Magnetic anomaly boundary information identification method and system based on magnetic gradient tensor

Abstract

The invention discloses a magnetic gradient tensor-based magnetic anomaly boundary information identification method and system, and relates to the technical field of magnetic anomaly data processing, and the method comprises the steps: collecting actual measurement magnetic anomaly data, and carrying out the preprocessing of the data, and obtaining the grid magnetic anomaly data after the pole transformation; carrying out calculation on the basis of the grid magnetic anomaly data after pole conversion to obtain magnetic gradient tensor component data; performing boundary identification calculation on the magnetic gradient tensor component data to obtain a boundary identification calculation result; and identifying geologic body boundary information by using the boundary identification calculation result. By introducing the magnetic gradient tensor data, the suppression effect on noise interference is stronger, so that the influence of oblique magnetization on the geologic body boundary identification result is small, and the boundary information of the geologic body can be identified more accurately; the geologic body boundary is identified by using the extreme value information of the boundary identification calculation result, the boundary information is highlighted, the accuracy of the identification result is ensured, and the lateral resolution of the identification result is improved.

Description

Technical Field

[0001] The invention relates to the technical field of magnetic anomaly data processing, and in particular to a method and system for identifying magnetic anomaly boundary information based on magnetic gradient tensor. Background Art

[0002] As a comprehensive reflection of the uneven magnetic distribution of geological bodies, magnetic anomalies have unique advantages in inferring and identifying the boundary positions of geological bodies, especially the advantage of high lateral resolution. Before the geological interpretation of magnetic anomaly data, a series of processing and conversion of magnetic anomaly data is usually required to accurately identify the boundary information of geological bodies. These processing is crucial for accurately identifying the boundary information of geological bodies and is the basis for high-quality geological interpretation. However, traditional magnetic anomaly processing and conversion methods have many limitations, which seriously restrict the application effect of magnetic anomaly data in geological interpretation. First, although conventional filtering methods can improve signal quality to a certain extent, they often lead to blurred anomaly boundaries, significantly reduce the recognition accuracy of linear structural edges, and make it difficult to accurately locate the boundaries of geological bodies. Second, these methods have relatively weak noise suppression capabilities. In actual exploration, magnetic anomaly data are often affected by various noises, such as instrument noise or environmental noise. In the case of low signal-to-noise ratio, it is easy to cause the loss of useful information. Especially in areas with complex geological conditions, the intensity of abnormal signals is often weak and more easily masked by noise, making the interpretation of magnetic anomaly data extremely difficult, which may cause important geological information to be incorrectly filtered out or masked by noise.

[0003] However, the common solutions currently available have many shortcomings, including: the existing methods for identifying magnetic anomaly boundary information are mainly divided into mathematical statistics and derivative analysis; among them, mathematical statistics methods, such as normalized standard deviation and small subdomain filtering, although they can adjust the noise suppression effect and boundary resolution by changing the window size, it is difficult to take both into account at the same time; although large windows have a good noise suppression effect, they will seriously reduce the boundary resolution; although small windows retain a high resolution, they have a poor noise suppression effect; derivative analysis methods mainly focus on the changing characteristics of the derivatives of abnormal data, which can improve the lateral resolution to a certain extent, but will also significantly amplify noise interference, and their application in complex geological environments is limited. Summary of the invention

[0004] The present invention is proposed in view of the fact that it is difficult to simultaneously take into account the noise suppression effect and boundary resolution when identifying boundary information of magnetic anomaly data in the existing technology. While improving the lateral resolution to a certain extent, the noise interference will be significantly amplified, resulting in the accuracy of boundary information identification being affected.

[0005] Therefore, the problem to be solved by the present invention is how to provide a method for suppressing the noise of magnetic anomaly signals and improving the resolution of magnetic anomaly body boundary information, introducing magnetic gradient tensor component data, utilizing the characteristics of magnetic gradient tensor data that are not affected by oblique magnetization, have a strong effect on suppressing noise interference, and have obvious directional information, and highlighting the boundary information by calculation to more accurately determine the boundary of the geological body.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor, which includes collecting measured magnetic anomaly data and performing preprocessing to obtain gridded magnetic anomaly data after polarization; performing calculations based on the gridded magnetic anomaly data after polarization to obtain magnetic gradient tensor component data; performing boundary identification calculations on the magnetic gradient tensor component data to obtain boundary identification calculation results; and using the boundary identification calculation results to identify geological body boundary information.

[0008] As a preferred solution of the magnetic anomaly boundary information identification method based on magnetic gradient tensor described in the present invention, wherein: the obtaining of the gridded magnetic anomaly data after polarization includes gridding and polarization processing of the measured magnetic anomaly data.

[0009] As a preferred solution of the magnetic anomaly boundary information identification method based on the magnetic gradient tensor of the present invention, the magnetic gradient tensor component data is enhanced by using the boundary identification calculation, and the calculation formula of the boundary identification calculation is as follows:

[0010]

[0011] Where L is the boundary recognition calculation result, that is, the two-dimensional data matrix containing the boundary information of the geological body; B xx B is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; B zz It is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component.

[0012] As a preferred solution of the magnetic anomaly boundary information identification method based on magnetic gradient tensor of the present invention, wherein: the identification of the boundary information of the geological body includes visualizing the boundary identification calculation result by using MATLAB or Surfer program to generate a plane contour map, and extracting the boundary information of the geological body by using the extreme value information of the boundary identification calculation result to identify the boundary of the geological body; the extraction of the boundary information of the geological body by using the extreme value information of the boundary identification calculation result L includes: judging the boundary identification calculation result L in the plane contour map, if the boundary identification calculation result L of the point P is P >Boundary recognition calculation result L on the left P左 and the boundary recognition calculation result L on the right P右 , then the boundary recognition calculation result L of the decision point P P is an extreme value, point P is an extreme value point and is recorded; if the boundary recognition calculation result L of point P P > Upper boundary recognition calculation result L P上 and the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P is an extreme value, point P is an extreme value point and is recorded; if the boundary recognition calculation result L of point P P ≤ the left boundary recognition calculation result L P左 Or the boundary recognition calculation result L on the right P右 , and the boundary recognition calculation result of point P is L P ≤ Upper boundary recognition calculation result L P上 Or the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P If it is not an extreme value, point P is not an extreme value point and is not recorded; the recorded extreme value points are combined to obtain the extreme value information of the boundary recognition calculation result L.

[0013] As a preferred solution of the magnetic anomaly boundary information identification method based on magnetic gradient tensor of the present invention, the specific formula of the extreme value information of the boundary identification calculation result is as follows:

[0014] [L(i,j)≥L(i+1,j)∩L(i,j)≥L(i-1,j)]∪[L(i,j)≥L(i,j+1)∩L(i,j)≥L(i,j-1)]

[0015] Wherein, L(i,j) is the boundary identification calculation result L of point (i,j); i is the horizontal coordinate; j is the vertical coordinate; the boundary of the geological body is identified by connecting all the extreme points of the boundary identification calculation result L into several closed or semi-closed curves according to the principle of proximity, and the corresponding curves are equivalently regarded as the boundaries of the geological body.

[0016] As a preferred solution of the magnetic anomaly boundary information identification method based on magnetic gradient tensor described in the present invention, the obtaining of magnetic gradient tensor component data includes the following steps: performing edge expansion calculation on the gridded magnetic anomaly data after polarization to obtain the magnetic anomaly data after edge expansion; performing frequency domain Fourier conversion on the magnetic anomaly data after edge expansion to obtain magnetic gradient tensor component data; the edge expansion calculation includes the upper area, the lower area, the left area and the right area.

[0017] As a preferred solution of the magnetic anomaly boundary information identification method based on magnetic gradient tensor of the present invention, the calculation formula of the magnetic gradient tensor component data is as follows:

[0018]

[0019] Among them, B xx is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; S xx (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion xx Spectrum of component; B yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; S yy (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion yy Spectrum of component; B zz is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component; S zz (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion zz The spectrum of the component; u is the angular frequency of the horizontal coordinate x; v is the angular frequency of the vertical coordinate y; i is the imaginary unit; x is the horizontal coordinate value; y is the vertical coordinate value.

[0020] In the second aspect, in order to further solve the security problems existing in the processing of magnetic anomaly data, the present invention provides a magnetic anomaly boundary information identification system based on the magnetic gradient tensor in an embodiment, which includes: a data processing module, which is used to collect measured magnetic anomaly data and perform gridding processing and polarization processing to obtain gridded magnetic anomaly data after polarization; a component calculation module, which is used to perform edge expansion calculation and frequency domain Fourier conversion on the gridded magnetic anomaly data after polarization to obtain magnetic gradient tensor component data; an identification calculation module, which is used to perform boundary identification calculation on the magnetic gradient tensor component data to obtain a two-dimensional data matrix containing geological body boundary information; a boundary identification module, which is used to visualize the two-dimensional data matrix, and use the extreme value information of the two-dimensional data matrix to extract the boundary information of the geological body and identify the boundary of the geological body.

[0021] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor as described in the first aspect of the present invention is implemented.

[0022] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor as described in the first aspect of the present invention is implemented.

[0023] The invention has the following beneficial effects: the invention introduces magnetic gradient tensor data for the purpose of suppressing magnetic anomaly signal noise and improving the resolution of magnetic anomaly body boundary information, thereby having a stronger suppression effect on noise interference, making the geological body boundary identification result less affected by oblique magnetization, and being able to more accurately identify the boundary information of the geological body; by gridding and polarization processing the magnetic anomaly data, the data has a unified spatial distribution format, more accurately reflects the magnetic distribution of underground geological bodies, and lays a data foundation for subsequent precise analysis; by using the boundary identification calculation method to comprehensively analyze the three main components of the magnetic gradient tensor, the sensitivity and resolution of boundary identification are significantly improved, and weak boundary features that are difficult to identify by conventional methods can be captured; by using the extreme value information of the boundary identification calculation result to identify the geological body boundary, the boundary information is highlighted, the accuracy of the identification result is guaranteed, and the lateral resolution of the identification result is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative work. Among them:

[0025] Figure 1 This is a flow chart of a method for identifying magnetic anomaly boundary information based on magnetic gradient tensor in Example 1.

[0026] Figure 2 This is a schematic diagram of the rectangular parallelepiped model structure in Example 2.

[0027] Figure 3 This is the plane contour map of the forward magnetic anomaly of the rectangular model in Example 2.

[0028] Figure 4 This is a diagram of the calculation results of the rectangular model boundary recognition in Example 2.

[0029] Figure 5This is a schematic diagram of the structure of the combined rectangular parallelepiped model in Example 2.

[0030] Figure 6 This is the plane contour map of the forward magnetic anomaly of the combined rectangular model in Example 2.

[0031] Figure 7 This is a diagram of the calculation results of the combined rectangular model boundary recognition in Example 2. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1

[0036] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for identifying magnetic anomaly boundary information based on magnetic gradient tensor, comprising the following steps:

[0037] S1: Collect measured magnetic anomaly data and preprocess them to obtain gridded magnetic anomaly data after polarization.

[0038] Specifically, Figure 1 The present invention shows the implementation process of magnetic anomaly boundary information identification. The measured magnetic anomaly data is collected and gridded and polarized to obtain the polarized gridded magnetic anomaly data. The cosine formula is then used to perform edge expansion calculations on the polarized gridded magnetic anomaly data from four regions, namely, the upper, lower, left, and right, to obtain the magnetic anomaly data after edge expansion and perform frequency domain Fourier conversion to obtain magnetic gradient tensor component data. Boundary identification calculations are performed based on the magnetic gradient tensor component data to obtain boundary identification calculation results. Finally, the boundary identification calculation results are visualized to generate a plane contour map. The boundary information of the geological body is extracted using the extreme value information of the boundary identification calculation results to identify the boundary of the geological body.

[0039] Preferably, obtaining the gridded magnetic anomaly data after polarization includes performing gridding processing and polarization processing on the measured magnetic anomaly data.

[0040] Furthermore, gridding is used to convert irregularly distributed magnetic anomaly data into a standard grid, which is convenient for subsequent calculation and analysis. When gridding is performed, for any node on the regular grid after gridding, the specific formula for the corresponding magnetic anomaly value is as follows:

[0041]

[0042] Where T is the magnetic anomaly value of a node on the regular grid after gridding; n is the number of known magnetic anomaly points closest to the corresponding node; T i is the magnetic anomaly value of the n known magnetic anomaly points closest to the corresponding node; S i is the distance from n known magnetic anomaly points to the corresponding grid nodes, where i = 1, 2, 3, ..., n.

[0043] Furthermore, polarization processing is used to eliminate the influence of the geomagnetic field inclination on the magnetic anomaly data, so that the magnetic anomaly data can directly reflect the magnetic distribution of the underground geological body. The specific formula for polarization in the frequency domain is as follows:

[0044]

[0045] Among them, S(u,v) is the frequency spectrum of the frequency domain magnetic anomaly after Fourier conversion; Th(x,y) is the gridded magnetic anomaly data; i is the imaginary unit; x is the horizontal coordinate value; y is the vertical coordinate value; u is the angular frequency of the horizontal coordinate x; v is the angular frequency of the vertical coordinate y; S T (u,v) is the frequency spectrum of the polar magnetic anomaly after Fourier conversion; I is the magnetic inclination; A is the magnetic declination; T(x,y) is the gridded magnetic anomaly data after polarization.

[0046] Preferably, the measured magnetic anomaly data is gridded to ensure the spatial consistency and comparability of the data, which is convenient for subsequent analysis; the influence of the geomagnetic field inclination on the identification results is eliminated by using the polarization processing, so that the gridded magnetic anomaly data after polarization can directly reflect the magnetic distribution of underground geological bodies, thereby improving the accuracy of subsequent boundary identification; by combining the gridding processing and the polarization processing, the initial data quality of boundary identification is significantly improved, laying a more reliable data foundation for subsequent boundary identification.

[0047] S2: Calculate based on the gridded magnetic anomaly data after polarization to obtain the magnetic gradient tensor component data.

[0048] Preferably, obtaining the magnetic gradient tensor component data includes the following steps: performing an expansion calculation on the gridded magnetic anomaly data after polarization to obtain the expanded magnetic anomaly data, wherein the magnetic anomaly data is divided into four regions of up, down, left and right along the horizontal and vertical coordinate directions, and performing an expansion calculation on each region using the cosine formula, thereby ensuring the data accuracy of the frequency domain Fourier conversion.

[0049] The frequency domain Fourier conversion is performed on the expanded magnetic anomaly data to obtain the magnetic gradient tensor component data. The calculation formula for the frequency domain Fourier conversion is as follows:

[0050]

[0051] Among them, S(u,v) is the frequency spectrum of the frequency domain magnetic anomaly after Fourier conversion; T(x,y) is the gridded magnetic anomaly data after polarization; i is the imaginary unit; u is the angular frequency of the horizontal coordinate x; v is the angular frequency of the vertical coordinate y; x is the horizontal coordinate value; y is the vertical coordinate value; S xx (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion xx Spectrum of the component; S yy (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion yy Spectrum of the component; S zz (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion zz Component spectrum; I is the magnetic inclination; A is the magnetic declination.

[0052] Furthermore, the edge expansion calculation includes an upper area, a lower area, a left area, and a right area.

[0053] Specifically, the cosine expansion calculation formula of the upper area is as follows:

[0054]

[0055] Among them, T1(i,j) is the magnetic anomaly data after cosine expansion in the upper area; T(i,1) is the first row of gridded magnetic anomaly data; n' is the number of rows in the upper and lower expanded areas; i is the row number; j is the column number; m is the number of columns of gridded magnetic anomaly data after polarization.

[0056] Specifically, the cosine expansion calculation formula for the lower area is as follows:

[0057]

[0058] Among them, T2(i,j) is the magnetic anomaly after cosine expansion in the lower area; T(i,n) is the last row of gridded magnetic anomaly data; i is the row number; j is the column number; n is the number of rows of gridded magnetic anomaly data after polarization; m is the number of columns of gridded magnetic anomaly data after polarization; n' is the number of rows in the upper and lower expanded areas.

[0059] Specifically, the cosine expansion calculation formula for the left area is as follows:

[0060]

[0061] Among them, T3(i,j) is the magnetic anomaly after cosine expansion in the left area; T(1,j) is the gridded magnetic anomaly data in the leftmost column; i is the row number; j is the column number; n is the number of rows of the gridded magnetic anomaly data after polarization; n' is the number of rows in the upper and lower expanded areas; m is the number of columns of the gridded magnetic anomaly data after polarization; m' is the number of columns in the left and right expanded areas.

[0062] Specifically, the cosine expansion calculation formula for the right area is as follows:

[0063]

[0064] Among them, T4(i,j) is the magnetic anomaly after cosine expansion in the right area; T(m,j) is the gridded magnetic anomaly data in the rightmost column; i is the row number; j is the column number; m is the number of columns of gridded magnetic anomaly data after polarization; m' is the number of columns in the expanded area on the left and right sides.

[0065] Preferably, by expanding the edges of the gridded magnetic anomaly data after polarization and using the cosine formula to process the four areas separately, unlike the traditional simple copying or linear extrapolation, a smoother and more natural data extension is achieved through the cosine function, avoiding the artificial discontinuity that may be introduced by the traditional method; effectively reducing the influence of the edge effect on the frequency domain Fourier transform, improving the data accuracy of the frequency domain Fourier transform, and enhancing the reliability of boundary identification and the accuracy of subsequent magnetic gradient tensor component calculations.

[0066] Furthermore, the calculation formula of the magnetic gradient tensor component data is as follows:

[0067]

[0068] Among them, B xx is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; S xx (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion xx Spectrum of component; B yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; Syy (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion yy Spectrum of component; B zz is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component; S zz (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion zz The spectrum of the component; u is the angular frequency of the horizontal coordinate x; v is the angular frequency of the vertical coordinate y; i is the imaginary unit; x is the horizontal coordinate value; y is the vertical coordinate value.

[0069] Preferably, the magnetic anomaly data in the spatial domain is converted into the frequency domain by utilizing the frequency domain Fourier conversion, so that the various components of the magnetic gradient tensor can be calculated more accurately. Compared with the traditional spatial domain difference method, it has higher accuracy and stability, and can better capture the boundary characteristics of the geological body; it can better preserve the subtle features of the geological body boundary, improve the sensitivity of subsequent boundary identification, and reduce the error accumulation in numerical calculations.

[0070] S3: Perform boundary recognition calculation on the magnetic gradient tensor component data to obtain boundary recognition calculation results.

[0071] Preferably, the magnetic gradient tensor component data is enhanced by using boundary recognition calculation to enhance the boundary signal of the geological body and suppress background noise. The calculation formula of the boundary recognition calculation is as follows:

[0072]

[0073] Where L is the boundary recognition calculation result, that is, the two-dimensional data matrix containing the boundary information of the geological body; B xx B is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; B zz It is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component.

[0074] Preferably, by cleverly utilizing the characteristics of the magnetic gradient tensor and comprehensively considering the multiple components of the magnetic gradient tensor, the obtained magnetic gradient tensor component data is further enhanced, thereby effectively enhancing the boundary signal of the geological body, while suppressing background noise and improving the signal-to-noise ratio of recognition; different from the traditional single gradient or total gradient method, it can more comprehensively capture the boundary characteristics of the geological body and identify weak boundaries that may be ignored by traditional methods; it has stronger adaptability to complex geological structures and can more accurately depict the boundaries of irregularly shaped geological bodies.

[0075] S4: Identify the boundary information of the geological body using the boundary recognition calculation results.

[0076] Preferably, identifying the boundary information of the geological body includes visualizing the boundary identification calculation result L using MATLAB or Surfer program to generate a plane contour map, and extracting the boundary information of the geological body using the extreme value information of the boundary identification calculation result L to identify the boundary of the geological body.

[0077] Further, using the extreme value information of the boundary recognition calculation result L to extract the boundary information of the geological body includes: judging the boundary recognition calculation result L in the plane contour map, if the boundary recognition calculation result L of the point P P >Boundary recognition calculation result L on the left P左 and the boundary recognition calculation result L on the right P右 , then the boundary recognition calculation result L of the decision point P P is the extreme value, point P is the extreme value point and is recorded.

[0078] If the boundary recognition result L of point P P > Upper boundary recognition calculation result L P上 and the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P is the extreme value, point P is the extreme value point and is recorded.

[0079] If the boundary recognition result L of point P P ≤ the left boundary recognition calculation result L P左 Or the boundary recognition calculation result L on the right P右 , and the boundary recognition calculation result of point P is L P ≤ Upper boundary recognition calculation result L P上 Or the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P It is not an extreme value. Point P is not an extreme value point and is not recorded.

[0080] The recorded extreme value points are combined to obtain the extreme value information of the boundary recognition calculation result L.

[0081] Specifically, the specific formula for the extreme value information of the boundary recognition calculation result L is as follows:

[0082] [L(i,j)≥L(i+1,j)∩L(i,j)≥L(i-1,j)]∪[L(i,j)≥L(i,j+1)∩L(i,j)≥L(i,j-1)]

[0083] Wherein, L(i,j) is the boundary recognition calculation result L of point (i,j); i is the horizontal coordinate; j is the vertical coordinate.

[0084] Furthermore, the boundary of the geological body is identified based on the proximity principle, and all extreme points of the boundary identification calculation results L are connected into several closed or semi-closed curves, wherein the corresponding curves are equivalently regarded as the boundary of the geological body.

[0085] Preferably, by making accurate extreme value judgment on the boundary signal of the enhanced geological body, that is, the boundary recognition calculation result, the corresponding extreme value information is identified, and then the boundary information of the geological body is extracted using the extreme value information of the boundary recognition calculation result, and the extreme value points are connected into a closed or semi-closed curve to enhance the accuracy of the extracted boundary information. Compared with the traditional threshold method or edge detection method, the present invention can more accurately depict the complex geological body boundary and improve the accuracy and efficiency of boundary recognition; improve the adaptability to anomalies of different intensities and can simultaneously identify geological body boundaries of different strengths; by using extreme values ​​for judgment, the subjective error caused by artificially setting the threshold is reduced, and the objectivity and repeatability of the boundary recognition result are improved.

[0086] The present embodiment also provides a magnetic anomaly boundary information identification system based on the magnetic gradient tensor, comprising: a data processing module, used to collect measured magnetic anomaly data and perform gridding processing and polarization processing to obtain gridded magnetic anomaly data after polarization; a component calculation module, used to perform edge expansion calculation and frequency domain Fourier conversion on the gridded magnetic anomaly data after polarization to obtain magnetic gradient tensor component data; an identification calculation module, used to perform boundary identification calculation on the magnetic gradient tensor component data to obtain a two-dimensional data matrix containing geological body boundary information; a boundary identification module, used to visualize the two-dimensional data matrix, and use the extreme value information of the two-dimensional data matrix to extract the boundary information of the geological body and identify the boundary of the geological body.

[0087] This embodiment also provides a computer device, which is suitable for a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor, and includes: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor as proposed in the above embodiment.

[0088] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0089] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for identifying magnetic anomaly boundary information based on a magnetic gradient tensor as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0090] In summary, the present invention introduces magnetic gradient tensor data for the purpose of suppressing magnetic anomaly signal noise and improving the resolution of magnetic anomaly body boundary information, which has a stronger suppression effect on noise interference, so that the geological body boundary identification result is less affected by oblique magnetization, and the boundary information of the geological body can be identified more accurately; by gridding and polarization processing of magnetic anomaly data, the data has a unified spatial distribution format, which more accurately reflects the magnetic distribution of underground geological bodies and lays a data foundation for subsequent precise analysis; by using the boundary recognition calculation method to comprehensively analyze the three main components of the magnetic gradient tensor, the sensitivity and resolution of boundary recognition are significantly improved, and weak boundary features that are difficult to identify by conventional methods can be captured; by using the extreme value information of the boundary recognition calculation result to identify the geological body boundary, the boundary information is highlighted, the accuracy of the recognition result is guaranteed, and the lateral resolution of the recognition result is improved.

[0091] Example 2

[0092] Reference Figure 2 to Figure 7 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0093] This example is aimed at the magnetic anomaly boundary information recognition method based on magnetic gradient tensor proposed by the present invention. The boundary information recognition effect is verified by using a rectangular parallelepiped model. The structure of the rectangular parallelepiped model is as follows: Figure 2 As shown, the total magnetization intensity of the rectangular model is 0.2A / m, the inclination angle of the total magnetization direction is 90°, and the deflection angle is 0°, as shown in Figure 3 The figure shows the plane contour lines of the forward magnetic anomaly of the rectangular parallelepiped model. By using the magnetic anomaly boundary information recognition method of the present invention to perform boundary recognition calculation on the forward magnetic anomaly of the rectangular parallelepiped model, the boundary recognition calculation result of the rectangular parallelepiped model is as follows: Figure 4 As shown, it can be seen from the figure that the present invention can clearly and accurately identify the boundary position information of the rectangular parallelepiped model, indicating that the present invention has high recognition accuracy and lateral resolution for the boundary of a single rectangular parallelepiped model.

[0094] Compared with the boundary information recognition of the above-mentioned single rectangular parallelepiped model, in order to verify the boundary recognition effect of the present invention on complex magnetic anomalies, this example also uses a combined model to demonstrate the effect of boundary information recognition in the present invention. The combined model is composed of three rectangular parallelepipeds. The structure of the combined rectangular parallelepiped model is as follows: Figure 5 As shown, the total magnetization intensity of the No. 1 rectangular model is 0.1A / m, the total magnetization intensity of the No. 2 rectangular model is 0.15A / m, the total magnetization intensity of the No. 3 rectangular model is 0.2A / m, the inclination angle of the total magnetization direction is 60°, and the deflection angle is 10°, as shown in Figure 6The figure shows the plane contour lines of the forward magnetic anomaly of the combined rectangular parallelepiped model. 0.1% white noise is added to the forward magnetic anomaly result to simulate noise interference, and then the boundary recognition method of the present invention is used to perform boundary recognition calculation.

[0095] like Figure 7 The figure shows the boundary recognition calculation results of the combined rectangular model by the present invention. It can be seen from the figure that for the complex magnetic anomaly caused by multiple rectangular models of different shapes and different magnetization intensities, the present invention can more clearly and accurately identify the boundary position information of each rectangular model. The results show that the magnetic anomaly boundary recognition method based on the magnetic gradient tensor proposed by the present invention is less affected by oblique magnetization, has a good suppression effect on noise, has high recognition accuracy and lateral resolution, and can more accurately identify the boundary information of the geological body.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying magnetic anomaly boundary information based on magnetic gradient tensor, characterized in that: include: Collect measured magnetic anomaly data and pre-process them to obtain gridded magnetic anomaly data after polarization; Calculating based on the gridded magnetic anomaly data after polarization to obtain magnetic gradient tensor component data; Performing boundary recognition calculation on the magnetic gradient tensor component data to obtain boundary recognition calculation results; The boundary information of the geological body is identified using the boundary identification calculation result.

2. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 1, characterized in that: The obtaining of the gridded magnetic anomaly data after polarization includes performing gridding processing and polarization processing on the measured magnetic anomaly data.

3. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 2, characterized in that: The magnetic gradient tensor component data is enhanced by using the boundary recognition calculation, and the calculation formula of the boundary recognition calculation is as follows: Where L is the boundary recognition calculation result, that is, the two-dimensional data matrix containing the boundary information of the geological body; B xx B is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; B zz It is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component.

4. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 3, characterized in that: The identification of the boundary information of the geological body includes visualizing the boundary identification calculation results using MATLAB or Surfer program to generate a plane contour map, and extracting the boundary information of the geological body using the extreme value information of the boundary identification calculation results to identify the boundary of the geological body; The method of extracting the boundary information of the geological body by using the extreme value information of the boundary recognition calculation result L comprises: The boundary recognition calculation result L is judged in the plane contour map. If the boundary recognition calculation result L of point P P >Boundary recognition calculation result L on the left P左 and the boundary recognition calculation result L on the right P右 , then the boundary recognition calculation result L of the decision point P P is the extreme value, point P is the extreme value point and is recorded; If the boundary recognition result L of point P P > Upper boundary recognition calculation result L P上 and the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P is the extreme value, point P is the extreme value point and is recorded; If the boundary recognition result L of point P P ≤ the left boundary recognition calculation result L P左 Or the boundary recognition calculation result L on the right P右 , and the boundary recognition calculation result of point P is L P ≤ Upper boundary recognition calculation result L P上 Or the boundary recognition calculation result L on the lower side P下 , then the boundary recognition calculation result L of the decision point P P Not an extreme value, point P is not an extreme value point, and is not recorded; The recorded extreme value points are combined to obtain the extreme value information of the boundary recognition calculation result L.

5. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 4, characterized in that: The specific formula for the extreme value information of the boundary recognition calculation result is as follows: [L(i,j)≥L(i+1,j)∩L(i,j)≥L(i-1,j)]∪[L(i,j)≥L(i,j+1)∩L(i,j)≥L(i,j-1)] Wherein, L(i,j) is the boundary recognition calculation result L of point (i,j); i is the horizontal coordinate; j is the vertical coordinate; The boundary of the geological body is identified by connecting all extreme points of the boundary identification calculation results L into several closed or semi-closed curves according to the principle of proximity, wherein the corresponding curves are equivalently regarded as the boundary of the geological body.

6. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 5, characterized in that: The obtaining of magnetic gradient tensor component data comprises the following steps: Performing edge expansion calculation on the gridded magnetic anomaly data after polarization to obtain the magnetic anomaly data after edge expansion; Performing frequency domain Fourier conversion on the expanded magnetic anomaly data to obtain magnetic gradient tensor component data; The edge expansion calculation includes an upper area, a lower area, a left area, and a right area.

7. The method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to claim 6, characterized in that: The calculation formula of the magnetic gradient tensor component data is as follows: Among them, B xx is the x-direction derivative data of the x-direction component of the coordinate axis in the magnetic gradient tensor component; S xx (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion xx Spectrum of component; B yy is the y-direction derivative data of the y-direction component of the coordinate axis in the magnetic gradient tensor component; S yy (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion yy Spectrum of component; B zz is the z-direction derivative data of the z-direction component of the coordinate axis in the magnetic gradient tensor component; S zz (u,v) is the frequency domain magnetic gradient tensor B after Fourier conversion zz The spectrum of the component; u is the angular frequency of the horizontal coordinate x; v is the angular frequency of the vertical coordinate y; i is the imaginary unit; x is the horizontal coordinate value; y is the vertical coordinate value.

8. A magnetic anomaly boundary information recognition system based on magnetic gradient tensor, based on a magnetic anomaly boundary information recognition method based on magnetic gradient tensor according to any one of claims 1 to 7, characterized in that: include, A data processing module is used to collect measured magnetic anomaly data and perform gridding and polarization processing to obtain gridded magnetic anomaly data after polarization; The component calculation module is used to perform edge expansion calculation and frequency domain Fourier conversion on the gridded magnetic anomaly data after polarization to obtain magnetic gradient tensor component data; An identification calculation module is used to perform boundary identification calculation on the magnetic gradient tensor component data to obtain a two-dimensional data matrix containing geological body boundary information; The boundary recognition module is used to visualize the two-dimensional data matrix, extract the boundary information of the geological body by using the extreme value information of the two-dimensional data matrix, and recognize the boundary of the geological body.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for identifying magnetic anomaly boundary information based on magnetic gradient tensor according to any one of claims 1 to 7 are implemented.

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