Method for obtaining precise airborne magnetic survey data based on field source parameter imaging method

Abstract

The application discloses a method for obtaining accurate aeromagnetic survey data based on a field source parameter imaging method, and comprises the following steps: obtaining the actually measured aeromagnetic survey data of a test area; performing Fourier transform on the aeromagnetic survey data in a frequency domain; obtaining an x-direction horizontal gradient according to the Fourier transform result of the aeromagnetic survey data and an x-direction gradient in the frequency domain; constructing a noise suppression factor, and obtaining an improved z-direction vertical gradient according to the Fourier transform result of the aeromagnetic survey data, a conversion factor of the x-direction gradient in the frequency domain and the noise suppression factor, wherein the noise suppression factor is related to the inversion quality of a subsurface magnetic body; and obtaining the accurate aeromagnetic survey data according to the x-direction horizontal gradient and the improved z-direction vertical gradient. The method for obtaining accurate aeromagnetic survey data based on the field source parameter imaging method can solve the problem of "analytical singular points" in the SPI field source parameter imaging method, thereby improving the calculation stability.

Description

Technical Field

[0001] This invention relates to the field of airborne magnetic measurement technology, and in particular to a method for obtaining accurate airborne magnetic measurement data based on field source parameter imaging. Background Technology

[0002] Airborne magnetic surveying involves installing airborne magnetometers (such as optically pumped, nuclear vortex, and fluxgate magnetometers) in an aircraft to observe geomagnetic field parameters (such as total geomagnetic field strength, total magnetic field anomaly, or its gradient) to locate magnetic or magnetically related mineral bodies. This information is used to understand geological structures, perform magnetic mapping, and address issues related to urban and engineering stability and archaeology.

[0003] Airborne magnetic survey data comprehensively reflects the magnetic field information of magnetic geological bodies of different depths, shapes, and scales on the observation surface. However, due to errors in the measurement data or the superposition of magnetic fields, the measurement data are difficult to distinguish, which brings difficulties to geological interpretation.

[0004] Currently, the ability to distinguish aeromagnetic anomalies can be improved through source parameter imaging (SPI).

[0005] The advantage of Source Parameter Imaging (SPI) is that it is unaffected by the magnetization direction and does not depend on the magnitude of magnetization intensity and magnetic inclination. It can quickly and automatically invert the characteristic points and burial depth of underground magnetic bodies. SPI is an inversion method that calculates the corresponding magnetic body parameters based on the analytical signal composed of the first derivative of the magnetic field, its amplitude, phase angle, and the wavenumber (phase angle change rate) of the first derivative analytical signal, and then plots the inversion results as an image.

[0006] In the field source parameter imaging method, the local wavenumber k of the first derivative analytical signal is defined as the rate of change of the phase angle θ(x,z) along the profile direction, and its expression is:

[0007]

[0008] However, in the formula when the denominator Firstly, the SPI field source parameter imaging method has "analytic singularities," which leads to poor stability of numerical calculation results. Secondly, it struggles with calculating the vertical gradient in the z-direction. It is susceptible to high-frequency noise interference, which reduces the inversion accuracy and affects the practical application effect.

[0009] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0010] To address the aforementioned problems, the present invention aims to provide a method and apparatus for acquiring accurate airborne magnetic measurement data based on the field source parameter imaging method, thereby solving the problem of "analytical singularities" in the SPI field source parameter imaging method and improving computational stability.

[0011] To achieve the above objectives, this invention provides a method for acquiring accurate airborne magnetic measurement data based on the SPI field source parameter imaging method, comprising: acquiring measured airborne magnetic measurement data ΔT(x,z) in the test area; performing a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z); and obtaining the horizontal gradient ΔT in the x-direction based on the result of the Fourier transform of the airborne magnetic measurement data and the conversion factor of the frequency domain x-direction gradient. x A noise suppression factor is constructed. Based on the Fourier transform results of the airborne magnetic data, the conversion factor of the z-direction gradient in the frequency domain, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies; according to the horizontal gradient ΔT in the x-direction. x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data.

[0012] In one embodiment, after acquiring the measured airborne magnetic data of the test area, before performing a frequency domain Fourier transform on the airborne magnetic data, the method further includes: preprocessing the acquired measured airborne magnetic data of the test area.

[0013] In one implementation, the horizontal gradient ΔT in the x-direction is obtained based on the Fourier transform result of the airborne magnetic measurement data and the conversion factor of the x-direction gradient in the frequency domain. x This includes: calculating the spectrum F[ΔT(x,z)] of the horizontal gradient in the x-direction based on the Fourier transform result F[ΔT(x,z)] of the airborne magnetic measurement data and the gradient conversion factor in the x-direction of the frequency domain. x [(x,z)]; the spectrum of the horizontal gradient in the x-direction F[ΔT] x Performing an inverse Fourier transform on [x,z) yields the horizontal gradient ΔT in the x-direction. x .

[0014] In one implementation, the improved vertical gradient ΔT in the z-direction is obtained based on the Fourier transform result of the airborne magnetic measurement data, the conversion factor of the z-direction gradient in the frequency domain, and the noise suppression factor. z iThis includes: obtaining the spectrum F[ΔT(x,z)] of the vertical gradient in the z-direction based on the Fourier transform result F[ΔT(x,z)] of the airborne magnetic measurement data and the conversion factor of the z-direction gradient in the frequency domain. z [x,z)]; based on the spectrum F[ΔT] of the vertical gradient in the z-direction. z [x,z] and high-frequency noise suppression factor The spectrum F of the improved z-direction vertical gradient was calculated. ′ [ΔT z [x,z)], where h up The height of the distance from the magnetic field observation surface; the spectrum F of the improved z-direction vertical gradient. ′ [ΔT z Performing an inverse Fourier transform on [x,z) yields the improved vertical gradient ΔT in the z-direction. z i .

[0015] In one embodiment, after acquiring the measured airborne magnetic survey data of the test area, and before performing a frequency domain Fourier transform on the airborne magnetic survey data, the method further includes: connecting to the network to retrieve geological feature distribution data of the test area; evaluating the test area based on the geological feature distribution data, and hierarchically decomposing the test area according to the evaluation results to obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity mean identifiers; and decomposing the preprocessed airborne magnetic survey data T with reference to the M hierarchical scale regions to obtain M hierarchical magnetic survey data t.

[0016] In one embodiment, performing a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z) includes: performing a Fourier transform on M levels of magnetic measurement data t; and obtaining the horizontal gradient ΔT in the x-direction based on the result of the Fourier transform of the airborne magnetic measurement data and the conversion factor of the frequency domain x-direction gradient. x This includes: determining the horizontal gradient Δt in the x-direction of the M levels of magnetic measurement data t based on the Fourier transform results of the M levels of magnetic measurement data t and the conversion factor of the gradient in the x-direction of the frequency domain. x Based on the weighted average of the M level complexities, M Δt values ​​are fused in the horizontal gradient along the x-direction. x The output is the horizontal gradient ΔT in the x-direction. x .

[0017] In one embodiment, performing a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z) includes: performing a Fourier transform on M levels of magnetic measurement data t; and based on the result of the Fourier transform of the airborne magnetic measurement data, the conversion factor of the frequency domain z-direction gradient, and the high-frequency noise suppression factor... The improved vertical gradient ΔT in the z-direction is obtained. zi This includes: the results of the t-Fourier transform of the magnetic measurement data from M levels, the frequency domain z-direction gradient, and the high-frequency noise suppression factor. Determine the horizontal gradient Δt in the z-direction of the M levels of magnetic measurement data. z Based on the weighted average of the M level complexities, M Δt values ​​are fused in the horizontal gradient along the z-direction. z i The output is the horizontal gradient ΔT in the z-direction. z i .

[0018] In one embodiment, the step of evaluating the test area based on the geological body feature distribution data and hierarchically decomposing the test area according to the evaluation results to obtain M hierarchical scale regions includes: dividing the test area into multiple grid regions based on a preset grid scale; using the multiple grid regions to divide the geological body feature distribution data to obtain multiple grid geological body feature data; evaluating the terrain complexity based on the multiple grid geological body feature data and outputting the terrain complexity of multiple regions in the multiple grid regions; setting a complexity threshold and performing multiple rounds of merging of the multiple grid regions based on whether the adjacent differences of the terrain complexity of the multiple regions meet the complexity threshold to obtain the M hierarchical scale regions.

[0019] In one implementation, the step is based on the horizontal gradient ΔT in the x-direction. x Improved vertical gradient ΔT in the z-direction z i Obtaining accurate airborne magnetic survey data includes: obtaining accurate airborne magnetic survey data according to the following formula, wherein the formula includes:

[0020]

[0021] Where max(ΔT) x ) represents ΔT x The maximum value, min(ΔT) x ) represents ΔT x The minimum value, max(ΔT) z i ) represents ΔT z i The maximum value, min(ΔT) z i ) represents ΔT z i The minimum value of , where x and z are the two directions of spatial coordinates, and α represents the regularization adjustment factor, taking a value between 0 and 1. Here, u is the noise suppression factor, and h is the frequency along the x-direction in the frequency domain. up This represents the height of the distance from the magnetic field observation surface.

[0022] To achieve the above objectives, the present invention also provides an apparatus for acquiring accurate airborne magnetic measurement data based on the field source parameter imaging method, comprising: an acquisition module for acquiring measured airborne magnetic measurement data in a test area; a Fourier transform module for performing a Fourier transform on the airborne magnetic measurement data in the frequency domain; and a horizontal gradient calculation module for obtaining the horizontal gradient ΔT in the x-direction based on the result of the Fourier transform of the airborne magnetic measurement data and the frequency domain x-direction gradient. x The improved vertical gradient calculation module is used to construct the noise suppression factor. Based on the Fourier transform results of the airborne magnetic survey data, the conversion factor of the frequency domain z-direction gradient, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies; the precise airborne magnetic survey data acquisition module is used to determine the horizontal gradient ΔT in the x-direction. x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data.

[0023] 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 field source parameter imaging as described in any one of the above claims.

[0024] Compared with the prior art, the method, apparatus and storage medium for obtaining accurate airborne magnetic measurement data based on the field source parameter imaging method according to the present invention can solve the problem of "analytical singularity" in the SPI field source parameter imaging method, thereby improving computational stability. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of the method for obtaining accurate airborne magnetic measurement data based on field source parameter imaging provided in an embodiment of the present invention is shown;

[0027] Figure 2 A schematic diagram of the device for obtaining accurate airborne magnetic measurement data based on the field source parameter imaging method provided in an embodiment of the present invention is shown. Detailed Implementation

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0029] In the description of this invention, 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," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0030] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0031] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0032] The flowchart of the method for obtaining accurate airborne magnetic measurement data based on the field source parameter imaging method provided in this embodiment of the invention is shown below. Figure 1 This includes steps S1 to S5.

[0033] Step 1: Obtain the measured airborne magnetic measurement data ΔT(x,z) of the test area.

[0034] The process involves analyzing the geological characteristics of the test area, such as burial depth, scale, and structural complexity, to classify the area into different levels of topographic complexity. Based on this distribution, flight trajectories are optimized to generate dynamic flight paths, which are then used to collect raw magnetic survey data. The spacing and altitude of the flight curves vary with the topographic complexity. The airborne magnetic survey data ΔT(x,z), representing the airborne magnetic anomaly field, is the additional magnetic field generated by ferromagnetic geological bodies in the Earth's crust under the influence of the Earth's magnetic field.

[0035] In one implementation, after step S1 and before step S2, the method may further include: preprocessing the acquired measured airborne magnetic measurement data ΔT(x,z) of the test area to obtain preprocessed measured airborne magnetic measurement data of the test area. The preprocessing includes one or more of coordinate transformation, normal field correction, diurnal variation correction, hysteresis correction, and magnetic field level adjustment. Preprocessing can eliminate noise and errors in the airborne magnetic field measurement data caused by various factors.

[0036] In one implementation, the order of preprocessing selection could be, for example, coordinate transformation → normal field correction → diurnal variation correction → hysteresis correction.

[0037] Furthermore, the above methods can be used in combination. For example, coordinate transformation and diurnal variation correction can be processed simultaneously by configuring two containers. Then, the registered spatial data and the time-corrected magnetic field data are synchronized in time and space to unify the timestamps and location labels. Efficiency can be improved through parallel processing of specific preprocessing steps and by aligning and overlaying the results.

[0038] Step 2: Perform a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z).

[0039] Specifically, the preprocessed airborne magnetic measurement data can be expanded first, and then the expanded data can be subjected to a Fourier forward transform to calculate the spectrum F[ΔT(x,z)].

[0040] It's important to note that edge expansion aims to meet the requirements of Fourier transform and avoid data distortion at the boundaries. Fourier transform assumes the signal is an infinitely periodic sequence. Abrupt changes at the boundaries of real-world data (such as sharp changes in edge values ​​in aeromagnetic data) can lead to high-frequency artifacts in the spectrum. Edge expansion increases the number of frequency domain sampling points, satisfying a power of 2, resulting in a smoother spectral curve and easier observation of details. The Fourier transform algorithm is most efficient when the data length is a power of 2. Existing edge expansion methods include cosine edge expansion, mean edge expansion, zero-value edge expansion, and minimum curvature edge expansion.

[0041] Step 3: Based on the Fourier transform result of the airborne magnetic measurement data ΔT(x,z) and the conversion factor of the frequency domain x-direction gradient, obtain the horizontal gradient ΔT in the x-direction. x .

[0042] Specifically, step 3 may include: calculating the spectrum F[ΔT] of the horizontal gradient in the x-direction based on the Fourier transform result of the airborne magnetic data T and the conversion factor of the gradient in the x-direction of the frequency domain. x [(x,z)]; the spectrum of the horizontal gradient in the x-direction F[ΔT] x Performing an inverse Fourier transform on [x,z) yields the horizontal gradient ΔT in the x-direction. x .

[0043] Specifically, the spectrum F[ΔT(x,z)] can be multiplied by the conversion factors of the gradients in the x and z directions of the frequency domain, respectively, to calculate the spectrum F[ΔT] of the horizontal gradient in the x direction. x The spectrum F[ΔT] of the vertical gradient in the z-direction and (x,z)] z (x,z)];for F[ΔT x Performing an inverse Fourier transform and edge reduction (i.e., removing expanded edges) on [x,z] yields the horizontal gradient ΔT in the x-direction. x The gradient transformation factor in the x-direction is 2πju, and ΔT x =F -1 {2πju·F[ΔT(x,z)]}.

[0044] In the formula, F[] represents the positive Fourier transform, F -1 [] represents the inverse Fourier transform, j represents the imaginary number, and u is the frequency along the x-direction in the frequency domain.

[0045] Step 4: Construct the noise suppression factor. Based on the Fourier transform results of the airborne magnetic survey data, the conversion factor of the frequency domain z-direction gradient, and the noise suppression factor, obtain the improved z-direction vertical gradient ΔT. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies.

[0046] In one implementation, the spectrum F[ΔT] of the vertical gradient in the z-direction is obtained based on the Fourier transform result of the airborne magnetic measurement data ΔT(x,z) and the conversion factor of the z-direction gradient in the frequency domain. z [x,z]; Construct a high-frequency noise suppression factor. Based on the spectrum of the vertical gradient in the z-direction, F[ΔT] z [x,z] and high-frequency noise suppression factor The spectrum F of the improved z-direction vertical gradient was calculated. ′ [ΔT z [x,z)], where h up The height above the magnetic field observation surface is determined based on the inversion quality of the underground magnetic body; the spectrum F of the improved z-direction vertical gradient is... ′ [ΔT z Performing an inverse Fourier transform on [x,z) yields the improved vertical gradient ΔT in the z-direction. z i .

[0047] It should be noted that when M is 1, Δt x With ΔT x same.

[0048] When calculating the vertical gradient in the z-direction, existing techniques suffer from high-frequency noise amplification. This embodiment constructs a high-frequency noise suppression factor. Then the calculation yields... To suppress high-frequency noise interference and improve accuracy.

[0049] For F[ΔT z The inverse Fourier transform and edge reduction (i.e., removing expanded edges) of [x,z] are performed to obtain the vertical gradient ΔT in the z-direction after high-frequency noise suppression improvement. z i The gradient transformation factor in the z-direction is 2π|u|.

[0050] In the formula, F[] represents the positive Fourier transform, F -1 [] represents the inverse Fourier transform, j represents the imaginary number, and u is the frequency along the x-direction in the frequency domain.

[0051] Before step 5, the maximum value of the horizontal gradient in the x-direction, max(ΔT), can be calculated. x ), minimum value min(ΔT) x ) and the maximum value of the vertical gradient in the z-direction max(ΔT) z i ), minimum value min(ΔT) z i That is, to obtain

[0052] Step 5, based on the horizontal gradient ΔT in the x-direction x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data.

[0053] The horizontal gradient ΔT in the x-direction x Improved vertical gradient ΔT in the z-direction z i Obtaining accurate airborne magnetic survey data includes:

[0054] Accurate airborne magnetic survey data are obtained using the following formula, which includes:

[0055]

[0056] Where, the horizontal gradient in the x-direction Vertical gradient in the z-direction max and min represent the maximum and minimum values, respectively. max(ΔT) x ) represents ΔT x The maximum value, min(ΔT) x ) represents ΔT xThe minimum value, max(ΔT) z i ) represents ΔT z i The maximum value, min(ΔT) z i ) represents ΔT z i The minimum value of , x and z are the two directions of spatial coordinates, α represents the regularization adjustment factor, different values ​​have an impact on the boundary recognition results; the value of α is 0-1. Here, u is the noise suppression factor, and h is the frequency along the x-direction in the frequency domain. up The height is determined based on the inversion quality of the underground magnetic body, and is the distance from the magnetic field observation surface.

[0057] The value of α can be 0.001. α is obtained automatically from a deep learning sample library, and its value varies depending on different geological conditions. The optimal parameter value is determined based on the quality of the geological interpretation results.

[0058] Then, based on the local wavenumber κ of the analytical signal i Inversion is performed to obtain accurate airborne magnetic measurement data.

[0059] Therefore, according to the local wavenumber κ of this embodiment i This study breaks through the traditional concept of defining the local wavenumber κ in the SPI source parameter imaging calculation process. It introduces an operator to suppress high-frequency noise and improve computational stability, and reconstructs a new local wavenumber κ. i The calculation formula solves the problems of "analytic singularity" in the original local wavenumber κ and susceptibility to high-frequency noise interference, thus improving the calculation stability and the accuracy of the inversion results.

[0060] In one implementation, the process after step S1 and before step S2 may further include:

[0061] The geological feature distribution data of the test area is retrieved via network; the test area is evaluated based on the geological feature distribution data, and the test area is hierarchically decomposed according to the evaluation results to obtain M hierarchical scale regions, wherein the M hierarchical scale regions have M hierarchical complexity mean identifiers; the preprocessed airborne magnetic measurement data T is decomposed with reference to the M hierarchical scale regions to obtain M hierarchical magnetic measurement data t.

[0062] Accordingly, step S2 may include: performing a Fourier transform on the M levels of magnetic measurement data t.

[0063] Step S3 may include: determining the horizontal gradient Δt in the x-direction of the M-level magnetic measurement data t based on the Fourier transform results of the M-level magnetic measurement data t and the conversion factor of the gradient in the x-direction of the frequency domain. xBased on the weighted average of the M level complexities, M Δt values ​​are fused in the horizontal gradient along the x-direction. x The output is the horizontal gradient ΔT in the x-direction. x .

[0064] In step S4, the results of the Fourier transform of the airborne magnetic measurement data T, the conversion factor of the frequency domain z-direction gradient, and the high-frequency noise suppression factor are used. The improved vertical gradient ΔT in the z-direction is obtained. z i It can include:

[0065] Based on the results of the Fourier transform of the magnetic measurement data at M levels, the frequency domain z-direction gradient, and the high-frequency noise suppression factor... Determine the horizontal gradient Δt in the z-direction of the M levels of magnetic measurement data. z Based on the weighted average of the M level complexities, M Δt values ​​are fused in the horizontal gradient along the z-direction. z i The output is the horizontal gradient ΔT in the z-direction. z i .

[0066] In step 5, the weighted fusion ΔT obtained through M hierarchical scale regions is used. x and ΔT z i By performing calculations, more stable magnetic measurement data can be obtained in complex geological scenarios.

[0067] In one implementation, 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 to obtain M hierarchical scale regions, include:

[0068] After dividing the test area into multiple grid regions based on a preset grid scale, the geological feature distribution data is segmented using the multiple grid regions to obtain multiple grid geological feature data. The terrain complexity is evaluated based on the multiple grid geological feature data, and the terrain complexity of multiple regions in the multiple grid regions is output. A preset complexity threshold is set, and based on whether the adjacent differences in the terrain complexity of the multiple regions meet the complexity threshold, multiple rounds of merging of the multiple grid regions are performed to obtain the M hierarchical scale regions.

[0069] It should be understood that in complex geological structures, gradient calculation at a single scale may result in excessively high gradient values ​​in areas with strong magnetic anomalies, leading to the neglect of weak gradients at depth. Based on this, this embodiment decomposes the magnetic data into multiple scales to obtain geological body signals corresponding to different burial depths. Then, the above horizontal gradient calculation is performed on each scale layer to obtain the horizontal gradient of each scale layer. Furthermore, weighted superposition is used to perform multi-scale gradient fusion.

[0070] Specifically, in this embodiment, geological feature data covering the test area is retrieved from the geological information platform. The geological feature data includes, but is not limited to, geological structure density information, lithological distribution information, burial depth feature information, and historical geomagnetic data. The geological feature distribution data provides basic geological background support for subsequent regional evaluation and hierarchical decomposition.

[0071] The test area is divided into multiple grid regions according to a preset grid scale (e.g., 1km×1km). Then, the geological feature distribution data obtained is segmented according to the grid region division to obtain multiple grid geological feature data.

[0072] The topographic complexity of each grid cell is calculated by inputting the geological feature data of the multiple grid cells into the quantization model. The evaluation indicators of the quantization model include structural density (based on the distribution density of fault zones or folds), lithological complexity (reflecting the distribution heterogeneity of different lithologies), burial depth variation coefficient (characterizing the 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).

[0073] A complexity threshold is set and used as a criterion for determining whether adjacent grid regions can be merged. If the complexity difference between adjacent grids is less than the threshold, they are merged into regions of the same scale; otherwise, they are retained as independent regions. Through multiple rounds of merging algorithms, adjacent grid regions are dynamically iterated and adjusted to ultimately obtain M scale regions of different levels.

[0074] Based on the grid region composition of each hierarchical scale region, extract the corresponding terrain complexity, calculate the mean, and mark it in the corresponding hierarchical scale region.

[0075] In one implementation, the regularization adjustment factor can be obtained by: based on M Δt values ​​of the horizontal gradient in the x-direction. x Calculate and output the first water average and the first level variance; arrange the M Δt values ​​in ascending order of the horizontal gradient in the x-direction. x This is done to extract the first level extremum; and so on, M Δt values ​​are applied to the vertical gradient in the z-direction. z iThe data processing outputs the second water average value, the second level variance, and the second level extreme value. After interactively obtaining the average burial depth of the test area, the average burial depth, the first water average value, the first level variance, the first level extreme value, the second water average value, the second level variance, and the second level extreme value are input into a pre-constructed regularization adjustment factor perturbation model for perturbation analysis, and the real-time regularization adjustment factor is output.

[0076] In one implementation, multiple sets of sample perturbation variables and multiple sample regularization adjustment factors are invoked via network connection. 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. The multiple sets of sample perturbation variables and multiple sample regularization adjustment factors are stored in association based on a knowledge graph to complete the construction of the regularization adjustment factor perturbation model. The average burial depth, first water level average value, first level variance, first level extreme value, second water level average value, second level variance, and second level extreme value are input into the regularization adjustment factor perturbation model. Data similarity is evaluated based on Euclidean distance ratio for the multiple sets of sample perturbation variables, and the sample regularization adjustment factor corresponding to the similarity extreme value is extracted as the real-time regularization adjustment factor.

[0077] In this embodiment, the first-order local wavenumber κ is reconstructed based on the calculated gradient value. i The expression is:

[0078]

[0079] Compared with the original expression for the first-order local wavenumber κ:

[0080]

[0081] Therefore, according to the first-order local wavenumber κ of this embodiment i This study breaks through the traditional concept of defining the local wavenumber κ in the SPI source parameter imaging calculation process. It introduces an operator to suppress high-frequency noise and improve computational stability, and reconstructs a new local wavenumber κ. i The calculation formula solves the problems of "analytic singularity" in the original local wavenumber κ and susceptibility to high-frequency noise interference, thus improving the calculation stability and the accuracy of the inversion results.

[0082] This invention also provides a device for acquiring accurate airborne magnetic measurement data based on the field source parameter imaging method, comprising: an acquisition module 1, a Fourier transform module 2, a horizontal gradient calculation module 3, an improved vertical gradient calculation module 4, and an accurate airborne magnetic measurement data acquisition module 5.

[0083] Module 1 is used to acquire measured airborne magnetic data in the test area. Module 2 is used to perform a frequency domain Fourier transform on the airborne magnetic data. Module 3 is used to calculate the horizontal gradient ΔT in the x-direction based on the result of the Fourier transform of the airborne magnetic data and the conversion factor of the frequency domain x-direction gradient. x The improved vertical gradient calculation module 4 is used to construct the noise suppression factor. Based on the Fourier transform results of the airborne magnetic data, the conversion factor of the frequency domain z-direction gradient, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies. The precise airborne magnetic survey data acquisition module 5 is used to determine the horizontal gradient ΔT in the x-direction. x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data.

[0084] This invention also provides a storage medium storing computer-executable instructions, which include a program for executing the above-described method for obtaining accurate airborne magnetic measurement data based on field source parameter imaging. The computer-executable instructions can execute the method in any of the above-described method embodiments.

[0085] The storage medium can be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical storage (e.g., CD, DVD, BD, HVD), and semiconductor storage (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0086] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for acquiring accurate airborne magnetic measurement data based on field source parameter imaging, characterized in that, include: Obtain the measured airborne magnetic data ΔT(x,z) of the test area; Perform a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z); Based on the Fourier transform results of the airborne magnetic measurement data and the conversion factor of the x-direction gradient in the frequency domain, the horizontal gradient ΔT in the x-direction is obtained. x ; A noise suppression factor is constructed. Based on the Fourier transform results of the airborne magnetic measurement data, the conversion factor of the z-direction gradient in the frequency domain, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies; Based on the horizontal gradient ΔT in the x-direction x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data; Among them, the horizontal gradient ΔT in the x-direction x Improved vertical gradient ΔT in the z-direction z i Obtaining accurate airborne magnetic survey data includes: Accurate airborne magnetic survey data are obtained using the following formula, which includes: Where max(ΔT) x ) represents ΔT x The maximum value, min(ΔT) x ) represents ΔT x The minimum value, max(ΔT) z i ) represents ΔT z i The maximum value, min(ΔT) z i ) represents ΔT z i The minimum value, ΔT x =F -1 {2πju·F[ΔT(x,z)]}, F[] represents the positive Fourier transform, F -1 [] denotes the inverse Fourier transform, j represents the imaginary number, and α represents the regularization adjustment factor, which takes values ​​from 0 to 1. Here, u is the noise suppression factor, and h is the frequency along the x-direction in the frequency domain. up This represents the height of the distance from the magnetic field observation surface.

2. The method according to claim 1, characterized in that, After acquiring the measured airborne magnetic data of the test area, before performing a frequency domain Fourier transform on the airborne magnetic data, the method further includes: preprocessing the acquired measured airborne magnetic data of the test area.

3. The method according to claim 2, characterized in that, Based on the Fourier transform results of the airborne magnetic measurement data and the gradient in the x-direction of the frequency domain, the horizontal gradient ΔT in the x-direction is obtained. x ,include: Based on the Fourier transform result F[ΔT(x,z)] of the airborne magnetic measurement data and the gradient conversion factor in the x-direction of the frequency domain, the spectrum F[ΔT] of the horizontal gradient in the x-direction is calculated. x [(x,z)]; The spectrum of the horizontal gradient in the x-direction F[ΔT] x Performing an inverse Fourier transform on [x,z) yields the horizontal gradient ΔT in the x-direction. x .

4. The method according to claim 3, characterized in that, Based on the Fourier transform results of the airborne magnetic measurement data, the conversion factor of the z-direction gradient in the frequency domain, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i ,include: Based on the Fourier transform result F[ΔT(x,z)] of the airborne magnetic measurement data and the conversion factor of the z-direction gradient in the frequency domain, the spectrum F[ΔT] of the vertical gradient in the z-direction is obtained. z [(x,z)]; Based on the spectrum of the vertical gradient in the z-direction, F[ΔT] z [x,z] and high-frequency noise suppression factor The spectrum F′[ΔT] of the improved vertical gradient in the z-direction was calculated. z [(x,z)]; The spectrum of the improved z-direction vertical gradient F′[ΔT] z Performing an inverse Fourier transform on [x,z) yields the improved vertical gradient ΔT in the z-direction. z i .

5. The method according to claim 2, characterized in that, After acquiring the measured airborne magnetic survey data of the test area, and before performing a frequency domain Fourier transform on the airborne magnetic survey data, the method further includes: 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.

6. The method according to claim 5, characterized in that, Performing a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z) includes: Perform Fourier transform on the M levels of magnetic measurement data t; The horizontal gradient ΔT in the x-direction is obtained based on the Fourier transform result of the airborne magnetic measurement data and the conversion factor of the gradient in the x-direction of the frequency domain. x ,include: Based on the Fourier transform results of the M-level magnetic measurement data t and the conversion factor of the gradient in the x-direction of the frequency domain, the horizontal gradient Δt in the x-direction of the M-level magnetic measurement data t is determined. x ; Based on the weighted average of the M level complexities, M Δt values ​​are fused in the horizontal gradient along the x-direction. x The output is the horizontal gradient ΔT in the x-direction. x .

7. The method according to claim 5, characterized in that, Performing a frequency domain Fourier transform on the airborne magnetic measurement data ΔT(x,z) includes: Perform Fourier transform on the M levels of magnetic measurement data t; Based on the Fourier transform results of the airborne magnetic measurement data, the conversion factor of the z-direction gradient in the frequency domain, and the high-frequency noise suppression factor... The improved vertical gradient ΔT in the z-direction is obtained. z i include: Based on the results of the Fourier transform of the magnetic measurement data at M levels, the frequency domain z-direction gradient, and the high-frequency noise suppression factor... Determine the horizontal gradient Δt in the z-direction of the M levels of magnetic measurement data. z ; Based on the weighted fusion of the mean values ​​of the M level complexities, M Δt values ​​are applied to the horizontal gradient in the z-direction. z i The output is the horizontal gradient ΔT in the z-direction. z i .

8. The method according to claim 5, 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 grid 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.

9. A device for acquiring accurate airborne magnetic measurement data based on field source parameter imaging method, characterized in that, include: The acquisition module is used to acquire the measured aeromagnetic data of the test area; The Fourier transform module is used to perform Fourier transform in the frequency domain on the airborne magnetic measurement data. The horizontal gradient calculation module is used to obtain the horizontal gradient ΔT in the x-direction based on the Fourier transform results of the airborne magnetic data and the conversion factor of the x-direction gradient in the frequency domain. x ; The improved vertical gradient calculation module is used to construct the noise suppression factor. Based on the Fourier transform results of the airborne magnetic data, the conversion factor of the frequency domain z-direction gradient, and the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i Among them, the noise suppression factor is related to the inversion quality of underground magnetic bodies; The precise airborne magnetic data acquisition module is used to acquire data based on the horizontal gradient ΔT in the x-direction. x Improved vertical gradient ΔT in the z-direction z i To obtain accurate airborne magnetic measurement data; Among them, the horizontal gradient ΔT in the x-direction x Improved vertical gradient ΔT in the z-direction z i Obtaining accurate airborne magnetic survey data includes: Accurate airborne magnetic survey data are obtained using the following formula, which includes: Where max(ΔT) x ) represents ΔT x The maximum value, min(ΔT) x ) represents ΔT x The minimum value, max(ΔT) z i ) represents ΔT z i The maximum value, min(ΔT) z i ) represents ΔT z i The minimum value, ΔT x =F -1 {2πju·F[ΔT(x,z)]}, F[] represents the positive Fourier transform, F -1 [] denotes the inverse Fourier transform, j represents the imaginary number, and α represents the regularization adjustment factor, which takes values ​​from 0 to 1. Here, u is the noise suppression factor, and h is the frequency along the x-direction in the frequency domain. up This represents the height of the distance from the magnetic field observation surface.

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