Buried ferromagnetic target detection and positioning method based on adaptive multipole model

Abstract

The application provides a buried ferromagnetic target detection and positioning method based on an adaptive multipole model, which comprises the following steps: collecting magnetic field scalar data of a target area; processing bad points of the magnetic field scalar data; filtering and linearly interpolating the magnetic field scalar data; drawing a magnetic field contour map through the calculated data, and marking positive and negative extreme value blocks in the contour map; constructing an adaptive multipole model according to the categories of the extreme value blocks; and optimizing and inverting the physical parameters of the target through the adaptive multipole model. The application effectively eliminates environmental noise, sensor abnormal values and geological background interference through three-level data cleaning of 'bad point processing + filtering + linear interpolation', retains real magnetic anomaly signals and improves the data signal-to-noise ratio. The automatic marking function of the extreme value blocks of the magnetic field contour map can intuitively identify the magnetic anomaly center position and avoid artificial interpretation errors.

Description

Technical Field

[0001] This application relates to the field of magnetic detection technology, and more specifically, to a method for detecting and locating buried magnetic targets based on an adaptive multi-dipole model. Background Technology

[0002] The results of this application can improve the success rate of unexploded ordnance detection and reduce the false alarm rate. They can be widely applied to the disposal of unexploded ordnance and have significant social benefits.

[0003] Magnetic detection technology, as a non-contact method for detecting ground features, is widely used in fields such as unexploded ordnance (UXO) identification and pipeline location. Its core principle is based on the spatiotemporal characteristic analysis of magnetic anomaly signals. By measuring the distribution of geomagnetic field anomalies, it inverts the physical parameters of underground targets, such as magnetic moment, location, and burial depth.

[0004] In traditional magnetic detection techniques, single-dipole and multi-dipole models serve as core theoretical tools for inverting ferromagnetic targets. They simulate the magnetic field distribution by equating the target to a single or multiple point magnetic sources (dipoles). Their physical basis stems from the approximate solution to the static magnetic field in Maxwell's equations, where the magnetic field strength generated by a target exhibits a specific functional relationship with its magnetic moment and position. For isolated unexploded ordnance or targets with high dispersion (i.e., the distance between any two targets is greater than 2.5 times their respective scales, and the shortest distance between the magnetic field sensor and the target is greater than 2.5 times the target's scale), single-dipole and multi-dipole models can effectively invert the target's position and magnetic moment parameters with high accuracy.

[0005] However, the model gradually reveals its limitations in complex scenarios, especially when facing multiple low-discreteness targets, where multiple targets are superimposed and have large interference, or non-uniform magnetized bodies, such as rusted iron structures, the model error increases significantly.

[0006] Therefore, a method for detecting and locating buried magnetic targets based on an adaptive multi-dipole model is proposed to solve one of the aforementioned technical problems. Summary of the Invention

[0007] The purpose of this application is to provide a method for detecting and locating buried magnetic targets based on an adaptive multi-dipole model, which can solve at least one of the aforementioned technical problems. The specific solution is as follows:

[0008] According to a specific embodiment of this application, this application provides a method for detecting and locating buried geomagnetic targets based on an adaptive multi-dipole model, the method comprising the following steps:

[0009] Collect magnetic field scalar data of the target area;

[0010] The bad points in the magnetic field scalar data are processed;

[0011] The magnetic field scalar data is filtered and linearly interpolated.

[0012] A magnetic field contour map is drawn using the calculated data, and positive and negative extreme value blocks are marked on the contour map.

[0013] An adaptive multi-dipole model is constructed based on the category of the extreme value blocks;

[0014] The target physical parameters are inverted through rule optimization of the adaptive multi-dipole model.

[0015] Furthermore, the magnetic field scalar data of the target area is collected by drawing grid lines in the target area, using an optically pumped magnetometer or a magnetic detector, and obtaining the magnetic field scalar values ​​at the intersections of the grid lines according to the set rules. The magnetic field scalar value data is entered into a two-dimensional array B in the order of the measurement points. The two-dimensional array B has m rows and n columns, and corresponds one-to-one with the intersections of the grid lines.

[0016] Furthermore, the processing of bad points in the magnetic field scalar data includes: calculating the lateral gradient of the magnetic field scalar value data. Longitudinal gradient scalar gradient Scalar gradient average Scalar gradient mean absolute deviation The median of the absolute deviation of the scalar gradient mean ;

[0017] ,

[0018] ,

[0019] ,

[0020] ,

[0021] if ,but If a pixel is identified as a bad pixel, its value is removed, and a replacement value is calculated using the following formula. , where e is the coefficient.

[0022] Furthermore, the value of e is 2.6 to 6.3.

[0023] Furthermore, the data point spacing after linear interpolation is no greater than 0.1m.

[0024] Furthermore, the extreme value blocks are categorized as follows: paired positive and negative extreme value blocks and unipolar blocks; the contour lines of the extreme value blocks are locally closed, and the maximum diameter of the outermost ring of the extreme value region is greater than d.

[0025] max

[0026] =2.3*d; d is the side length of the cell grid.

[0027] Furthermore, the rules for modeling the unipolar block are as follows: the number of dipoles is N2∈[3,12], the dipoles are randomly distributed in a circle with the extreme value center point as the center and a diameter of d. s =g*d max Inside the circle; the magnetic moment has T degrees of freedom. max / T min ∈[1.3, 5.2], T max For the maximum magnetic moment, T min is the minimum magnetic moment, g is a coefficient, simulating irregular targets.

[0028] Furthermore, the rules for modeling the paired positive and negative extremum blocks are as follows: the number of dipoles is N2 = floor(d1 / 0.22), and they are linearly and uniformly distributed, where d1 is the distance between the center points of the positive and negative extrema, and floor is a floor function; the arrangement is along the direction of the line connecting the positive and negative extremum points, with a length L ∈ [0.82d1, 1.35d1] and a direction angle θ ∈ [a1 - 9.3°, a1 + 11.2°], where a1 is the direction angle; the magnetic moment degree of freedom is T. max / T min ∈[1.6, 8.2], T max For the maximum magnetic moment, T min To minimize the magnetic moment, simulate a single target.

[0029] Furthermore, the optimization objective function is as follows:

[0030] ,

[0031] in, Let be the magnetic moment vector of the k-th magnetic dipole. Let be the displacement vector from the k-th magnetic dipole to the observation point (i, j). These are the observations after defect removal and smoothing. Where M is the number of dipoles, N is the number of rows of measurement points, and N is the number of columns of measurement points. ρ is the magnetic permeability in vacuum.

[0032] According to specific embodiments of this application, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in any of the preceding claims.

[0033] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0034] This invention employs a three-stage data cleaning process—"bad pixel processing + filtering + linear interpolation"—to effectively eliminate environmental noise, sensor anomalies, and geological background interference, preserving the true magnetic anomaly signal and improving the data signal-to-noise ratio. The automatic marking function for extreme value blocks in the magnetic field contour map allows for intuitive identification of the magnetic anomaly center location, avoiding errors from manual interpretation. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0036] Figure 1 This is a schematic diagram of the walking survey line and grid lines of the handheld magnetic detector in the area to be tested, as shown in an embodiment of this application.

[0037] Figure 2 This is a schematic diagram of the magnetic field contour lines of the area to be measured, as shown in an embodiment of this application.

[0038] Figure 3 This is a schematic diagram of the electronic device structure shown in an embodiment of this application.

[0039] In the diagram: 401, processing device; 402, ROM; 403, RAM; 404, bus; 405, I / O interface; 406, input device; 407, output device; 408, storage device; and 409, communication device. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] 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 application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0042] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0043] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0044] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0045] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0046] A method for detecting and locating buried geomagnetic targets based on an adaptive multi-dipole model, comprising the following steps:

[0047] Step 1: Collect scalar magnetic field data of the target area: Draw a grid of lines in the target area (6m*6m), with each grid cell being a square and a side length d ranging from 0.2m. The intersections of the grid lines are the measuring points, with a total of 31*31=961 measuring points. Use a magnetic detector equipped with an optically pumped magnetometer, with a calibration accuracy better than 0.3nT and a data sampling rate of 10Hz. The operator turns on the magnetic detector, and after the readings are normal, starts measuring from the upper left corner of the grid in the area to be measured, such as... Figure 1 As shown, the handheld scalar magnetic detector is steadily moved along the survey line and in the direction indicated by the arrow at a speed of 0.8 m / s. It pauses for 2 seconds at a distance of 0.2 * 1.8 = 0.36 m directly above each measuring point, reads the stable magnetic field scalar value, and records it. Simultaneously, the recorded magnetic field scalar values ​​are entered into a two-dimensional array B (31 rows, 31 columns) in the order of the measuring points, with each magnetic field scalar value corresponding to a specific measuring point location.

[0048] Step 2: Process the magnetic field scalar data Handling bad points: processing the magnetic field scalar value data Calculate the lateral gradient Longitudinal gradient scalar gradient Scalar gradient average Scalar gradient mean absolute deviation The median of the absolute deviation of the scalar gradient mean ;

[0049] ,

[0050] ,

[0051] ,

[0052] ,

[0053] if ,but If a pixel is identified as a bad pixel, its value is removed, and a replacement value is calculated using the following formula. , where e is the coefficient.

[0054] Where e=3.5. Five bad pixels were ultimately found and replaced.

[0055] Step 3: Process the magnetic field scalar data Filtering and linear interpolation calculations are performed on two-dimensional magnetic field scalar data. Implement two-dimensional filtering to suppress high-frequency noise;

[0056] ,

[0057] Where k=5, k1=2, This is the filtered magnetic field scalar data.

[0058] right Perform two-dimensional linear interpolation, and the spacing between data points after interpolation should not exceed 0.1m.

[0059] Step 4: Draw a magnetic field contour map using the calculated data, such as... Figure 2 As shown, positive and negative extreme value blocks are marked in the contour map.

[0060] Step 5: Construct an adaptive multi-dipole model based on the categories of the extreme value blocks; the categories of the extreme value blocks include: paired positive and negative extreme value blocks and unipolar blocks; the contour lines of the extreme value blocks are locally closed, and the maximum diameter of the outermost ring of the extreme value region is greater than d. max =2.3*d; d is the side length of the unit grid. In this embodiment, approximately concentric circular blocks are found in the drawn contour map, and the outermost diameter of the extreme value region is greater than d. max =0.2*2.3=0.46m.

[0061] Step Six: Optimize and invert the target physical parameters using the rules of the adaptive multi-dipole model: The rules of the adaptive multi-dipole model are: pairwise positive and negative extremum block modeling rules and unipolar block modeling rules; a unipolar block is an isolated extremum region, adjacent extremum regions do not have symmetry, and the distance between the nearest extremum regions is greater than 7.6*0.2=1.52m. The modeling rule is that the number of dipoles is N2∈[3,12], and the dipoles are randomly distributed in a circle with the extremum center point as the center and a diameter of d. s =g*d max Inside the circle; the magnetic moment has T degrees of freedom. max / T min ∈[1.3, 5.2], T max For the maximum magnetic moment, T min Let g be the minimum magnetic moment and g be a coefficient, simulating an irregular target. In this embodiment, the extreme value blocks are paired positive and negative extreme value blocks, with the positive and negative extreme value regions adjacent to each other and symmetrical. The distance between the center points of the positive and negative extreme values ​​is [missing value]. d 1 =2.32m; Modeling rule is the number of dipoles: N 2 =floor(d1 / 0.22)=10, uniform spacing; arrangement constraints: along the direction of the line connecting the positive and negative extreme points (direction angle a1≈95°), length L∈[1.9, 3.1], direction angle θ∈[85.7°, 106.2°]; magnetic moment degrees of freedom: T max / T min ∈[1.6, 8.2], T max For the maximum magnetic moment, T min To minimize the magnetic moment, simulate a single target.

[0062] Using an optimization algorithm, under the constraints of the adaptive multi-dipole model construction rules, we iteratively search for the objective parameter combination that best matches the model's forward values ​​with the observed values. The optimization objective function is as follows:

[0063] ,

[0064] in, Let be the magnetic moment vector of the k-th magnetic dipole. Let be the displacement vector from the k-th magnetic dipole to the observation point (i, j). These are the observations after defect removal and smoothing. Where M is the number of dipoles, N is the number of rows of measurement points, and N is the number of columns of measurement points. ρ is the magnetic permeability in vacuum.

[0065] After optimization and inversion, the target length was calculated to be approximately 2.7m and the burial depth to be 3.2m.

[0066] like Figure 3As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the method steps described in the above embodiment.

[0067] This application provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0068] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0069] like Figure 3 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0070] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0071] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a storage device 408, or installed from a ROM 402. When the computer program is executed by the processing device 401, it performs the functions defined in the methods of the embodiments of this application.

[0072] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0073] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0074] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A method for detecting and locating buried magnetic targets based on an adaptive multi-dipole model, characterized in that, The method includes the following steps: Collect magnetic field scalar data of the target area; The bad points in the magnetic field scalar data are processed; The magnetic field scalar data is filtered and linearly interpolated. A magnetic field contour map is drawn using the calculated data, and positive and negative extreme value blocks are marked on the contour map. An adaptive multi-dipole model is constructed based on the category of the extreme value blocks; The physical parameters of the inverted target are optimized using the rules of the adaptive multi-dipole model. The extreme value blocks are categorized into: paired positive and negative extreme value blocks and unipolar blocks; the contour lines of the extreme value blocks are locally closed, and the maximum diameter of the outermost ring of the extreme value region is greater than d. max =2.3*d; d is the side length of the cell mesh; The objective function is optimized as follows: ， in, Let be the magnetic moment vector of the k-th magnetic dipole. Let be the displacement vector from the k-th magnetic dipole to the observation point (i, j). These are the observations after defect removal and smoothing. Where M is the number of dipoles, N is the number of rows of measurement points, and N is the number of columns of measurement points. ρ is the magnetic permeability in vacuum.

2. The method according to claim 1, characterized in that, The magnetic field scalar data of the target area is collected by drawing grid lines in the target area, using an optically pumped magnetometer or a magnetic detector, and obtaining the magnetic field scalar values ​​at the intersections of the grid lines according to the set rules. The magnetic field scalar value data is entered into a two-dimensional array B in the order of measurement points. The two-dimensional array B has m rows and n columns, and corresponds one-to-one with the intersections of the grid lines.

3. The method according to claim 2, characterized in that, Processing bad pixels in the magnetic field scalar data includes: calculating the lateral gradient of the magnetic field scalar data. Longitudinal gradient scalar gradient Scalar gradient average Scalar gradient mean absolute deviation The median of the absolute deviation of the scalar gradient mean ; ， ， ， ， if ,but If a pixel is identified as a bad pixel, its value is removed, and a replacement value is calculated using the following formula. , where e is the coefficient.

4. The method according to claim 3, characterized in that, The value of e is 2.6 to 6.

3.

5. The method according to claim 1, characterized in that, After linear interpolation calculation, the spacing between data points is no greater than 0.1m.

6. The method according to claim 1, characterized in that, The rules for modeling the unipolar block are as follows: the number of dipoles is N2∈[3,12], the dipoles are randomly distributed, and a circle with the extreme value center point as the center and a diameter of d is formed. s =g*d max Inside the circle; the magnetic moment has T degrees of freedom. max / T min ∈[1.3, 5.2], T max For the maximum magnetic moment, T min is the minimum magnetic moment, g is a coefficient, simulating irregular targets.

7. The method according to claim 1, characterized in that, The rules for modeling paired positive and negative extremum blocks are as follows: the number of dipoles is N2 = floor(d1 / 0.22), and they are linearly and uniformly distributed, where d1 is the distance between the center points of the positive and negative extrema, and floor is the floor function; they are arranged along the direction of the line connecting the positive and negative extremum points, with a length L ∈ [0.82d1, 1.35d1] and a direction angle θ ∈ [a1 - 9.3°, a1 + 11.2°], where a1 is the direction angle; the magnetic moment degrees of freedom are T. max / T min ∈[1.6, 8.2], T max For the maximum magnetic moment, T min To minimize the magnetic moment, simulate a single target.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

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