A sparse two-dimensional magnetic imaging method

Abstract

This invention provides a sparse two-dimensional magnetic imaging method. The method includes: equating the magnetic anomaly field generated by a ferromagnetic target to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice; wherein the ferromagnetic target includes targets such as underground pipes, ironware and metal fragments in archaeological work, and landmines and shells in military applications; uniformly dividing the two-dimensional horizontal slice into small rectangular grid units, where the magnetic moments of the magnetic dipoles in the grid where the target is located are non-zero, and the magnetic moments of the spatial magnetic dipoles without the target are all zero; and estimating the parameters of the magnetic dipoles using an orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target. This embodiment simplifies the complex magnetic field distribution to the superposition of multiple magnetic dipoles, reducing the complexity of the problem; through modeling and parameter estimation of the magnetic anomaly field, the location and identification of ferromagnetic targets can be achieved, which has important application value in fields such as geological exploration, archaeology, and landmine detection.

Description

Technical Field

[0001] This invention relates to the fields of target detection, localization and imaging technology, and in particular to a sparse two-dimensional magnetic imaging method. Background Technology

[0002] Ferromagnetic targets generate an induced magnetic field around themselves under the influence of the Earth's magnetic field, and this induced magnetic field superimposes with the Earth's magnetic field to form a magnetic anomaly field. Magnetic detection is a passive detection method with wide applications in near-surface target detection, such as in medicine, materials science, and earth sciences; it is particularly useful in underground pipeline inspection and archaeology. By using magnetic field sensors to measure the magnetic field data above ferromagnetic targets, and then using magnetic field data interpretation methods, target detection, localization, and imaging can be achieved.

[0003] Currently, magnetic imaging of underground targets based on space magnetic field data is of great significance for target classification and identification. Existing magnetic imaging methods are all based on numerical inversion methods, which divide the unknown three-dimensional space into a grid-like magnetic susceptibility model, establish an objective function in the spatial domain, and use optimization inversion algorithms to solve for the magnetic susceptibility distribution in the spatial domain, thereby obtaining the basic shape characteristics of ferromagnetic targets. However, the calculation of magnetic susceptibility-based inversion methods generally involves large matrix inversions, resulting in poor stability of the inversion results and long inversion iteration times.

[0004] Prior art 1, application number: CN202311193103.X, discloses a method for removing regional fields from gravity and magnetic data, including the following steps: obtaining measured gravity and magnetic data of the working area, deleting obviously disturbed measurement data, and obtaining a preprocessed dataset. Performing three-dimensional inversion on the preprocessed dataset to obtain an inverted magnetic susceptibility model or inverted density model over a large spatial range. Defining the range of anomaly regions, setting the magnetic susceptibility or density values ​​at the corresponding locations of the anomaly regions to 0, and obtaining a new large-scale magnetic susceptibility model or density model. Performing three-dimensional forward modeling to obtain a forward modeled dataset of magnetic or gravitational fields over a large spatial range. Subtracting the forward modeled magnetic or gravitational field values ​​at the corresponding locations from the data values ​​at the corresponding locations of the anomaly regions in the preprocessed dataset to obtain the remaining anomaly field or local anomaly field. Although it can quickly and accurately obtain residual or local anomalies in magnetic field or gravity field data, thus leading to more reasonable inferences or inversion results, it requires data processing and the construction of inversion magnetic susceptibility or inversion density models, making the processing relatively complex and affecting the efficiency of magnetic imaging to some extent. If there are problems with the model, it will directly affect the accuracy of the inversion results.

[0005] Prior art two, application number CN202110894407.3, discloses a joint inversion method for airborne, surface, and well-drilled magnetic anomaly data based on well rock property constraints. This method treats airborne, surface, and well-drilled magnetic anomalies as three different types of data, and considers the subsurface magnetic susceptibility models causing these anomalies as three different "apparent" physical models. In the joint inversion, the three types of data are used alternately as the primary observation data. When one type of data is used as the primary observation data, the models inverted from the other two types of data are added to the joint inversion as structural constraints in the form of cross-gradient terms. The joint inversion yields three different "apparent" magnetic susceptibility models. During the inversion process, the weight of any one type of data is neither strengthened nor weakened, fully utilizing the information in each data set. Although a linear regression neural network model is used to process the three "apparent" magnetic susceptibility models, and strong constraints are applied to the magnetic susceptibility models based on the well-drilled rock magnetic susceptibility, fully utilizing the information from the well-collected rock magnetic susceptibility and improving the accuracy of the inversion results, the presence of a forward modeling operator matrix leads to poor stability of the inversion results, and the inversion iteration requires a long time.

[0006] Prior art three, application number CN202110865233.8, discloses a magnetic susceptibility inversion method and system. The method includes: obtaining spatial domain residuals based on observed and theoretical magnetic field strengths; representing the spatial domain residuals using a two-dimensional discrete Fourier inverse transform based on the wavenumber domain residuals; obtaining wavenumber domain model perturbations based on the spatial domain residuals, and further obtaining spatial domain model perturbations based on inverse Fourier transforms; and obtaining a magnetic susceptibility inversion model based on the spatial domain model perturbations. Although this reduces the required memory space and improves computational efficiency, further improving inversion efficiency, the computational complexity of the magnetic susceptibility inversion model is high, and the magnetic susceptibility inversion model typically requires a large amount of computation and iteration.

[0007] Current technologies 1, 2, and 3 suffer from poor stability in inversion results calculated using existing magnetic susceptibility inversion methods, and the inversion iterations require a long time. Therefore, this invention provides a two-dimensional sparse magnetic imaging method that decomposes complex targets into multiple continuous magnetic dipole models and uses the Orthogonal Matching Pursuit (OMP) algorithm to estimate the horizontal distribution of the magnetic moments of the magnetic dipoles, achieving two-dimensional imaging results for ferromagnetic targets. This invention's method differs from traditional magnetic susceptibility inversion models, offering advantages such as computational stability and speed. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a sparse two-dimensional magnetic imaging method, comprising the following steps:

[0009] The magnetic anomaly field generated by a ferromagnetic target is equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice.

[0010] The two-dimensional horizontal slice is uniformly divided into small rectangular grid units. The magnetic moment of the magnetic dipole in the grid where the target is located is non-zero, and the magnetic moment of the spatial magnetic dipole is zero when there is no target.

[0011] The parameters of the magnetic dipole are estimated using the orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target.

[0012] Optionally, the process of uniformly dividing a two-dimensional horizontal slice into small rectangular grid units includes the following steps:

[0013] The scope of the two-dimensional horizontal slice is determined, that is, the length and width of the two-dimensional horizontal slice are determined; upon receiving the two-dimensional horizontal slice to be divided, the size, characteristics and magnetic field resolution of the target mesh are classified according to the requirements of each search result based on a preset search program, the division results of the search results are fused, and the minimum mesh shape and size of the two-dimensional horizontal slice to be divided are determined based on the fusion result.

[0014] Based on the length, width, and minimum grid size of the 2D horizontal slice, calculate the number of grids to be divided into the 2D horizontal slice; based on the calculated number of grids, determine the origin of the coordinate system, i.e., the center point of the 2D horizontal slice; determine the direction of the polar axis, i.e., a fixed direction on the 2D horizontal slice; determine the size of each annular region based on the minimum grid size; uniformly divide the 2D horizontal slice in the polar coordinate system based on the annular region grid size; determine the starting angle and angle range of each annular region, and obtain the coordinate range of each annular region grid; complete the uniform division of the 2D horizontal slice.

[0015] Determine the boundary of each annular region grid in the polar coordinate system, that is, determine the coordinates of the four vertices of each annular region grid.

[0016] Optionally, the process of uniformly dividing the two-dimensional horizontal slice into the smallest rectangular grid unit adopts the target two-dimensional imaging model. For targets with complex shapes near the ground surface, the magnetic anomaly field generated is equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles on a horizontal slice. The two-dimensional horizontal slice is uniformly divided into the smallest rectangular grid unit. The magnetic moment of the magnetic dipole in the grid in the target area is non-zero, and the magnetic moment of the spatial magnetic dipole without the target is zero.

[0017] Two-dimensional imaging of a target is an inverse problem described above. The parameters of the equivalent magnetic dipole are extracted by measuring the observed magnetic anomaly field ΔT above the target. The two-dimensional imaging result of the target can be characterized based on the position distribution of the magnetic dipole and the magnitude of the magnetic moment.

[0018] Optionally, the magnetic field sensor measures the magnetic field in a two-dimensional plane above the target. The measurement plane is much larger than the spatial distribution of the target. The target is sparse in the solution space. The horizontal position and magnetic moment vector magnitude of the non-zero magnetic dipoles are obtained by using the orthogonal matching pursuit algorithm. The distribution result of the non-zero magnetic dipoles on the two-dimensional plane is the imaging result of the anomalous target.

[0019] Optionally, the magnetic field sensor may be configured as one or more of the following: optically pumped magnetometer, Hall effect magnetic field sensor, fluxgate magnetoresistive sensor, and superconducting quantum magnetometer.

[0020] Optionally, in a multi-sensor system, a method for preventing interference between different magnetic field sensors includes the following steps:

[0021] The original electromagnetic wave from the magnetic field sensor is converted into a digital signal to determine the number of magnetic field sensors in the area where the magnetic field sensor is located. It is then determined whether the number of magnetic field sensors exceeds a preset threshold, which serves as the trigger condition for activating the magnetic field sensor's anti-interference program.

[0022] If the number of magnetic field sensors exceeds a preset threshold, an adaptive neural network algorithm is used to adjust the frequency, phase, and amplitude of the digital signal to reconstruct the digital signal interference and obtain the digital interference signal.

[0023] The digital interference signal is superimposed on the digital signal, and the power of the digital interference signal is adjusted according to a preset weight to eliminate the interference signal in the digital signal, thus obtaining the processed digital signal.

[0024] Optionally, the magnetic field observation plane is much larger than the target's distribution range. In the two-dimensional imaging plane, the magnitude of most of the grid magnetic dipoles is 0, and the magnitude of the magnetic dipoles is non-zero only at the target's distribution location. The horizontal position and magnetic moment vector magnitude of the non-zero magnetic dipoles are obtained using an orthogonal matching pursuit algorithm. The measured magnetic field signal is input, and a sensing matrix is ​​established based on the established magnetic dipole model. The parameters of the non-zero magnetic dipoles are iteratively solved until the calculation error is less than a preset threshold. The distribution of the non-zero grid magnetic dipoles corresponds to the two-dimensional imaging result of the target, thus realizing the two-dimensional imaging of the target.

[0025] Optionally, the two-dimensional magnetic imaging process includes the following steps:

[0026] The magnetic anomaly field of the target is represented by a sparse coefficient vector σ; the target is sparsely distributed in the solution space, that is, the coefficients of most magnetic dipoles are 0;

[0027] By solving the L0 optimization problem, the parameters of the magnetic dipole are estimated. The L0 norm represents the number of non-zero elements. The estimated value of the sparse coefficient vector σ is determined by minimizing the L0 norm and the residual of the observed data.

[0028] Optionally, the process of achieving two-dimensional imaging of the underground space through the distribution of θ in the magnetic dipole parameter estimation results includes the following steps:

[0029] The parameters are selected based on the geometry, magnetic properties, and distribution patterns of the target two-dimensional image, and a parameter set θ is generated to describe the position and magnetic moment vector of the magnetic dipole in two-dimensional space.

[0030] By establishing the correspondence between the parameter set θ and the distribution of magnetic dipoles in two-dimensional space, the parameter set is mapped to the corresponding spatial location; the position of the magnetic dipole on the observation plane is determined and used for magnetic field imaging.

[0031] Two-dimensional imaging results are generated by mapping the positions of magnetic dipoles in the parameter set θ onto the observation plane and combining them with the magnetic moment vector of the magnetic dipoles.

[0032] Optionally, the two-dimensional imaging result is a two-dimensional image or a heat map, showing the distribution of magnetic dipoles on the observation plane.

[0033] This invention proposes a two-dimensional sparse magnetic imaging method based on the OMP algorithm, which differs from traditional imaging methods that invert the magnetic susceptibility of targets. The target is equivalent to the superposition of magnetic fields generated by multiple consecutive magnetic dipoles on the same horizontal slice. Considering that the equivalent magnetic dipoles are sparse throughout the imaging region, the OMP algorithm can be used to directly estimate the parameters of the magnetic dipoles, achieving two-dimensional magnetic imaging of the target. A new forward model for anomalous targets is established, which is equivalent to the superposition of magnetic fields generated by multiple consecutive magnetic dipoles on the same horizontal slice. Considering that the observation plane is much larger than the interval where the target is located, the distribution of the target (non-zero magnetic dipoles) in the solution space is sparse. The target distribution in the underground two-dimensional plane is estimated based on the scalar magnetic field signal observed above the target. The orthogonal matching pursuit algorithm can be used to directly estimate the parameters of the magnetic dipoles, achieving two-dimensional magnetic imaging of the target.

[0034] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0035] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a flowchart of the sparse two-dimensional magnetic imaging method in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of the magnetic field generated by the magnetic dipole in Embodiment 2 of the present invention;

[0039] Figure 3 This is a process diagram of uniformly dividing a two-dimensional horizontal slice into small rectangular grid units in Embodiment 3 of the present invention;

[0040] Figure 4 This is a schematic diagram of the equivalent distribution of targets on a two-dimensional cross-section of the underground space in Embodiment 4 of the present invention;

[0041] Figure 5 This is a flowchart of the method for preventing interference between different magnetic field sensors in Embodiment 5 of the present invention;

[0042] Figure 6 This is a schematic diagram of the simulation experiment scenario in Embodiment 9 of the present invention;

[0043] Figure 7 This is a map showing the distribution of magnetic field contour lines on the observation plane in Embodiment 9 of the present invention;

[0044] Figure 8 This is a schematic diagram of the two-dimensional imaging result (the curve is the outline of the target) in Embodiment 9 of the present invention. Detailed Implementation

[0045] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0046] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0047] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0048] Example 1: As Figure 1 As shown, this embodiment of the invention provides a sparse two-dimensional magnetic imaging method, comprising the following steps:

[0049] S100: The magnetic anomaly field generated by a ferromagnetic target is equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice; among which, ferromagnetic targets include underground pipes, ironware and metal fragments in archaeological work, and targets such as landmines and shells in military applications;

[0050] S200: The two-dimensional horizontal slice is uniformly divided into small rectangular grid units. The magnetic moment of the magnetic dipole in the grid where the target is located is non-zero, and the magnetic moment of the spatial magnetic dipole is zero when there is no target.

[0051] S300: The parameters of the magnetic dipole are estimated using the orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target.

[0052] The working principle and beneficial effects of the above technical solution are as follows: Firstly, this embodiment equates the magnetic anomaly field generated by a ferromagnetic target to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice. Ferromagnetic targets include underground pipes, iron artifacts and metal fragments from archaeological work, and landmines and artillery shells used in military applications. Secondly, the two-dimensional horizontal slice is uniformly divided into small rectangular grid units. The magnetic moments of magnetic dipoles in the grid area where the target is located are non-zero, while the magnetic moments of spatial magnetic dipoles without targets are all zero. Finally, the parameters of the magnetic dipoles are estimated using an orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target. In this embodiment, the scalar magnetic anomaly field is the projection of the magnetic anomaly vector generated by the target onto the direction of the geomagnetic field. It is assumed that the magnetic dipoles are generated by the induced magnetization of the geomagnetic field, and the magnetic moment vector of the magnetic dipoles is parallel to the direction of the geomagnetic field. The magnetic field generated by the magnetic dipoles is a linear combination of the magnitude of the magnetic moment vector and the horizontal and vertical positions of the magnetic moment. The above solution utilizes the orthogonal matching pursuit algorithm (OMP) to perform two-dimensional magnetic imaging of the magnetic anomaly field generated by ferromagnetic targets, thereby achieving the detection and location of underground targets. The magnetic anomaly field of a ferromagnetic target is equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice. This equivalent model helps to understand and describe the formation mechanism of the magnetic anomaly field, simplifying the complex magnetic field distribution to the superposition of multiple magnetic dipoles, thus reducing the complexity of the problem. The two-dimensional horizontal slice is uniformly divided into small rectangular grid units, and the magnetic moments of non-zero magnetic dipoles are set in the grid of the target area, while the magnetic moments are set to zero in the space where there is no target. Through this gridded representation, the distribution of the magnetic anomaly field can be discretized, making the problem transform into finding the location of magnetic dipoles and estimating their magnetic moments. The parameters of the magnetic dipoles are estimated using the orthogonal matching pursuit algorithm to achieve two-dimensional magnetic imaging of the target. OMP is a compressed sensing algorithm that estimates the sparse representation of the signal by iteratively selecting the support set of the sparse signal; by iteratively selecting the location and magnetic moment of the magnetic dipoles, the magnetic anomaly field of the target can be gradually reconstructed, realizing the imaging of underground targets.

[0053] This embodiment enables the location and identification of ferromagnetic targets through modeling and parameter estimation of magnetic anomaly fields, which has important application value in fields such as geological exploration, archaeology and landmine detection.

[0054] This embodiment innovatively proposes a two-dimensional sparse magnetic imaging method based on the OMP algorithm, which differs from traditional imaging methods that invert the magnetic susceptibility of targets. The target is equivalent to the superposition of magnetic fields generated by multiple consecutive magnetic dipoles on the same horizontal slice. Considering that the equivalent magnetic dipoles are sparse throughout the imaging region, the OMP algorithm can directly estimate the parameters of the magnetic dipoles, achieving two-dimensional magnetic imaging of the target. A new forward model for anomalous targets is established, equating the target to the superposition of magnetic fields generated by multiple consecutive magnetic dipoles on the same horizontal slice. Considering that the observation plane is much larger than the interval where the target is located, the distribution of the target (non-zero magnetic dipoles) in the solution space is sparse. The target distribution result in the underground two-dimensional plane is estimated based on the observed scalar magnetic field signal above the target. The orthogonal matching pursuit algorithm can be used to directly estimate the parameters of the magnetic dipoles, achieving two-dimensional magnetic imaging of the target.

[0055] Example 2: Based on Example 1, the magnetic anomaly field provided in this embodiment of the invention is generated by a magnetic dipole model. The magnetic dipole model can calculate the spatial magnetic field generated by a point target. When the distance between the observation point and the target is greater than three times the maximum size of the target, the shape, attitude, and other characteristics of the target can be ignored, and it is equivalent to a point in space. The formula for calculating the magnetic field vector generated by the magnetic dipole is as follows:

[0056]

[0057]

[0058]

[0059] Among them, B x B y B z Let r represent the components of the magnetic field vector in the x, y, and z directions, respectively. x r y r z represents the x, y, and z components of the distance vector between the observation point and the magnetic dipole position, respectively, and r represents the magnitude of the distance vector between the observation point and the magnetic dipole position.

[0060] like Figure 2 As shown, the magnetic moment magnitude of the magnetic dipole is K, and the unit vector is (l,m,n). Let D be the magnitude of the distance vector between the observation point and the position of the magnetic dipole, and let D be the dot product of the distance vector and the unit vector of the magnetic moment of the magnetic dipole.

[0061] D = lr x +mr y +nr z (4)

[0062] Therefore, the target magnetic anomaly field measured by the scalar magnetic field sensor is the projection of the vector magnetic field generated by the magnetic dipole onto the direction of the geomagnetic field, where the unit vector direction of the geomagnetic field is (L,m,N), resulting in:

[0063] ΔT=LB x +MB y +NB z (5)

[0064] When the magnetic dipole model considers only the effects of induced magnetization and neglects demagnetization, the direction of the magnetic moment vector is the same as the direction of the geomagnetic field, i.e., (l,m,n)=(L,M,N). In this case, the magnetic anomaly field can be simplified to:

[0065]

[0066] The working principle and beneficial effects of the above technical solution are as follows: This embodiment provides a method for calculating the magnetic field generated by a point target based on the magnetic dipole model. The components of the magnetic field in different directions can be calculated by formulas (1), (2) and (3); formula (4) is used to calculate the relationship between the distance vector and the magnetic moment of the magnetic dipole; formula (5) represents the magnetic anomaly field of the target measured by the scalar magnetic field sensor, which is the projection of the vector magnetic field generated by the magnetic dipole in the direction of the geomagnetic field. When considering the influence of induced magnetization and ignoring the demagnetization effect, formula (6) simplifies the calculation of the magnetic anomaly field. The significance includes: It provides a mathematical model for calculating the magnetic field of a point target: Through the magnetic dipole model, the magnetic field calculation of a point target can be transformed into a simple mathematical formula, thereby facilitating magnetic field analysis and calculation. It simplifies the complexity of magnetic field calculation: By ignoring the shape and attitude characteristics of the target, the target is equivalent to a point in space, which simplifies the complexity of magnetic field calculation and reduces the amount of calculation. It improves the calculation efficiency: By simplifying the model and calculation formula, the efficiency and speed of magnetic field calculation are improved, making magnetic field analysis and related applications more efficient. Suitable for situations where the distance is much larger than the target size: When the distance between the observation point and the target is more than 3 times the maximum size of the target, the target can be equivalent to a point in space, which simplifies the model and is suitable for practical application scenarios where the distance is much larger than the target size.

[0067] In summary, the significance of this embodiment lies in providing a simplified method for calculating magnetic fields, which is applicable to calculating the magnetic fields generated by point targets and has high computational efficiency and applicability.

[0068] Example 3: As Figure 3 As shown, based on Example 1, the process of uniformly dividing a two-dimensional horizontal slice into small rectangular grid units provided by this embodiment of the invention includes the following steps:

[0069] S201: Determine the range of the two-dimensional horizontal slice, that is, determine the length and width of the two-dimensional horizontal slice; upon receiving the two-dimensional horizontal slice to be divided, classify the target mesh size, characteristics and magnetic field resolution according to the requirements of each search result based on the preset search program, fuse the division results of the search results, and determine the minimum mesh shape and size of the two-dimensional horizontal slice to be divided based on the fusion result.

[0070] S202: Calculate the number of grids in the 2D horizontal slice based on its length, width, and minimum grid size; determine the origin of the coordinate system, i.e., the center point of the 2D horizontal slice, based on the calculated number of grids; determine the direction of the polar axis, i.e., a fixed direction on the 2D horizontal slice; determine the size of each annular region based on the minimum grid size; uniformly divide the 2D horizontal slice in the polar coordinate system based on the annular region grid size; determine the starting angle and angle range of each annular region to obtain the coordinate range of each annular region grid; complete the uniform division of the 2D horizontal slice.

[0071] S203: Determine the boundary of each annular region grid in the polar coordinate system, that is, determine the coordinates of the four vertices of each annular region grid.

[0072] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first determines the range of the two-dimensional horizontal slice, that is, determines the length and width of the two-dimensional horizontal slice; upon receiving the two-dimensional horizontal slice to be divided, the size, characteristics, and magnetic field resolution of the target mesh are used to classify the search results according to a preset search program, and the division results of the search results are fused. Based on the fusion result, the minimum mesh shape and size of the two-dimensional horizontal slice to be divided are determined; secondly, based on the length, width, and minimum mesh size of the two-dimensional horizontal slice, the number of meshes to be divided in the two-dimensional horizontal slice is calculated; based on the calculated number of meshes, the origin of the coordinate system is determined, that is, the center point of the two-dimensional horizontal slice; the direction of the polar axis is determined, that is, a fixed direction on the two-dimensional horizontal slice, and the size of each annular region is determined based on the minimum mesh; based on the size of the annular region mesh, uniform division is performed in the polar coordinate system; the starting angle and angle range of each annular region are determined, and the coordinate range of each annular region mesh is obtained; the uniform division on the two-dimensional horizontal slice is completed; finally, the boundary of each annular region mesh is determined in the polar coordinate system, that is, the coordinates of the four vertices of each annular region mesh are determined. The above scheme achieves finer and more accurate grid division when uniformly dividing a two-dimensional horizontal slice by determining the minimum grid shape and size, as well as the starting angle and angle range of each annular region. By fusing multiple search results, a more comprehensive and integrated division result can be obtained. The significance of determining the minimum grid shape and size based on the fusion result lies in the ability to classify according to the needs of different search results and combine multiple factors to determine the most suitable grid shape and size. This better adapts to the characteristics of the target and the magnetic field resolution requirements, ensuring that the divided grid accurately reflects the magnetic anomaly field distribution of the target. After uniformly dividing the two-dimensional horizontal slice, determining the boundary and coordinate range of each annular region facilitates subsequent data processing and analysis. Each grid represents a small region that can be used to calculate and process magnetic anomaly field data, enabling two-dimensional imaging of the target and the study of its magnetic field distribution.

[0073] This embodiment improves the precision and accuracy of grid division: by fusing multiple search results, the minimum grid shape and size, as well as the boundaries and coordinate range of the annular region, are determined, allowing for finer division of two-dimensional horizontal slices and improving grid precision and accuracy. It adapts to different needs and characteristics: by classifying needs based on the fusion results, the most suitable grid shape and size can be determined according to the requirements of different search results, adapting to the characteristics and magnetic field resolution requirements of different targets. It improves the efficiency of data processing and analysis: after division, each grid represents a small region, facilitating data processing and analysis, and enabling the study of two-dimensional imaging and magnetic field distribution of the target.

[0074] In summary, the significance of this embodiment lies in providing a method for more accurately and precisely dividing two-dimensional horizontal slices. By fusing the results, the minimum grid shape and size are determined, as well as the starting angle and angle range of each annular region, thus obtaining the coordinate range of each annular region grid. This enables two-dimensional imaging of the target and the study of magnetic field distribution. This has a positive promoting effect on magnetic field data processing and analysis. Through the above steps, the two-dimensional horizontal slice can be uniformly divided into small rectangular grid units. Each grid represents a small region used to calculate and process magnetic anomaly field data. In the target two-dimensional imaging model, each grid can be equivalent to a magnetic dipole. By superimposing the magnetic fields of multiple grids, the magnetic anomaly field distribution of the entire slice can be obtained; this enables the analysis and imaging of magnetic anomaly fields of complex targets.

[0075] Example 4: Based on Example 1, the process of uniformly dividing a two-dimensional horizontal slice into the smallest rectangular grid unit provided in this embodiment of the invention adopts a target two-dimensional imaging model. For targets with complex near-surface shapes, the generated magnetic anomaly field can be equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles on a horizontal slice, such as... Figure 4 As shown; the two-dimensional horizontal slice is uniformly divided into the smallest rectangular grid units. The magnetic moment of the magnetic dipole in the grid where the target is located is non-zero, while the magnetic moment of the spatial magnetic dipole is zero in areas without a target. Therefore:

[0076]

[0077] Where, ΔT i Let θ represent the magnetic anomaly field generated by the i-th magnetic dipole, and n represent the number of magnetic dipoles. θ = {θ i}=[x i ,y i ,K i ] represents the parameter set of the i-th magnetic dipole, (x i ,y i K represents the horizontal position coordinates of the magnetic dipole. i The magnitude of the magnetic moment of the magnetic dipole can be calculated according to formula (6).

[0078] Two-dimensional imaging of a target is an inverse problem described above. By measuring the observed magnetic anomaly field ΔT above the target, the parameters of the equivalent magnetic dipole can be extracted. The two-dimensional imaging result of the target can be characterized based on the position distribution of the magnetic dipole and the magnitude of the magnetic moment.

[0079] The working principle and beneficial effects of the above technical solution are as follows: This embodiment adopts a two-dimensional imaging model of the target, uniformly divides the two-dimensional horizontal slice into the smallest rectangular grid unit, and uses a magnetic dipole model to describe the magnetic anomaly field generated by the target. It has the following significance: It provides a magnetic anomaly field calculation method based on the magnetic dipole model: Using the magnetic dipole model, the magnetic anomaly field generated by the target is equivalent to the superposition of magnetic fields generated by multiple magnetic dipoles; the magnetic anomaly field generated by each magnetic dipole can be calculated by formula (7). It realizes the uniform division of the two-dimensional horizontal slice: By uniformly dividing the two-dimensional horizontal slice into the smallest rectangular grid unit, the area where the target is located can be divided into multiple small areas, which facilitates the calculation and analysis of the magnetic anomaly field of each small area. It provides a method for two-dimensional imaging of the target: By measuring the observed magnetic anomaly field above the target, the parameters of the equivalent magnetic dipole, including the position distribution and the magnitude of the magnetic moment, can be extracted; these parameters can characterize the two-dimensional imaging result of the target, and realize the study and analysis of the target's shape and characteristics. It provides a basis for magnetic field data processing and analysis: By using the magnetic dipole model and the two-dimensional imaging method, the magnetic anomaly field of the target can be calculated and analyzed. This provides a foundation for magnetic field data processing and analysis, helping to understand the magnetic properties and shape of the target.

[0080] In summary, the significance of this embodiment lies in providing a method for calculating magnetic anomaly fields and a two-dimensional imaging method for targets based on a magnetic dipole model, which provides a foundation for magnetic field data processing and analysis, and helps to study and analyze the magnetic properties and shape of targets.

[0081] Example 5: Figure 5 As shown, based on Example 1, in step S200 of this embodiment, the magnetic field sensor measures the magnetic field in a two-dimensional plane above the target. The measurement plane is much larger than the spatial distribution of the target. The target (non-zero magnetic dipoles) is sparsely distributed in the solution space. The horizontal position and magnetic moment vector magnitude of the non-zero magnetic dipoles can be obtained using the orthogonal matching pursuit algorithm. The magnetic field sensor includes: optically pumped magnetometer, Hall effect magnetic field sensor, fluxgate magnetometer, magnetoresistive sensor, and superconducting quantum magnetometer, etc. The distribution result of the non-zero magnetic dipoles on the two-dimensional plane is the imaging result of the anomalous target.

[0082] The magnetic field sensor is configured as one or more of the following: optically pumped magnetometer, Hall effect magnetic field sensor, fluxgate magnetoresistive sensor, and superconducting quantum magnetometer. In a multi-sensor system, cross-interference may exist between different magnetic field sensors, meaning that the measurement signal of one sensor may be affected by other sensors, thus affecting the accuracy of the measurement results. The method for preventing interference between different magnetic field sensors includes the following steps:

[0083] S204: Convert the original electromagnetic wave of the magnetic field sensor into a digital signal, determine the number of magnetic field sensors in the area where the magnetic field sensor is located, and determine whether the number of magnetic field sensors exceeds a preset number threshold. The preset number threshold is the trigger condition for starting the magnetic field sensor anti-interference program.

[0084] S205: When the number of magnetic field sensors exceeds a preset threshold, an adaptive neural network algorithm is used to adjust the frequency, phase, and amplitude of the digital signal to reconstruct the digital signal interference and obtain the digital interference signal.

[0085] S206: Superimpose the digital interference signal with the digital signal, adjust the power of the digital interference signal according to the preset weight, so as to eliminate the interference signal in the digital signal and obtain the processed digital signal.

[0086] The working principle and beneficial effects of the above technical solution are as follows: This embodiment utilizes different types of magnetic field sensors for measurement, converting the original electromagnetic waves from the magnetic field sensors into digital signals. Then, based on whether the number of magnetic field sensors exceeds a preset threshold, it determines whether to activate the anti-interference program for the magnetic field sensors. If the number of magnetic field sensors exceeds the preset threshold, an adaptive neural network algorithm can be used to adjust the frequency, phase, and amplitude of the digital signal, reconstructing the digital interference signal. This interference signal is then superimposed on the digital signal, and its power is adjusted to eliminate the interference signal in the digital signal, resulting in a processed digital signal. Eliminating interference signals in the digital signal improves signal quality and accuracy. In practical applications, digital signals may be affected by various interference sources, such as electromagnetic interference and noise; these interference signals may cause signal quality degradation, affecting signal analysis and processing. By adopting the above solution, interference signal components in the digital signal can be weakened or eliminated through magnetic field sensor measurement and interference signal processing, thereby improving signal accuracy and reliability. Eliminating interference signals improves signal transmission quality, reduces error rate, and makes the signal more interpretable and effective. This is crucial for ensuring data reliability and improving system performance.

[0087] Example 6: Based on Example 1, the magnetic field observation plane provided in this embodiment of the invention is much larger than the distribution range of the target. The magnitude of most of the grid magnetic dipoles in the two-dimensional imaging plane is 0, and the magnitude of the magnetic dipoles is non-zero only at the location of the target distribution. The horizontal position and magnetic moment vector magnitude of the non-zero magnetic dipoles can be obtained by using the orthogonal matching pursuit algorithm. The measured magnetic field signal is input, and a sensing matrix is ​​established according to the established magnetic dipole model. The parameters of the non-zero magnetic dipoles are iteratively solved until the calculation error is less than a preset threshold. The distribution of the non-zero grid magnetic dipoles corresponds to the two-dimensional imaging result of the target, realizing the two-dimensional imaging of the target.

[0088] The working principle and beneficial effects of the above technical solution are as follows: This embodiment utilizes an orthogonal matching pursuit algorithm to perform two-dimensional imaging of the target on the magnetic field observation plane. The significance lies in: Two-dimensional imaging of the target: By measuring the magnetic field signal and using the orthogonal matching pursuit algorithm, the horizontal position and magnetic moment vector magnitude of the non-zero magnetic dipoles can be determined; by iteratively solving the magnetic dipole parameters, the magnetic dipole distribution of the non-zero grid can be obtained, which corresponds to the two-dimensional imaging result of the target. Therefore, two-dimensional imaging of the target can be achieved, allowing understanding of the target's distribution on the magnetic field observation plane. Efficient target imaging: Since the magnetic field observation plane is much larger than the target's distribution range, and the magnetic dipole magnitude of most grids is 0, this scheme can reduce the complexity of computation and processing, improving the efficiency of target imaging. Focusing only on the magnetic dipole distribution of the non-zero grid reduces the computational load and storage space requirements, while improving the speed and accuracy of imaging. Location and parameter estimation of non-zero magnetic dipoles: Using the orthogonal matching pursuit algorithm, this method can accurately determine the horizontal position and magnetic moment vector magnitude of non-zero magnetic dipoles; this is very important for understanding the magnetic characteristics and properties of the target and can provide key information for subsequent analysis and application.

[0089] In summary, the significance of this embodiment lies in achieving two-dimensional imaging of the target through the orthogonal matching pursuit algorithm, improving imaging efficiency, and accurately locating and estimating the parameters of non-zero magnetic dipoles; it provides an effective method for magnetic field observation and analysis, which can be applied to multiple fields, such as magnetic field imaging, target detection, and magnetic material characterization.

[0090] Example 7: Based on Example 1, the two-dimensional magnetic imaging process for achieving the target provided in this embodiment of the invention includes the following steps:

[0091] The matrix form of the magnetic anomaly field generated by the target is as follows:

[0092] s=D(θ)σ+n (8)

[0093] Where s represents the column vectorization of the observed magnetic anomaly field, D(θ) represents the dictionary of the corresponding parameter set, σ represents the sparse coefficient vector, and n represents additive white Gaussian noise; considering that the observation plane is much larger than the interval where the target is located, the target (non-zero magnetic dipole) is sparsely distributed in the solution space;

[0094] The magnetic dipole parameter estimation results are obtained by solving the L0 optimization problem in formula (9);

[0095]

[0096] Where ||·||0 represents the L0 norm, Let θ be the estimated sparse magnetic dipole parameter vector, and ε be the noise level; two-dimensional imaging of underground space can be achieved through the distribution of θ.

[0097] The working principle and beneficial effects of the above technical solution are as follows: This embodiment realizes two-dimensional magnetic imaging of the target and has the following important significance: Sparsity representation: The magnetic anomaly field of the target is represented by the sparse coefficient vector σ; According to formula (8), the target is sparsely distributed in the solution space, that is, most of the magnetic dipole coefficients are 0. This sparsity representation can effectively reduce the data storage and processing burden and improve the efficiency of magnetic imaging. Solving the L0 optimization problem: By solving the L0 optimization problem in formula (9), the magnetic dipole parameter estimation result σ^ can be obtained. The L0 norm represents the number of non-zero elements. The estimated value of the sparse coefficient vector σ is determined by minimizing the L0 norm and the residual of the observation data; the solution of this optimization problem can realize the accurate positioning and estimation of the target. Two-dimensional imaging of underground space: Through the magnetic dipole parameter estimation result σ^ and the distribution of the parameter set θ, two-dimensional imaging of underground space can be realized; by mapping the estimated magnetic dipole parameters to the corresponding positions, a two-dimensional distribution image of the target on the observation plane can be drawn, thereby realizing the imaging of the target.

[0098] In summary, the significance of this embodiment lies in achieving two-dimensional magnetic imaging of the target through sparse representation, solving the L0 optimization problem, and utilizing parameter distribution; it provides an effective method for magnetic field observation and analysis, which can be applied to fields such as geological exploration, mineral resource detection, and underground pipeline inspection; through magnetic imaging, spatial distribution information of the target can be obtained, providing important basis for subsequent analysis and decision-making.

[0099] Example 8: Based on Example 7, the process of achieving two-dimensional imaging of underground space through the distribution of θ provided in this embodiment of the invention includes the following steps:

[0100] The parameters are selected based on the geometry, magnetic properties, and distribution patterns of the target two-dimensional image, and a parameter set θ is generated to describe the position and magnetic moment vector of the magnetic dipole in two-dimensional space.

[0101] By establishing the correspondence between the parameter set θ and the distribution of magnetic dipoles in two-dimensional space, the parameter set is mapped to the corresponding spatial location; the position of the magnetic dipole on the observation plane is determined and used for magnetic field imaging.

[0102] By mapping the positions of magnetic dipoles in the parameter set θ onto the observation plane and combining them with the magnetic moment vector of the magnetic dipoles, a two-dimensional imaging result is generated. The two-dimensional imaging result can be a two-dimensional image or a heat map, showing the distribution of magnetic dipoles on the observation plane.

[0103] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first selects parameters based on the geometric shape, magnetic properties, and distribution patterns of the target's two-dimensional image to generate a parameter set θ, describing the position and magnetic moment vector of the magnetic dipole in two-dimensional space; by establishing the correspondence between the parameter set θ and the distribution of the magnetic dipole in two-dimensional space, the parameter set is mapped to the corresponding spatial position; the position of the magnetic dipole on the observation plane is determined and used for magnetic field imaging; by mapping the position of the magnetic dipole in the parameter set θ to the observation plane and combining it with the magnetic moment vector of the magnetic dipole, a two-dimensional imaging result is generated; the two-dimensional imaging result can be a two-dimensional image or a heat map, showing the distribution of the magnetic dipole on the observation plane. The above solution achieves two-dimensional magnetic field imaging of the target by generating the parameter set θ, establishing the correspondence between the parameter set and the distribution of the magnetic dipole, and the mapping and imaging process. It has the following significance: Accurate imaging of the target: By selecting a suitable parameter set θ, the geometric shape, magnetic properties, and distribution patterns of the target can be described. By establishing a correspondence between a parameter set and the distribution of magnetic dipoles, the parameter set is mapped to its corresponding spatial location. This allows for accurate determination of the magnetic dipole's position on the observation plane, and imaging is performed by combining this with the magnetic dipole's magnetic moment vector. In this way, accurate imaging of the target can be achieved, revealing its magnetic characteristics and distribution on the observation plane. Visualization of the two-dimensional imaging results: By mapping the magnetic dipole positions in the parameter set θ to the observation plane and combining this with the magnetic dipole's magnetic moment vector, two-dimensional imaging results can be generated. These results can be two-dimensional images or heatmaps, visually demonstrating the distribution of magnetic dipoles on the observation plane. Visualization helps users understand and analyze the target's magnetic field characteristics and provides important data for subsequent data processing and decision-making. Data processing and decision support: Implementing the above scheme allows for the acquisition of two-dimensional magnetic field imaging results of the target, which is of great significance in fields such as geological exploration, mineral resource detection, and underground pipeline inspection. Analysis and processing of the imaging results can extract the target's characteristic parameters, perform anomaly detection and classification, and provide support for decision-making and applications.

[0104] In summary, the significance of this embodiment lies in achieving two-dimensional magnetic field imaging of the target, accurately describing the target's magnetic characteristics and distribution, and supporting subsequent decision-making and applications through visualization and data processing. This provides an effective method for magnetic field observation and analysis, applicable to multiple fields such as geological exploration, resource detection, and environmental monitoring.

[0105] Example 9: As Figure 6 and Figure 7 As shown, based on Examples 1-8, the simulation experiment provided in this invention establishes a target magnetic measurement scenario in the electromagnetic calculation software ANSYS Maxwell, such as... Figure 6As shown, (a) is the side view of the xoz plane, and (b) is the top view of the xoy plane. The computational space of the simulation experiment is 40m × 40m × 9m. A rectangular coordinate system o-xyz is established, with the x-axis pointing due east, the y-axis pointing due north, and the z-axis pointing vertically upward. Therefore, the observation space in the x-direction is -7m to 33m, the observation space in the y-direction is -7m to 33m, and the observation space in the z-direction is -3m to 6m.

[0106] The geomagnetic field in space has the following characteristics: magnitude 55000 nT, magnetic tilt 55°, and magnetic declination -5°. At this time: B x =-2749.4764nT, B y =31426.6592nT, B z = -45053.3624nT.

[0107] The underground anomaly is a steel cube measuring 4m × 4m × 0.5m. The four vertices of the cube's upper surface are located at (8m, 8m, -0.5m), (8m, 12m, -0.5m), (12m, 12m, -0.5m), and (8m, 12m, -0.5m). A scalar magnetic field sensor is used to measure the magnetic field on the plane above the target. The observation plane is parallel to the xoy plane, measuring 20m × 20m and 0.5m in height. Figure 7 A contour map of the magnetic field on the observation plane.

[0108] To estimate the two-dimensional distribution of underground targets, a horizontal slice depth of z = -0.5m was set, with slice sizes of 0m-20m in both the x and y directions. The unit grid of the horizontal slice was 1m × 1m, resulting in 20 × 20 = 400 grids. Based on the OMP algorithm-based two-dimensional imaging method proposed in the patent, the magnetic dipole parameters of each grid were calculated, and the results are as follows. Figure 8 As shown. From Figure 8 As can be seen, the magnetic dipole modulus of most grids in the slice plane is 0, the distribution of non-zero magnetic dipole grids is basically consistent with the distribution of anomalous targets, and the outline of the targets is square, consistent with the real targets.

[0109] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A sparse two-dimensional magnetic imaging method, characterized by, The method comprises the following steps: The magnetic anomaly field generated by the ferromagnetic target is equivalent to the superposition of the magnetic fields generated by multiple magnetic dipoles on a two-dimensional horizontal slice; The two-dimensional horizontal slice is evenly divided into small rectangular grid units, and the magnetic moments of the magnetic dipoles in the grid where the target is located are non-zero, and the magnetic moments of the magnetic dipoles in the space without the target are all zero; The orthogonal matching pursuit algorithm is used to estimate the parameters of the magnetic dipoles, and two-dimensional magnetic imaging of the target is realized; The process of two-dimensional magnetic imaging comprises the following steps: The magnetic anomaly field of the target is represented by a sparse coefficient vector σ; the target is sparse in the solving space, that is, most of the magnetic dipoles have a coefficient of 0; By solving the L0 optimization problem, the magnetic dipole parameter estimation result is obtained; the L0 norm represents the number of non-zero elements, and the estimation value of the sparse coefficient vector σ is determined by minimizing the L0 norm and the residual of the observation data; The matrix form of the magnetic anomaly field generated by the target is represented as: s = D(θ)σ + n Where s represents the column vectorization of the observed magnetic anomaly field, D(θ) represents the dictionary corresponding to the parameter set, σ represents the sparse coefficient vector, and n represents the additive Gaussian white noise; considering that the observation plane is much larger than the interval where the target is located, the target or the non-zero magnetic dipole is sparse in the solving space; The magnetic dipole parameter estimation result is obtained by solving the L0 optimization problem in the formula; where ||·||0denotes the L0norm, is the estimated sparse magnetic dipole parameter vector, and ε is the noise level; the two-dimensional imaging of the subsurface space is achieved through the distribution of θ; The process of two-dimensional imaging of the underground space through the distribution of θ in the magnetic dipole parameter estimation result comprises the following steps: According to the geometric shape, magnetic properties and distribution law of the target two-dimensional image, the parameter set θ is generated, which describes the position and magnetic moment vector of the magnetic dipole in the two-dimensional space; By establishing the corresponding relationship between the parameter set θ and the distribution of the magnetic dipole in the two-dimensional space, the parameter set is mapped to the corresponding spatial position; the position of the magnetic dipole on the observation plane is determined and used for magnetic field imaging; By mapping the magnetic dipole position in the parameter set θ to the observation plane and combining the magnetic moment vector of the magnetic dipole, a two-dimensional imaging result is generated.

2. The sparse two-dimensional magnetic imaging method of claim 1, wherein, The process of evenly dividing the two-dimensional horizontal slice into small rectangular grid units comprises the following steps: The range of the two-dimensional horizontal slice is determined, that is, the length and width of the two-dimensional horizontal slice are determined; after receiving the two-dimensional horizontal slice to be divided, the size, characteristics and magnetic field resolution of the target grid are classified based on the preset search program; the division results of the search results are fused, and the minimum grid shape and size of the two-dimensional horizontal slice to be divided are determined according to the fusion results; According to the length, width and minimum grid size of the two-dimensional horizontal slice, the number of grid divisions of the two-dimensional horizontal slice is calculated; according to the calculated number of grids, the origin position of the coordinate system is determined, that is, the center point of the two-dimensional horizontal slice; the direction of the polar axis is determined, that is, a fixed direction on the two-dimensional horizontal slice, the size of each annular region is determined according to the minimum grid; the annular region is evenly divided in the polar coordinate system according to the grid size; the starting angle and angle range of each annular region are determined to obtain the coordinate range of each annular region grid; the two-dimensional horizontal slice is evenly divided; The boundary of each annular area grid is determined in a polar coordinate system, that is, the coordinates of four vertices of each annular area grid are determined.

3. The sparse two-dimensional magnetic imaging method of claim 1, wherein, The process of uniformly dividing the two-dimensional horizontal slice into the smallest rectangular grid unit adopts a model of target two-dimensional imaging, and for a near-surface target with complex shape, the generated magnetic anomaly field is equivalent to the magnetic field generated by multiple magnetic dipoles on a horizontal slice; the two-dimensional horizontal slice is uniformly divided into the smallest rectangular grid unit, and the magnetic moment of the magnetic dipole in the grid where the target is located is non-zero, and the magnetic moment of the magnetic dipole in the space without the target is zero; The two-dimensional imaging of the target is an inverse problem of the above, and the parameters of the equivalent magnetic dipole are extracted by measuring the observed magnetic anomaly field ΔT above the target, and the two-dimensional imaging result of the target can be described according to the position distribution and the modulus value of the magnetic moment of the magnetic dipole.

4. The sparse two-dimensional magnetic imaging method of claim 1, wherein, The magnetic field sensor measures the magnetic field in the two-dimensional plane above the target, and the measurement plane is much larger than the spatial distribution of the target, and the target is sparse in the solving space, so the horizontal position and the modulus value of the magnetic moment vector of the non-zero magnetic dipole are obtained by using the orthogonal matching pursuit algorithm; the distribution result of the non-zero magnetic dipole on the two-dimensional plane is the imaging result of the abnormal target.

5. The sparse two-dimensional magnetic imaging method of claim 4, wherein, The magnetic field sensor is one or more of an optically pumped magnetometer, a Hall effect magnetic field sensor, a fluxgate, a magnetoresistive sensor, and a superconducting quantum magnetometer.

6. The sparse two-dimensional magnetic imaging method of claim 5, wherein, In a multi-sensor system, an anti-interference method between different magnetic field sensors includes the following steps: The original electromagnetic wave of the magnetic field sensor is converted into a digital signal, the number of magnetic field sensors in the area where the magnetic field sensor is located is determined, and it is judged whether the number of magnetic field sensors exceeds a preset number threshold, which is a trigger condition for starting the anti-interference program of the magnetic field sensor; When the number of magnetic field sensors exceeds the preset number threshold, the frequency, phase and amplitude of the digital signal are adjusted using an adaptive neural network algorithm, the interference of the digital signal is reconstructed, and a digital interference signal is obtained; The digital interference signal and the digital signal are superimposed, the power of the digital interference signal is adjusted according to the pre-set weight, so as to eliminate the interference signal in the digital signal, and a processed digital signal is obtained.

7. The sparse two-dimensional magnetic imaging method of claim 1 wherein, The magnetic field observation plane is much larger than the distribution range of the target, most of the grid magnetic dipoles in the two-dimensional imaging plane have a modulus value of 0, and only the magnetic dipoles in the target distribution position have a non-zero modulus value; the horizontal position and the modulus value of the magnetic moment vector of the non-zero magnetic dipole are obtained by using the orthogonal matching pursuit algorithm; the measured magnetic field signal is input, a perception matrix is established according to the established magnetic dipole model, and the parameters of the non-zero magnetic dipole are iteratively solved until the calculation error is less than a preset threshold; the distribution of the non-zero grid magnetic dipole corresponds to the two-dimensional imaging result of the target, and the two-dimensional imaging of the target is realized.

8. The sparse two-dimensional magnetic imaging method of claim 1, wherein, The two-dimensional imaging result is a two-dimensional image or a heat map, which shows the distribution of the magnetic dipole on the observation plane.

Images