Method for acquiring accurate aviation magnetic survey data based on analytic signal modulo method

Abstract

The invention discloses a method for acquiring accurate aviation magnetic measurement data based on an analytic signal modulo method. The method comprises the following steps: acquiring aviation magnetic measurement data T actually measured in a test area; calculating a horizontal gradient # imgabs0 # in the x direction, a horizontal gradient # imgabs1 # in the y direction and a vertical gradient # imgabs2 # in the z direction according to the aviation magnetic measurement data T; calculating an original analysis signal model ASM0 according to the horizontal gradient # imgabs3 # in the x direction, the horizontal gradient # imgabs4 # in the y direction and the vertical gradient # imgabs5 # in the z direction of the aviation magnetic measurement data T; calculating a weak signal enhancement function B according to the horizontal gradient # imgabs6 # of the aviation magnetic measurement data T in the x direction and the horizontal gradient # imgabs7 # of the aviation magnetic measurement data T in the y direction; and according to the original analytic signal module ASM0 and the weak signal enhancement function B, obtaining accurate aviation magnetic survey data. According to the method for obtaining the accurate aeromagnetic survey data based on the analytic signal modulo method, the transverse resolution capability of the geologic body boundary can be improved, so that the aeromagnetic data processing and conversion quality and the geologic body boundary recognition effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aeromagnetic surveying, and in particular to a method for obtaining accurate aeromagnetic surveying data based on an analytical signal model method. Background Art

[0002] Airborne magnetic survey is to install an airborne magnetometer system (such as optical pumping, nuclear spinning and fluxgate) on an aircraft to observe the geomagnetic field parameters (such as the total geomagnetic field intensity T or the total magnetic field anomaly △T or its gradient) to find magnetic or magnetic-related ore bodies in order to understand geological structure, conduct magnetic mapping, and solve problems such as urban and engineering stability and archaeology.

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

[0004] To improve the resolution of aeromagnetic anomalies and highlight more useful information, the analytical signal modeling method can be used to identify the edges of magnetic bodies. Existing technologies have the advantage of being minimally affected by the magnetic anomaly component and magnetization direction, but a disadvantage is the low lateral resolution of the identification results, which affects practical application effectiveness.

[0005] Therefore, in order to improve the quality of aeromagnetic data processing and conversion and the effect of geological body boundary identification, it is necessary to study and improve the ability of analytical signal modeling in the actual detection of geological body boundaries using aeromagnetic data.

[0006] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0007] To solve the above problems, the purpose of the embodiments of the present invention is to provide a method for obtaining accurate aeromagnetic data based on the analytical signal model method to improve the lateral resolution of geological body boundaries, thereby improving the quality of aeromagnetic data processing and conversion and the geological body boundary identification effect.

[0008] To achieve the above objectives, the present invention provides a method for obtaining accurate aeromagnetic data based on an analytical signal model method, comprising:

[0009] Obtain the measured aeromagnetic data T of the test area; calculate the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction According to the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytical signal mode ASM0; according to the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B; obtain accurate aeromagnetic data based on the original analytical signal mode ASM0 and the weak signal enhancement function B.

[0010] In one embodiment, after obtaining the measured aeromagnetic data T of the test area, the horizontal gradient of the aeromagnetic data T in the x direction is calculated. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Before that, it includes: pre-processing the obtained aerial magnetic survey data T measured in the test area.

[0011] In one embodiment, the preprocessing includes coordinate conversion, normal field correction, diurnal variation correction, hysteresis correction, and magnetic field level adjustment.

[0012] In one embodiment, the calculation of the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction The method comprises: calling geological body characteristic distribution data of the test area through an internet connection; evaluating the test area according to the geological body characteristic 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 mean identifiers; decomposing the pre-processed aeromagnetic data T with reference to the M hierarchical scale areas to obtain M hierarchical magnetic data t; determining the M horizontal gradients of the M hierarchical magnetic data t in the x-direction according to the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain; M horizontal gradients in the y direction M horizontal gradients in the z direction According to the weighted fusion of the M level complexity mean in the x direction, the M horizontal gradients Output horizontal gradient in the x direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the y direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the z direction

[0013] In one embodiment, the test area is evaluated based on the geological body characteristic distribution data, and the test area is hierarchically decomposed according to the evaluation results to obtain M hierarchical scale areas, including: dividing the test area into multiple grid areas based on a preset grid scale, using the multiple grid areas to divide the geological body characteristic distribution data to obtain multiple grid geophysical characteristic data; performing terrain complexity evaluation based on the multiple grid geological body characteristic data, and outputting multiple regional terrain complexities of the multiple grid areas; presetting a complexity threshold, and mapping and performing multiple rounds of merging of the multiple grid areas based on whether adjacent differences in the terrain complexities of the multiple areas meet the complexity threshold to obtain the M hierarchical scale areas.

[0014] In one embodiment, obtaining accurate aeromagnetic data according to the original analytical signal modulus ASM0 and the weak signal enhancement function B includes obtaining accurate aeromagnetic data according to the following formula, wherein:

[0015]

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

[0017] In one embodiment, the method further comprises: Calculate the output first level mean and first level variance; Arrange the M horizontal gradients in the x direction in ascending order To extract the first horizontal extreme value; and so on, perform M horizontal gradients in the y direction The data processing is to output the second level mean, the second level variance and the second level extreme value; and so on, to perform M horizontal gradients in the z direction. After interactively obtaining the average burial depth of the test area, the average burial depth, the first level average, the first level variance, the first level extreme value, the second level average, the second level variance and the second level extreme value, the third level average, the third level variance and the third level extreme value are input into a pre-built adjustment parameter disturbance model for disturbance analysis, and real-time adjustment parameters are output.

[0018] In one embodiment, it also includes: calling multiple groups of sample disturbance variables and multiple sample adjustment parameters online, wherein each group of sample disturbance variables includes sample burial depth, first sample mean, first sample variance, first sample extreme value, second sample mean, second sample variance, second sample extreme value, third sample mean, third sample variance and third sample extreme value; storing the multiple groups of sample disturbance variables and multiple sample adjustment parameters based on the knowledge graph association to complete the construction of the adjustment parameter disturbance model; inputting the average burial depth, first level mean, first level variance, first level extreme value, second level mean, second level variance, second level extreme value, third level mean, third level variance and third level extreme value into the adjustment parameter disturbance model, performing data similarity evaluation on the multiple groups of sample disturbance variables based on Euclidean distance ratio, and extracting the sample adjustment parameters corresponding to the similarity extreme values as real-time adjustment parameters.

[0019] To achieve the above object, the present invention also provides a device for obtaining accurate aeromagnetic data based on the analytical signal model method, comprising: an acquisition module for obtaining the aeromagnetic data T measured in the test area; a gradient calculation module for calculating the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction The original analytical signal mode calculation module is used to calculate the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytical signal mode ASM0; weak signal enhancement function calculation module, used to calculate the horizontal gradient in the x direction according to the aeromagnetic data T The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B; the precise aeromagnetic data acquisition module is used to obtain precise aeromagnetic data based on the original analytical signal model ASM0 and the weak signal enhancement function B.

[0020] To achieve the above objectives, the present invention further provides a storage medium storing computer-executable instructions for executing any one of the above-mentioned methods for obtaining accurate aeromagnetic data based on the analytical signal modeling method.

[0021] Compared with the prior art, the method, device and storage medium for obtaining accurate aeromagnetic data based on the analytical signal model method according to the present invention can better detect the boundaries of the target geological body in the aeromagnetic data, make the boundary recognition results more convergent, and solve the problem of low resolution of the existing analytical signal model method, thereby improving the quality of aeromagnetic data processing and conversion and the geological body boundary recognition effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A flow chart of a method for obtaining accurate aeromagnetic data based on an analytical signal modeling method provided by an embodiment of the present invention is shown;

[0024] Figure 2 A schematic structural diagram of a device for obtaining accurate aeromagnetic data based on an analytical signal modeling method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

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

[0026] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0027] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations such as "include" or "comprising", etc., will be understood to include the stated elements or components but not to exclude other elements or other components.

[0028] 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 the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0029] The embodiment of the present invention provides a flow chart of a method for obtaining accurate aeromagnetic data based on an analytical signal model method, see Figure 1 , including: step S1-step S4.

[0030] Step 1: Obtain the measured aeromagnetic data T of the test area.

[0031] The test area is analyzed for geological characteristics, such as burial depth, scale, and structural complexity, to determine the terrain complexity of the test area. This determines the distribution of terrain complexity, and then optimizes the flight trajectory based on this terrain complexity distribution, generating a dynamic flight trajectory. Raw magnetic data is then collected based on this dynamic flight trajectory. The spacing and altitude of the flight curves vary with terrain complexity. The aeromagnetic data, or T, or aeromagnetic 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.

[0032] In one implementation, after step S1 and before step S2, the method may further include preprocessing the acquired aeromagnetic data T measured in the test area to obtain preprocessed aeromagnetic data T measured in the test area. The preprocessing may include one or more of coordinate conversion, normal field correction, diurnal variation correction, hysteresis correction, and magnetic field level adjustment. Preprocessing can eliminate noise and errors in the aeromagnetic field measurement data caused by various factors.

[0033] In one implementation, the selection order of pre-processing may be, for example, coordinate transformation→normal field correction→diurnal variation correction→lag correction.

[0034] Furthermore, the above methods can be combined. For example, coordinate transformation and diurnal variation correction can be processed simultaneously in two containers. The registered spatial data can then be synchronized with the time-corrected magnetic field data to unify timestamps and location tags. This improves efficiency by parallelizing specific preprocessing steps and aligning and overlaying the results.

[0035] Step 2: Calculate the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction

[0036] In one implementation, step S2 may further include:

[0037] According to the correspondence between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the horizontal gradient of the pre-processed aeromagnetic data T in the x direction is determined. The horizontal gradient of the preprocessed aeromagnetic data T in the y direction and the vertical gradient in the z direction

[0038] The corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain may include:

[0039]

[0040] Specifically, the two-dimensional Hilbert transform has a Fourier domain transform factor of:

[0041] DH(u,v)=-i·sign(u,v),

[0042]

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

[0044]

[0045] Among them, F[], F -1 [] represent Fourier transform and inverse transform respectively; H x With H y Represent the components of the two-dimensional Hilbert transform in the x and y directions respectively.

[0046] Therefore, the horizontal gradient in the x direction Horizontal gradient in the y direction and the vertical gradient in the z direction It can be calculated using the Hilbert transform, and the two have an equivalent relationship:

[0047]

[0048] By leveraging the correspondence between the Hilbert and Fourier transforms, the Hilbert transform is used directly instead of the Fourier transform to calculate the gradients in the x, y, and z directions. Applying this equivalent relationship, i.e., replacing the Fourier transform with the Hilbert transform, has the advantage of not amplifying noise interference in the aeromagnetic data.

[0049] In one implementation, step S2 may further include:

[0050] The geological body characteristic distribution data of the test area is called online; the test area is evaluated according to the geological body characteristic distribution data, and the test area is hierarchically decomposed according to the evaluation result to obtain M hierarchical scale areas, wherein the M hierarchical scale areas have M hierarchical complexity mean identifiers; the pre-processed aeromagnetic data T is decomposed with reference to the M hierarchical scale areas to obtain M hierarchical magnetic data t; according to the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the horizontal gradient of the M hierarchical magnetic data t in the x direction is determined. M horizontal gradients in the y direction M horizontal gradients in the z direction According to the weighted fusion of the M level complexity mean in the x direction, the M horizontal gradients Output horizontal gradient in the x direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the Y direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the z direction

[0051] In one implementation, the test area is evaluated based on the geological body characteristic distribution data, and the test area is hierarchically decomposed according to the evaluation results to obtain M hierarchical scale areas, including:

[0052] After dividing the test area into multiple grid areas based on a preset grid scale, the multiple grid areas are used to divide the geological body characteristic distribution data to obtain multiple grid geological body characteristic data; terrain complexity evaluation is performed based on the multiple grid geological body characteristic data, and multiple regional terrain complexities of the multiple grid areas are output; a complexity threshold is preset, and based on whether adjacent differences in the terrain complexities of the multiple areas meet the complexity threshold, the multiple grid areas are mapped and merged multiple times to obtain the M hierarchical scale areas.

[0053] It should be understood that in complex geological structure scenarios, the gradient calculation at a single scale may have the problem of excessively high gradient values in strong magnetic anomaly areas, resulting in the neglect of deep weak gradients. Based on this, this embodiment decomposes the magnetic survey data into multiple scales to obtain geological body signals corresponding to different burial depths, and then performs the above horizontal gradient calculation on each scale layer to obtain the horizontal gradient of each scale layer, and further uses weight superposition to perform multi-scale gradient fusion.

[0054] Specifically, in this embodiment, geological body characteristic data covering the test area are called from the geological information platform. The geological body characteristic data include but are not limited to geological structure density information, lithology distribution information, burial depth characteristic information and historical geomagnetic data. The geological body characteristic distribution data provides basic geological background support for subsequent regional evaluation and hierarchical decomposition.

[0055] The test area is divided into multiple grid areas according to a preset grid scale (such as 1 km × 1 km), and then the obtained geological body characteristic distribution data are divided according to the grid area division to obtain multiple grid geophysical characteristic data.

[0056] The terrain complexity of each grid is calculated by inputting the geophysical characteristic data of the multiple grids into a quantitative model. The evaluation indicators of the quantitative model include structural density (based on the distribution density of fault zones or folds), lithologic complexity (reflecting the distribution heterogeneity of different lithologies), burial depth variation coefficient (characterizing the spatial variation degree of the burial depth of the geological body), and magnetic anomaly gradient (the first-order derivative of the magnetic field intensity is calculated in combination with historical geomagnetic data to describe the boundary characteristics of the magnetic body).

[0057] A complexity threshold is set as the criterion for merging adjacent grid regions. If the complexity difference between adjacent grid regions is below the threshold, they are merged into the same hierarchical scale region; otherwise, they remain as independent regions. Multiple rounds of merging algorithms dynamically iterate and adjust the adjacent grid regions, ultimately obtaining M hierarchical scale regions.

[0058] According to the grid area composition of each hierarchical scale area, several corresponding terrain complexities are extracted, the average is calculated, and the average is marked in the corresponding hierarchical scale area.

[0059] Further, referring to the M-level scale regional decomposition preprocessed aeromagnetic data T, M-level magnetic survey data t are obtained; according to the correspondence between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the M-level horizontal gradient of the M-level magnetic survey data t in the x direction is determined. M horizontal gradients in the y direction and the horizontal gradient M in the z direction According to the weighted fusion of the M level complexity mean in the x direction, the M horizontal gradients Output horizontal gradient in the x direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the y direction According to the weighted fusion of the M level complexity mean in the z direction, the M horizontal gradients Output horizontal gradient in the z direction

[0060] In step 1, the horizontal gradients M in the x direction of the M level magnetic measurement data t are determined according to the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain. M horizontal gradients in the y direction and the horizontal gradient M in the z direction In the frequency domain, the corresponding relationship between the two-dimensional Fourier transform and the Hilbert transform can include:

[0061]

[0062] Then, the M horizontal gradients in the x direction are weighted and fused according to the mean value of the complexity of the M levels. Output x-direction horizontal gradient According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output y-direction horizontal gradient According to the weighted fusion of the M level complexity mean in the z direction, the M horizontal gradients Output y-direction horizontal gradient

[0063] Step 3: According to the horizontal gradient of the aeromagnetic data T in the x direction And the horizontal gradient of the aeromagnetic data T in the y direction Calculate the original total horizontal gradient H xy .

[0064] Among them, the horizontal gradient It can be based on the weighted fusion of the M level complexity mean in the x direction horizontal gradient M The x-direction horizontal gradient output later Horizontal gradient It can be based on the weighted fusion of the M level complexity mean in the y direction. The Y-direction horizontal gradient output later

[0065] In this embodiment, the M weights of the M-level scale regions are calculated based on the M-level complexity mean values, which are used for weighted fusion calculation of the x-direction horizontal gradient. And the horizontal gradient in the Y direction

[0066] Step 3: According to the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytic signal modulus ASM0.

[0067] Among them, the horizontal gradient It can be based on the weighted fusion of the M level complexity mean in the x direction horizontal gradient M The x-direction horizontal gradient output later Horizontal gradient It can be based on the weighted fusion of the M level complexity mean in the y direction. The horizontal gradient in the y direction outputted later Horizontal gradient It can be based on the weighted fusion of the M level complexity mean in the z direction horizontal gradient M The z-direction horizontal gradient output later

[0068] In this embodiment, the M weights of the M-level scale regions are calculated based on the M-level complexity mean values, which are used for weighted fusion calculation of the x-direction horizontal gradient. Horizontal gradient in the y direction and the horizontal gradient in the z direction

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

[0070]

[0071] in, is the horizontal gradient of the aeromagnetic data T in the x direction, is the horizontal gradient of the aeromagnetic data T in the y direction, is the vertical gradient of the aeromagnetic data T in the z direction.

[0072] Step 4: Calculate the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B.

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

[0074]

[0075] It should be noted that the original analytical signal modulus ASM0 and the weak signal enhancement function can be calculated based on the gradient after weighted fusion according to the M level complexity means, or can be directly calculated based on the unfused gradient.

[0076] Step 5: Obtain accurate aeromagnetic data based on the original analytical signal mode ASM0 and the weak signal enhancement function B.

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

[0078]

[0079] in, Max(ASM0) represents the maximum value of the analytic signal modulus ASM0. T represents the measured aeromagnetic data, x, y, and z represent the three spatial coordinates, and Δ represents an adjustment parameter ranging from 0 to 1. Setting the adjustment parameter prevents the denominator from being zero and avoids "analytic singularities" in the expression.

[0080] In step 5, the weighted fusion gradient values obtained through M hierarchical scale areas are used for calculation, which can make the magnetic survey data more stable in complex geological structure scenarios.

[0081] In one implementation, the adjustment parameters can be obtained by: Calculate the output first level mean and first level variance; Arrange the M horizontal gradients in the x direction in ascending order To extract the first horizontal extreme value; and so on, perform M horizontal gradients in the y direction The data processing is to output the second level mean, the second level variance and the second level extreme value; and so on, to perform M horizontal gradients in the z direction. data processing, and outputting a third level mean, a third level variance and a third level extreme value; after interactively obtaining the average burial depth of the test area, the average burial depth, the first level mean, the first level variance, the first level extreme value, the second level mean, the second level variance, the second level extreme value, the third level mean, the third level variance and the third level extreme value are input into a pre-built adjustment parameter perturbation model for perturbation analysis, and outputting real-time adjustment parameters.

[0082] In one implementation, multiple groups of sample disturbance variables and multiple sample adjustment parameters are called online, wherein each group of sample disturbance variables includes sample burial depth, first sample mean, first sample variance, first sample extreme value, second sample mean, second sample variance, second sample extreme value, third sample mean, third sample variance and third sample extreme value; the multiple groups of sample disturbance variables and multiple sample adjustment parameters are stored in association with each other based on the knowledge graph to complete the construction of the adjustment parameter disturbance model; the average burial depth, first level mean, first level variance, first level extreme value, second level mean, second level variance, second level extreme value, third level mean, third level variance and third level extreme value are input into the adjustment parameter disturbance model, the multiple groups of sample disturbance variables are evaluated for data similarity based on Euclidean distance ratio, and the sample adjustment parameters corresponding to the similarity extreme values are extracted as real-time adjustment parameters.

[0083] Therefore, the method for acquiring accurate aeromagnetic data based on the analytical signal model provided in this embodiment can better detect the boundaries of target geological bodies in aeromagnetic data, resulting in more convergent boundary identification results. This solves the low resolution problem of existing analytical signal model methods, thereby improving the quality of aeromagnetic data processing and conversion and the effectiveness of geological body boundary identification. Furthermore, it eliminates the generation of false geological body boundary interference in aeromagnetic data, enhances the signal-to-noise ratio, and improves the ability to enhance and extract the boundaries of geological bodies at different burial depths, achieving higher resolution and accuracy.

[0084] As a result, the ability to enhance and extract the boundary positions of geological bodies at different burial depths is improved, with higher resolution and accuracy.

[0085] The analytical signal model expression provided in this embodiment is:

[0086]

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

[0088]

[0089] The analytical signal modulus expression provided in this embodiment addresses the problem of insufficient boundary enhancement for deeply buried sources using the analytical signal modulus. It proposes normalizing ASM0·B using the maximum value of the analytical signal modulus. This aims to limit the amplitude of aeromagnetic anomalies with large differences in strength to within a specific range, thereby balancing strong and weak aeromagnetic anomalies and avoiding distortion caused by excessively strong or weak magnetic anomaly signals, thereby improving the stability and noise immunity of the magnetic anomaly signals.

[0090] By introducing the weak signal enhancement function B and the Hilbert transform, a reasonable equalization filter is established, redefining the analytical signal model method. This method can better detect the boundaries of target geological bodies in aeromagnetic data, making the boundary recognition results more convergent. This solves the low resolution problem of the existing analytical signal model method and improves the boundary position enhancement and extraction capabilities of geological bodies at different burial depths, achieving higher resolution and accuracy.

[0091] The analytical signal modeling method provided in this embodiment is used to obtain the position of the geological target body in the aeromagnetic data, thereby further accurately inferring the boundary, depth, occurrence, scale, field distribution law and physical properties of the structural field source. This is of great significance for dividing tectonic units, conducting structural zoning, determining the location of fault structural belts, distinguishing the distribution of different lithologies and strata, and conducting physical property mapping.

[0092] An embodiment of the present invention further provides a device for acquiring accurate aeromagnetic data based on an analytical signal modulus method, comprising: an acquisition module 1, a gradient calculation module 2, an original analytical signal modulus calculation module 3, a weak signal enhancement function calculation module 4, and an accurate aeromagnetic data acquisition module 5.

[0093] Specifically, the acquisition module 1 is used to obtain the measured aeromagnetic data T of the test area. The gradient calculation module 2 is used to calculate the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction The original analytical signal mode calculation module 3 is used to calculate the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytical signal model ASM0. The weak signal enhancement function calculation module 4 is used to calculate the horizontal gradient of the aeromagnetic data T in the x direction according to the aeromagnetic data T. The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B. The precise aeromagnetic data acquisition module 5 is used to acquire precise aeromagnetic data according to the original analytical signal model ASM0 and the weak signal enhancement function B.

[0094] An embodiment of the present invention further provides a storage medium storing computer-executable instructions, which includes a program for executing the above-mentioned method for obtaining accurate aeromagnetic data based on the analytical signal model method. The computer-executable instructions can execute the method in any of the above-mentioned method embodiments.

[0095] In this way, the boundaries of the target geological bodies in the aeromagnetic data can be better detected, the boundary recognition results can be more converged, and the low resolution problem of the existing analytical signal model method can be solved, thereby improving the quality of aeromagnetic data processing and conversion and the geological body boundary recognition effect.

[0096] The storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid-state drives (SSDs), etc.).

[0097] 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 modifications or substitutions that can be easily conceived by a person 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 based on the scope of protection of the claims.

Claims

1. A method for obtaining accurate aeromagnetic data based on analytical signal modeling, characterized in that: include: Obtain the measured aeromagnetic data T of the test area; Calculate the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction According to the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytical signal modulus ASM0; According to the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B; Accurate aeromagnetic data are obtained based on the original analytical signal mode ASM0 and the weak signal enhancement function B.

2. The method according to claim 1, characterized in that After obtaining the measured aeromagnetic data T of the test area, the horizontal gradient of the aeromagnetic data T in the x direction is calculated. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Previously, the method also included: The obtained aeromagnetic data T measured in the test area are preprocessed.

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

4. The method according to claim 2, characterized in that Calculating the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction include: Retrieving the geological body characteristic distribution data of the test area through the Internet; The test area is evaluated according to the geological body characteristic distribution data, and the test area is hierarchically decomposed according to the evaluation result to obtain M hierarchical scale areas, wherein the M hierarchical scale areas have M hierarchical complexity mean identifiers; Referring to the M-level scale regional decomposition preprocessed aeromagnetic data T, M-level magnetic survey data t are obtained; According to the correspondence between the two-dimensional Fourier transform and the Hilbert transform in the frequency domain, the horizontal gradient M of the M-level magnetic measurement data t in the x direction is determined. M horizontal gradients in the y direction and the horizontal gradient M in the z direction According to the weighted fusion of the M level complexity mean in the x direction, the M horizontal gradients Output horizontal gradient in the x direction According to the weighted fusion of the M level complexity mean in the y direction, the M horizontal gradient Output horizontal gradient in the y direction According to the weighted fusion of the M level complexity mean in the z direction, the M horizontal gradients Output horizontal gradient in the z direction 5. The method according to claim 4, characterized in that The test area is evaluated based on the geological body characteristic distribution data, and the test area is hierarchically decomposed according to the evaluation results to obtain M hierarchical scale areas including: After dividing the test area into a plurality of grid areas based on a preset grid scale, the plurality of grid areas are used to divide the geological body characteristic distribution data to obtain a plurality of grid geological body characteristic data; Performing terrain complexity evaluation based on the plurality of grid geological body feature data, and outputting a plurality of regional terrain complexities of the plurality of grid regions; A complexity threshold is preset, and according to whether adjacent differences in the terrain complexity of the multiple regions meet the complexity threshold, multiple rounds of merging of the multiple grid regions are mapped to obtain the M hierarchical scale regions.

6. The method according to claim 1 or 4, characterized in that The method of obtaining accurate aeromagnetic data according to the original analytical signal model ASM0 and the weak signal enhancement function B includes: Accurate aeromagnetic data is obtained according to the following formula, which includes: in, Max(ASM0) represents the maximum value of the analytical signal modulus ASM0, T is the measured aeromagnetic data, x, y, and z are the three directions of the spatial coordinates, and Δ is the adjustment parameter, with a value of 0-1.

7. The method according to claim 6, characterized in that Also includes: According to the M horizontal gradients in the x direction Calculate the output first level mean and first level variance; Arrange M horizontal gradients in the x direction in ascending order To extract the first level extreme value; Similarly, perform M horizontal gradients in the y direction. Data processing, outputting the second level mean, second level variance and second level extreme value; Similarly, perform M horizontal gradients in the z direction. Data processing, output the third level mean, third level variance and third level extreme value; After interactively obtaining the average burial depth of the test area, the average burial depth, first level average, first level variance, first level extreme value, second level average, second level variance, second level extreme value, third level average, third level variance and third level extreme value are input into a pre-built adjustment parameter perturbation model for perturbation analysis, and real-time adjustment parameters are output.

8. The method according to claim 7, characterized in that Also includes: Calling multiple groups of sample disturbance variables and multiple sample adjustment parameters online, wherein each group of sample disturbance variables includes sample burial depth, first sample mean, first sample variance, first sample extreme value, second sample mean, second sample variance, second sample extreme value, third sample mean, third sample variance, and third sample extreme value; Associatively storing the multiple groups of sample disturbance variables and the multiple sample adjustment parameters based on the knowledge graph, and completing the construction of the adjustment parameter disturbance model; The average burial depth, first level mean, first level variance, first level extreme value, second level mean, second level variance, second level extreme value, third level mean, third level variance and third level extreme value are input into the adjustment parameter disturbance model, and the data similarity of the multiple groups of sample disturbance variables is evaluated based on the Euclidean distance ratio, and the sample adjustment parameters corresponding to the similarity extreme values are extracted as real-time adjustment parameters.

9. A device for obtaining accurate aeromagnetic data based on analytical signal modeling, characterized in that: include: An acquisition module is used to obtain the measured aeromagnetic data T of the test area; Gradient calculation module, used to calculate the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction The original analytical signal mode calculation module is used to calculate the horizontal gradient of the aeromagnetic data T in the x direction. The horizontal gradient of the aeromagnetic data T in the y direction and the vertical gradient of the aeromagnetic data T in the z direction Calculate the original analytical signal modulus ASM0; Weak signal enhancement function calculation module, used to calculate the horizontal gradient of the aeromagnetic data T in the x direction The horizontal gradient of the aeromagnetic data T in the y direction Calculate the weak signal enhancement function B; The precise aeromagnetic data acquisition module is used to obtain precise aeromagnetic data based on the original analytical signal model ASM0 and the weak signal enhancement function B.

10. A storage medium, characterized in that: The storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the method for obtaining accurate aeromagnetic data based on the analytical signal modeling method according to any one of claims 1 to 8.

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