy Using the weak signal enhancement function B, accurate airborne magnetic measurement data can be obtained; Among them, the step of using the original total horizontal gradient H xy Using the weak signal enhancement function B, accurate airborne magnetic measurement data is obtained, including: Accurate airborne magnetic survey data are obtained using the following formula, which includes: Among them, Max(H) xy ) represents the original total horizontal gradient H xy Maximum value, T represents the measured airborne magnetic survey data, x and y represent the two directions of the spatial coordinates, and Δ is the adjustment parameter, with a value range of 0-1.

9. A storage medium, characterized in that, The storage medium stores computer-executable instructions for executing the method for obtaining accurate airborne magnetic measurement data based on the total horizontal gradient method as described in any one of claims 1-7.

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