Method for acquiring accurate aviation magnetic survey data based on field source parameter imaging method

Abstract

The invention discloses a method for acquiring accurate aviation magnetic measurement data based on a field source parameter imaging method. The method comprises the following steps: acquiring aviation magnetic measurement data actually measured in a test area; fourier transform in a frequency domain is carried out on the airborne magnetic survey data; obtaining an x-direction horizontal gradient according to the Fourier transform result of the airborne magnetic survey data and the x-direction gradient of the frequency domain; a noise suppression factor is constructed, an improved z-direction vertical gradient is obtained according to the Fourier transform result of the aeromagnetic survey data, the conversion factor of the frequency domain direction gradient and the noise suppression factor, and the noise suppression factor is related to the inversion quality of the underground magnetic body; and according to the x-direction horizontal gradient and the improved z-direction vertical gradient, obtaining accurate aviation magnetic survey data. According to the method for obtaining the accurate aviation magnetic survey data based on the field source parameter imaging method, the problem that an SPI field source parameter imaging method has an analysis singular point can be solved, and therefore the calculation stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aeromagnetic survey, and in particular to a method for obtaining accurate aeromagnetic survey data based on a field source parameter imaging method. Background Art

[0002] Airborne magnetometer 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 intensity of the geomagnetic field or the total magnetic field anomaly 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] Currently, the ability to resolve aeromagnetic anomalies can be improved through source parameter imaging (SPI).

[0005] Source Parameter Imaging (SPI) is unaffected by magnetization direction and is independent of magnetization intensity and magnetic inclination. It can quickly and automatically invert the characteristic points and burial depth of underground magnetic bodies. SPI uses the analytical signal composed of the first-order derivative of the magnetic field, along with its amplitude, phase angle, and the wavenumber (rate of change of the phase angle) of the first-order derivative analytical signal to calculate the corresponding magnetic body parameters and then plot the inversion results as an image.

[0006] The local wave number k of the first-order derivative analytical signal of the field source parameter imaging method 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 When calculating the vertical gradient in the z direction, the SPI field source parameter imaging method has an "analytic singularity", which makes the numerical calculation results unstable. It is susceptible to high-frequency noise interference, which reduces the inversion accuracy and affects the actual application effect.

[0009] 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

[0010] To address the above-mentioned issues, the present invention aims to provide a method and apparatus for obtaining accurate aeromagnetic data based on the SPI source parameter imaging method, thereby resolving the "analytic singularity" problem of the SPI source parameter imaging method and improving computational stability.

[0011] To achieve the above object, the present invention provides a method for obtaining accurate aeromagnetic data based on the SPI field source parameter imaging method, comprising: obtaining aeromagnetic data ΔT(x,z) measured in a test area; performing a frequency domain Fourier transform on the aeromagnetic data ΔT(x,z); obtaining the x-direction horizontal gradient ΔT based on the Fourier transform result of the aeromagnetic data and the conversion factor of the frequency domain x-direction gradient. x ; Construct a noise suppression factor, and obtain the improved z-direction vertical gradient ΔT based on the Fourier transform results of the aeromagnetic survey data and the conversion factor of the frequency domain z-direction gradient and the noise suppression factor z i , where 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 z-direction vertical gradient ΔT z i , to obtain accurate aeromagnetic data.

[0012] In one embodiment, after obtaining the measured aeromagnetic data of the test area and before performing the frequency domain Fourier transform on the aeromagnetic data, the method further includes: preprocessing the obtained aeromagnetic data measured in the test area.

[0013] In one embodiment, the x-direction horizontal gradient ΔT is obtained based on the Fourier transform result of the aeromagnetic survey data and the conversion factor of the x-direction gradient in the frequency domain. x , including: according to the Fourier transform result F[ΔT(x,z)] of the aeromagnetic survey data and the gradient conversion factor in the x direction of the frequency domain, the spectrum F[ΔT x (x,z)]; the spectrum of the horizontal gradient in the x direction F[ΔT x (x,z)] performs inverse Fourier transform to obtain the horizontal gradient ΔT in the x direction x .

[0014] In one embodiment, the improved z-direction vertical gradient ΔT is obtained based on the Fourier transform result of the aeromagnetic survey data and the conversion factor of the frequency domain z-direction gradient and the noise suppression factor. z i, including: according to the Fourier transform result F[ΔT(x,z)] of the aeromagnetic survey data and the conversion factor of the z-direction gradient in the frequency domain, the spectrum F[ΔT z (x,z)]; according to the spectrum of the vertical gradient in the z direction F[ΔT z (x,z)] and high-frequency noise suppression factor The improved spectrum F of the vertical gradient in the z direction is calculated ′ [ΔT z (x,z)], where h up is the height from the magnetic field observation surface; the spectrum F of the improved z-direction vertical gradient ′ [ΔT z (x,z)] is subjected to inverse Fourier transform to obtain the improved vertical gradient ΔT in the z direction z i .

[0015] In one embodiment, after obtaining the measured aeromagnetic data of the test area and before performing a frequency domain Fourier transform on the aeromagnetic data, the method further includes: calling the geological body characteristic distribution data of the test area via an Internet; evaluating the test area based on the geological body characteristic distribution data, and hierarchically decomposing the test area according to the evaluation results to obtain M hierarchical scale areas, wherein the M hierarchical scale areas have M hierarchical complexity mean identifiers; and decomposing the preprocessed aeromagnetic data T with reference to the M hierarchical scale areas to obtain M hierarchical magnetic data t.

[0016] In one embodiment, the frequency domain Fourier transform of the aeromagnetic data ΔT(x,z) is performed, including: performing Fourier transform on M levels of magnetic survey data t; obtaining the x-direction horizontal gradient ΔT based on the Fourier transform result of the aeromagnetic data and the conversion factor of the frequency domain x-direction gradient x , including: determining the horizontal gradient Δt in the x direction of the M-level magnetic survey data t according to the Fourier transform result of the M-level magnetic survey data t and the conversion factor of the frequency domain x-direction gradient x ; According to the weighted fusion of the M level complexity mean in the x direction horizontal gradient M Δt x , output the horizontal gradient ΔT in the x direction x .

[0017] In one embodiment, the aeromagnetic data ΔT(x,z) is subjected to a frequency domain Fourier transform, including: performing a Fourier transform on the M-level magnetic data t; and performing a frequency domain Fourier transform on the aeromagnetic data according to the conversion factor of the frequency domain z-direction gradient and the high-frequency noise suppression factor. The improved z-direction vertical gradient ΔT is obtained zi Including: the results of Fourier transform of M-level magnetic survey data and the frequency domain z-direction gradient and high-frequency noise suppression factor Determine the horizontal gradient Δt in the z direction of the M-level magnetic survey data z ; According to the weighted fusion of the M level complexity mean in the z direction horizontal gradient M Δt z i , output the horizontal gradient ΔT in the z direction z i .

[0018] 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.

[0019] In one embodiment, the horizontal gradient ΔT in the x direction x , improved z-direction vertical gradient ΔT z i , obtaining accurate aeromagnetic data includes: obtaining accurate aeromagnetic data according to the following formula, the formula including:

[0020]

[0021] Among them, 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 x and z are the two directions of the spatial coordinates, α represents the regularization adjustment factor, and its value is 0-1. is the noise suppression factor, u is the frequency along the x direction in the frequency domain, h up is the height from the magnetic field observation surface.

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

[0023] 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 field source parameter imaging method.

[0024] Compared with the prior art, the method, device and storage medium for obtaining accurate aeromagnetic data based on the field source parameter imaging method according to the present invention can solve the problem of "analytic singularity" existing in the SPI field source parameter imaging method, thereby improving the calculation stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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.

[0026] Figure 1 A flow chart of a method for obtaining accurate aeromagnetic data based on a field source parameter imaging method provided by an embodiment of the present invention is shown;

[0027] Figure 2 A schematic structural diagram of a device for acquiring precise aeromagnetic data based on a field source parameter imaging method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] 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.

[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 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.

[0032] The flowchart of the method for obtaining accurate aeromagnetic data based on the field source parameter imaging method provided by the embodiment of the present invention is shown in FIG. Figure 1 , including: step S1-step S5.

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

[0034] 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, ΔT(x,z), or the 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.

[0035] In one implementation, after step S1 and before step S2, the method may further include preprocessing the acquired aeromagnetic data ΔT(x,z) measured in the test area to obtain preprocessed aeromagnetic data 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 data caused by various factors.

[0036] In one implementation, the selection order of pre-processing may be, for example, coordinate transformation→normal field correction→diurnal variation correction→lag correction.

[0037] 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.

[0038] Step 2: Performing a frequency domain Fourier transform on the aeromagnetic data ΔT(x,z).

[0039] Specifically, the pre-processed aeromagnetic data may be expanded first, and then the expanded data may be subjected to a Fourier transform to obtain the frequency spectrum F[ΔT(x,z)] of ΔT(x,z).

[0040] It should be noted that the purpose of edge expansion is to meet the needs of Fourier transform and avoid data distortion at the boundaries. The Fourier transform assumes that the signal is an infinite periodic sequence. If there are sudden changes in the boundaries of the actual data (such as a sudden change in the edge value of aeromagnetic data), it will cause high-frequency artifacts in the spectrum. By expanding the edge, the number of frequency domain sampling points is increased to meet the nth power of 2, making the spectrum curve smoother and the details easier to observe. 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 results of the aeromagnetic data ΔT(x,z) and the conversion factor of the x-direction gradient in the frequency domain, the x-direction horizontal gradient ΔT is obtained. x .

[0042] Specifically, step 3 may include: calculating the spectrum F[ΔT x (x,z)]; the spectrum of the horizontal gradient in the x direction F[ΔT x (x,z)] performs inverse Fourier transform to obtain 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 gradient in the x and z directions of the frequency domain respectively to calculate the spectrum F[ΔT(x,z)] of the horizontal gradient in the x direction. x (x,z)] and the spectrum of the vertical gradient in the z direction F[ΔT z (x,z)]; for F[ΔT x (x, z)] performs inverse Fourier transform and edge reduction (i.e., deletes the edge expansion) calculation to obtain the horizontal gradient ΔT in the x direction x The x-direction gradient conversion factor is 2πju, ΔT x =F -1 {2πju·F[ΔT(x,z)]}.

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

[0045] Step 4: Construct a noise suppression factor. According to the Fourier transform results of the aeromagnetic data and the conversion factor of the frequency domain z-direction gradient, as well as the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i , where the noise suppression factor is related to the inversion quality of underground magnetic bodies.

[0046] In one implementation, the frequency spectrum F[ΔT(x,z) of the vertical gradient in the z direction is obtained based on the Fourier transform result of the aeromagnetic 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 According to the spectrum of the vertical gradient in the z direction F[ΔT z (x,z)] and high-frequency noise suppression factor The improved spectrum F of the vertical gradient in the z direction is calculated ′ [ΔT z (x,z)], where h up is the height from the magnetic field observation surface, which is determined according to the inversion quality of the underground magnetic body; the spectrum F of the improved vertical gradient in the z direction ′ [ΔT z (x,z)] is subjected to inverse Fourier transform to obtain the improved vertical gradient ΔT in the z direction z i .

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

[0048] When calculating the vertical gradient in the z direction, the existing technology has a high-frequency noise interference amplification effect. This embodiment constructs a high-frequency noise suppression factor Then calculate Suppress high-frequency noise interference to improve accuracy.

[0049] F[ΔT z (x, z)] to perform inverse Fourier transform and edge reduction (i.e., delete the edge expansion) calculation to obtain the vertical gradient ΔT in the z direction after high-frequency noise suppression. z i ; The gradient conversion factor in the z direction is 2π|u|.

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

[0051] Before step 5, the maximum horizontal gradient in the x direction can be obtained (ΔT x )、min(ΔT x ) and the maximum vertical gradient in the z direction max(ΔT z i )、min(ΔT z i ). That is, we get

[0052] Step 5: According to the horizontal gradient ΔT in the x direction x , improved z-direction vertical gradient ΔT z i , to obtain accurate aeromagnetic data.

[0053] According to the horizontal gradient ΔT in the x direction x , improved z-direction vertical gradient ΔT z i , obtaining accurate aeromagnetic data includes:

[0054] Accurate aeromagnetic data is obtained according to the following formula, which includes:

[0055]

[0056] Among them, the horizontal gradient in the x direction Vertical gradient in the z direction max and min represent the maximum and minimum values, 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 the spatial coordinates, α represents the regularization adjustment factor, and different values have an impact on the boundary recognition results; the value of α is 0-1. is the noise suppression factor, u is the frequency along the x direction in the frequency domain, h up It is the height from the magnetic field observation surface and is determined according to the inversion quality of the underground magnetic body.

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

[0058] Then, according to the local wave number κ of the analytical signal i Perform inversion to obtain accurate aeromagnetic data.

[0059] Therefore, the local wave number κ according to this embodiment i , breaking through the idea of defining the local wave number κ in the SPI field source parameter imaging calculation process, introducing operators to suppress high-frequency noise suppression factors and improve calculation stability, and reconstructing a new local wave number κ i The calculation expression formula solves the problems of "analytic singularity" and susceptibility to high-frequency noise interference in the original local wave number κ, and improves the calculation stability and the accuracy of the inversion results.

[0060] In one implementation, after step S1 and before step S2, the following steps may be further included:

[0061] The geological body characteristic distribution data of the test area is retrieved online; the test area is evaluated based on 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; and the preprocessed aeromagnetic data T is decomposed with reference to the M hierarchical scale areas to obtain M hierarchical magnetic data t.

[0062] Accordingly, step S2 may include: performing 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 survey data t according to the Fourier transform result of the M-level magnetic survey data t and the conversion factor of the frequency domain x-direction gradient x; According to the weighted fusion of the M level complexity mean in the x direction horizontal gradient M Δt x , output the horizontal gradient ΔT in the x direction x .

[0064] In step S4, the conversion factor of the frequency domain z-direction gradient and the high-frequency noise suppression factor are calculated based on the Fourier transform result of the aeromagnetic survey data T. The improved z-direction vertical gradient ΔT is obtained z i This can include:

[0065] According to the results of Fourier transform of M-level magnetic survey data and the frequency domain z-direction gradient and high-frequency noise suppression factor Determine the horizontal gradient Δt in the z direction of the M-level magnetic survey data z ; According to the weighted fusion of the M level complexity mean in the z direction horizontal gradient M Δt z i , output the horizontal gradient ΔT in the z direction z i .

[0066] In step 5, the weighted fusion ΔT obtained by M level scale regions is used x and ΔT z i Calculations can make the magnetic survey data more stable in complex geological structure scenarios.

[0067] 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:

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] 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.

[0075] In one implementation, the regularization adjustment factor can be obtained by: x Calculate and output the first level mean and first level variance; Arrange the horizontal gradients M Δt in the x direction in ascending order x , to extract the first horizontal extreme value; and so on, perform M vertical gradients in the z direction Δt z idata processing, and outputting the second level mean, 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 level mean, the first level variance, the first level extreme value, the second level mean, the second level variance and the second level extreme value are input into a pre-built regularization adjustment factor perturbation model for perturbation analysis, and a real-time regularization adjustment factor is output.

[0076] In one implementation, multiple groups of sample disturbance variables and multiple sample regularization adjustment factors 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 and second sample extreme value; the multiple groups of sample disturbance variables and multiple sample regularization adjustment factors are stored in association with each other based on the knowledge graph to complete the construction of the regularization adjustment factor disturbance model; the average burial depth, first level mean, first level variance, first level extreme value, second level mean, second level variance and second level extreme value are input into the regularization adjustment factor 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 regularization adjustment factor corresponding to the similarity extreme value is extracted as the real-time regularization adjustment factor.

[0077] This embodiment reconstructs the first-order local wave number κ based on the calculated gradient value i , the expression is:

[0078]

[0079] Compared with the original first-order local wave number κ expression:

[0080]

[0081] Therefore, according to the first-order local wave number κ of this embodiment i , breaking through the idea of defining the local wave number κ in the SPI field source parameter imaging calculation process, introducing operators to suppress high-frequency noise suppression factors and improve calculation stability, and reconstructing a new local wave number κ i The calculation expression formula solves the problems of "analytic singularity" and susceptibility to high-frequency noise interference in the original local wave number κ, and improves the calculation stability and the accuracy of the inversion results.

[0082] An embodiment of the present invention also provides a device for obtaining accurate aeromagnetic data based on a 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 aeromagnetic data acquisition module 5.

[0083] The acquisition module 1 is used to obtain the measured aeromagnetic data of the test area. The Fourier transform module 2 is used to perform a Fourier transform in the frequency domain on the aeromagnetic data. The horizontal gradient calculation module 3 is used to obtain the x-direction horizontal gradient ΔT based on the Fourier transform result of the aeromagnetic data and the conversion factor of the x-direction gradient in the frequency domain. x The improved vertical gradient calculation module 4 is used to construct the noise suppression factor. According to the Fourier transform results of the aeromagnetic survey data and the conversion factor of the frequency domain z-direction gradient, as well as the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i , wherein the noise suppression factor is related to the inversion quality of underground magnetic bodies. The precise aeromagnetic data acquisition module 5 is used to obtain the horizontal gradient ΔT in the x direction. x , improved z-direction vertical gradient ΔT z i , to obtain accurate aeromagnetic data.

[0084] 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 precise aeromagnetic data based on the field source parameter imaging method. The computer-executable instructions can execute the method in any of the above-mentioned method embodiments.

[0085] 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.).

[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 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 field source parameter imaging, characterized in that: include: Obtain the measured aeromagnetic data ΔT(x,z) of the test area; Performing a frequency domain Fourier transform on the aeromagnetic data ΔT(x,z); According to the Fourier transform results of the aeromagnetic survey data and the conversion factor of the x-direction gradient in the frequency domain, the x-direction horizontal gradient ΔT is obtained. x ; The noise suppression factor is constructed. According to the Fourier transform results of the aeromagnetic survey data and the conversion factor of the z-direction gradient in the frequency domain, as well as the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i , where 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 z-direction vertical gradient ΔT z i , to obtain accurate aeromagnetic data.

2. The method according to claim 1, characterized in that After obtaining the aeromagnetic data actually measured in the test area, and before performing Fourier transform in the frequency domain on the aeromagnetic data, the method further includes: preprocessing the aeromagnetic data actually measured in the test area.

3. The method according to claim 2, characterized in that According to the Fourier transform results of the aeromagnetic survey data and the frequency domain x-direction gradient, the x-direction horizontal gradient ΔT is obtained. x ,include: According to the Fourier transform result F[ΔT(x,z)] of the aeromagnetic data and the gradient conversion factor in the x direction of the frequency domain, the spectrum of the horizontal gradient in the x direction F[ΔT x (x,z)]; The spectrum of the horizontal gradient in the x direction F[ΔT x (x,z)] performs inverse Fourier transform to obtain the horizontal gradient ΔT in the x direction x .

4. The method according to claim 3, characterized in that According to the Fourier transform results of the aeromagnetic survey data and the conversion factor of the z-direction gradient in the frequency domain, as well as the noise suppression factor, the improved z-direction vertical gradient ΔT is obtained. z i ,include: According to the Fourier transform result F[ΔT(x,z)] of the aeromagnetic data and the conversion factor of the z-direction gradient in the frequency domain, the spectrum of the vertical gradient in the z-direction F[ΔT z (x,z)]; According to the spectrum of the vertical gradient in the z direction F[ΔT z (x,z)] and high-frequency noise suppression factor The improved spectrum F of the vertical gradient in the z direction is calculated ′ [ΔT z (x,z)], where h up is the height from the magnetic field observation surface; The spectrum F of the improved vertical gradient in the z direction ′ [ΔT z (x,z)] is subjected to inverse Fourier transform to obtain the improved vertical gradient ΔT in the z direction z i .

5. The method according to claim 2, characterized in that After obtaining the measured aeromagnetic data of the test area, and before performing a frequency domain Fourier transform on the aeromagnetic data, the method further includes: 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; With reference to the M-level scale regional decomposition preprocessed aeromagnetic data T, M-level magnetic survey data t are obtained.

6. The method according to claim 5, characterized in that Performing a frequency domain Fourier transform on the aeromagnetic data ΔT(x,z), comprising: Perform Fourier transform on the M-level magnetic survey data t; The x-direction horizontal gradient ΔT is obtained based on the Fourier transform result of the aeromagnetic survey data and the conversion factor of the x-direction gradient in the frequency domain. x ,include: Determine the horizontal gradient Δt of the M-level magnetic survey data t in the x-direction according to the Fourier transform result of the M-level magnetic survey data t and the conversion factor of the frequency domain x-direction gradient. x ; According to the weighted fusion of the M level complexity mean in the x direction, the horizontal gradient M Δt x , output 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 aeromagnetic data ΔT(x,z), comprising: Perform Fourier transform on the M-level magnetic survey data t; According to the results of Fourier transform of aeromagnetic survey data and the conversion factor of z-direction gradient in frequency domain, as well as the high-frequency noise suppression factor The improved z-direction vertical gradient ΔT is obtained z i include: According to the results of Fourier transform of M-level magnetic survey data and the frequency domain z-direction gradient and high-frequency noise suppression factor Determine the horizontal gradient Δt in the z direction of the M-level magnetic survey data z ; According to the weighted fusion of the M level complexity mean in the z direction, the horizontal gradient M Δt z i , output the horizontal gradient ΔT in the z direction z i .

8. The method according to claim 5, 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.

9. The method according to claim 1 or 4, characterized in that According to the horizontal gradient ΔT in the x direction x , improved z-direction vertical gradient ΔT z i , obtaining accurate aeromagnetic data includes: Accurate aeromagnetic data is obtained according to the following formula, which includes: Among them, 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 x and z are the two directions of the spatial coordinates, α represents the regularization adjustment factor, and its value is 0-1. is the noise suppression factor, u is the frequency along the x direction in the frequency domain, h up is the height from the magnetic field observation surface.

10. A device for obtaining accurate aeromagnetic data based on field source parameter imaging method, characterized in that: include: An acquisition module is used to obtain the measured aeromagnetic data of the test area; Fourier transform module, used to perform Fourier transform in the frequency domain on the aeromagnetic data The horizontal gradient calculation module is used to obtain the x-direction horizontal gradient ΔT based on the Fourier transform results of the aeromagnetic survey 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. According to the Fourier transform results of the aeromagnetic survey data and 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 , where the noise suppression factor is related to the inversion quality of underground magnetic bodies; Accurate aeromagnetic data acquisition module, used to obtain the horizontal gradient ΔT in the x-direction x , improved z-direction vertical gradient ΔT z i , to obtain accurate aeromagnetic data.

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