Magnetic multi-target positioning and magnetic moment inversion method based on measuring line vertical gradient

Abstract

The invention belongs to the field of magnetic target body positioning, and relates to a measuring line vertical gradient-based magnetic multi-target positioning and magnetic moment inversion method, which comprises the following steps of: obtaining a unit orthogonal primary function and a primary function coefficient corresponding to each component of magnetic anomaly gradient data vertical to a measuring line, and calculating an energy curved surface of the magnetic anomaly gradient data vertical to the measuring line; detecting a local energy peak value of the energy curved surface, obtaining an energy peak value ratio sequence, obtaining a target number and horizontal position estimation corresponding to the target according to an energy sudden change value in the energy ratio sequence, and calculating a target depth corresponding to a measuring line vertical magnetic anomaly gradient energy extreme value by using a dichotomy method, and constructing a linear equation set about the target magnetic moment by using the measuring line vertical magnetic anomaly gradient data and the unit orthogonal basis function, and obtaining target magnetic moment parameter estimation through a least square method. According to the invention, the anisotropy difference of the estimation precision of the horizontal position of the target is effectively reduced, and the problem of target detection omission caused by large difference between the target burial depth and the magnetic moment module value in multi-target positioning is improved.

Description

Technical Field

[0001] The present application relates to the field of underground magnetic target positioning, and in particular to a method for magnetic multi-target positioning and magnetic moment inversion based on the vertical gradient of a survey line. Background Art

[0002] Magnetic anomaly detection technology detects and locates ferromagnetic targets by measuring the magnetic anomaly field generated by the Earth's magnetic field. It is widely used in fields such as unexploded ordnance detection, mineral exploration, archaeological research, pipeline positioning, and industrial non-destructive testing. Traditional magnetic anomaly detection technology primarily relies on one-dimensional survey line data. While this technology can effectively determine the presence of targets, precise positioning in multi-target scenarios requires analysis of two-dimensional survey area data.

[0003] Currently, magnetic target positioning methods based on two-dimensional survey area data mainly include Euler deconvolution, differential evolution multi-target positioning, total field orthogonal basis function method, and stochastic resonance technology combined with image processing. Euler deconvolution method rapidly estimates magnetic source location through three-dimensional inversion of magnetic anomaly data, but suffers from noise sensitivity, false solutions, and dependence on prior information such as structure index. Differential evolution multi-target positioning method estimates target parameters using magnetic dip and normalized source intensity, but the complexity of parameter optimization increases and accuracy decreases as the number of targets increases. Total field two-dimensional orthogonal basis function method achieves positioning in high-noise environments through energy map conversion and threshold recognition, but faces challenges such as difficulty in adaptively selecting energy thresholds, sensitivity to magnetic moment direction, and large vertical positioning errors. Stochastic resonance technology combined with image processing achieves rapid horizontal positioning, but the anomaly center offset in specific magnetic moment directions leads to significant positioning errors.

[0004] Existing methods are generally limited by parameter dependence, insufficient noise immunity and positioning accuracy issues under complex magnetic moment directions in multi-target scenarios. There is an urgent need to develop more robust and adaptive magnetic target positioning algorithms to improve the reliability and efficiency of practical engineering applications. Summary of the Invention

[0005] The embodiments of the present application provide a magnetic multi-target positioning and magnetic moment inversion method based on the vertical gradient of the survey line, which solves the limitations of the existing magnetic multi-target positioning method based on two-dimensional survey area data, especially the problems that the target detection accuracy depends on prior information, the parameter estimation accuracy is affected by the number of targets, the positioning results are sensitive to changes in the magnetic moment direction, and the horizontal positioning accuracy has anisotropic differences.

[0006] This application is implemented in this way. A method for magnetic multi-target positioning and magnetic moment inversion based on a survey line perpendicular magnetic gradient, the method comprising: S1 performing low-pass filtering and interpolation on magnetic anomaly gradient data perpendicular to the survey line to obtain two-dimensional magnetic anomaly gradient data of a survey area with uniform grid distribution; S2 obtains the unit orthogonal basis functions corresponding to each component of the magnetic anomaly gradient data perpendicular to the survey line according to the magnetic dipole model; S3 uses a two-dimensional sliding window to perform discrete integration on each component of the magnetic anomaly gradient data perpendicular to the survey line and the corresponding unit orthogonal basis function to obtain all basis function coefficients; S4 calculates the energy surface of the vertical magnetic anomaly gradient data of the survey line according to the basis function coefficients, detects the local energy peak of the energy surface, calculates the ratio between adjacent energy peaks, obtains the energy ratio sequence, and obtains the target number and the corresponding horizontal position estimation of the target according to the energy mutation value in the energy ratio sequence; S5 selects a single target and uses the dichotomy method to calculate the target depth corresponding to the extreme value of the vertical magnetic anomaly gradient energy of the survey line to estimate the target burial depth; S6 constructs a set of linear equations about the target magnetic moment using the vertical magnetic anomaly gradient data of the survey line and the unit orthogonal basis function, and obtains the target magnetic moment parameter estimation through the least squares method.

[0007] Furthermore, S1 includes: S11 aligns the survey line in the survey area with the X direction and uses a magnetic total field gradiometer composed of four total field magnetometers perpendicular to the survey line direction to obtain magnetic anomaly gradient data along the pre-planned survey line in the survey area; S12 defines the distance between the two total field magnetometers in the Z direction as the baseline distance in the Z direction. The difference between the measured values ​​of the two total field magnetometers and the baseline distance in the Z direction is used to express the total magnetic field gradient in the Z direction. Similarly, the total magnetic field gradient in the Y direction is obtained. S13 uses a low-pass filter with a passband cutoff frequency of 2 Hz to denoise the magnetic anomaly gradient data, and then uses the Kriging interpolation method to interpolate to obtain two-dimensional magnetic anomaly gradient field data with uniformly distributed grid sizes.

[0008] Furthermore, S2 includes: S21 uses the magnetic anomaly gradient model of the magnetic dipole to convert the vertical magnetic anomaly gradient at the measuring point into Represented as a set of basis functions A linear combination of S22 According to the Schmidt orthogonalization principle, the basis function Perform two-dimensional orthogonalization to obtain the corresponding unit orthogonal basis function ; S23 uses unit orthogonal basis functions Reconstructing magnetic anomaly gradients Using unit orthogonal basis functions and basis function coefficients express.

[0009] Furthermore, S3 includes: S31 uses unit orthogonal basis functions The unit orthogonal basis function properties, calculate the basis function coefficients ; , S32 basis function coefficients The continuous integral approximation of is: , in and is the horizontal grid size, P and Q are the control ranges of numerical integration, and are the midpoints of the two horizontal directions of the numerical integration region, then Represents the basis function coefficients of the center point of the integration region; S33 sets the window size to 3 to 6 times the distance z from the measurement plane to the target by multiplying both sides of the equation by , the basis function coefficients are expressed as: , Furthermore, S4 specifically includes: Calculate the magnetic anomaly gradient perpendicular to the survey line and magnetic anomaly gradient Energy function ; By sliding the numerical integration window in the entire survey area grid, traversing the entire survey area, the magnetic anomaly gradient is obtained and magnetic anomaly gradient The energy surface of Detect local energy peaks of the energy surface, arrange the local energy peaks in ascending order to obtain an energy increasing sequence, calculate the ratio between adjacent energy peaks, and obtain an energy ratio sequence; In the energy ratio sequence, the energy mutation value divides the energy ratio sequence into two segments, so that the cost function is minimized; The energy mutation value corresponds to the first Value , then the energy of the target magnetic anomaly gradient signal is recorded as , in the energy increasing sequence, when the energy value is greater than or equal to When , it is determined that the energy peak is generated by the target gradient signal, and its horizontal position on the energy surface is the target horizontal position.

[0010] Furthermore, S5 includes: selecting the detected target, in the distance range from the target to the measurement plane ( Change the measurement plane distance range The estimated value of makes its energy approach the maximum value, corresponding to Is the true depth value of the target, where To measure the plane height; repeat the operation until the depth estimation of all targets is completed.

[0011] Furthermore, S6 includes: After completing the target horizontal position and depth estimation, S61 selects Target, on the magnetic anomaly gradient surface The top is cut off at its horizontal position The magnetic anomaly window of the grid node in the window is expressed as ; S62 separates the target magnetic moment and expresses it as: , in, , , ; is the unit direction vector of the Earth's magnetic field. is the basis function In the The value of each measuring point.

[0012] S63 will target position Substitute basis functions , the target magnetic moment can be estimated ; S64 repeats the above operation, selecting all targets one by one until the magnetic moment estimation of all targets is completed.

[0013] Compared with the prior art, the present application has the following advantages: Without reducing the interval between survey lines, the method of this application significantly reduces the target positioning error perpendicular to the survey line direction and improves the positioning accuracy parallel to the survey line direction, thereby achieving higher target positioning efficiency and accuracy. At the same time, this method is insensitive to changes in the direction of the magnetic moment, which enhances the robustness of the positioning results. The energy mutation value detection method solves the problem of selecting a fixed energy threshold in the original two-dimensional orthogonal basis function method and achieves accurate identification of multiple targets. In addition, while completing the accurate estimation of the target's horizontal position, the target's magnetic moment can be accurately estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Flowchart of the magnetic multi-target positioning and magnetic moment inversion method based on the vertical gradient of the survey line provided for the implementation example of this application; Figure 2 Magnetic anomaly gradient provided for the implementation example of this application The energy surface of Figure 3Magnetic anomaly gradient provided for the implementation example of this application The energy surface of Figure 4 An energy surface defined by the magnetic anomaly gradient provided in the embodiment of this application; Figure 5 The local energy peak increasing curve provided for the implementation example of this application; Figure 6 The local energy peak ratio change curve provided for the implementation example of this application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0016] See also Figure 1 As shown, the present application provides a magnetic multi-target positioning and magnetic moment inversion method based on the vertical gradient of the survey line, including: S1 performs low-pass filtering and interpolation on the magnetic anomaly gradient data perpendicular to the survey line to obtain two-dimensional magnetic anomaly gradient data of the survey area with uniform grid distribution; S2 obtains the unit orthogonal basis functions corresponding to each component of the magnetic anomaly gradient data perpendicular to the survey line according to the magnetic dipole model; S3 uses a two-dimensional sliding window to perform discrete integration on each component of the magnetic anomaly gradient data perpendicular to the survey line and the corresponding unit orthogonal basis function to obtain all basis function coefficients; S4 calculates the energy surface of the vertical magnetic anomaly gradient data of the survey line according to the basis function coefficients, detects the local energy peak of the energy surface, calculates the ratio between adjacent energy peaks, obtains the energy ratio sequence, and obtains the target number and the corresponding horizontal position estimation of the target according to the energy mutation value in the energy ratio sequence; S5 selects a single target and uses the dichotomy method to calculate the target depth corresponding to the extreme value of the vertical magnetic anomaly gradient energy of the survey line to estimate the target burial depth; S6 constructs a set of linear equations about the target magnetic moment using the vertical magnetic anomaly gradient data of the survey line and the unit orthogonal basis function, and obtains the target magnetic moment parameter estimation through the least squares method.

[0017] S1 specifically includes: S11 aligns the survey line in the survey area with the X direction and uses a magnetic total field gradiometer composed of four total field magnetometers perpendicular to the survey line direction to obtain magnetic anomaly gradient data along the pre-planned survey line in the survey area; S12 defines the distance between the two total field magnetometers in the Z direction as the baseline distance in the Z direction. The difference between the measured values ​​of the two total field magnetometers and the baseline distance in the Z direction is used to express the total magnetic field gradient in the Z direction. Similarly, the total magnetic field gradient in the Y direction is obtained. S13 uses a low-pass filter with a 2Hz cutoff frequency to denoise the magnetic anomaly gradient data. Kriging interpolation is then used to obtain two-dimensional magnetic anomaly gradient data with a uniformly distributed grid size. Kriging interpolation uses the semivariogram function, which describes the spatial autocorrelation between data points.

[0018] In a specific embodiment, the measurement area size is 30×20m, and the height of the measurement plane is The gradient measurement system moves at a constant speed of 1 m / s along the X-axis (north-south direction) within the measurement plane. The sampling frequency is 10 Hz, so the interval between adjacent sampling points is 0.1 m. The length of a single survey line is 30 m, and the interval between survey lines is 1 m. Assume that the geomagnetic inclination is 60° and the declination is -10°. Five magnetic dipoles were randomly generated below the measurement area. Their position parameters were (5 m, 6 m, 0.37 m), (10 m, 15 m, 0.22 m), (17 m, 10 m, 0.56 m), (20 m, 14 m, 0.73 m), and (25 m, 8 m, 0.81 m). Their magnetic moment parameters were (3.21 A∙m^2, -1.53 ​​A∙m^2, -3.01 A∙m^2), (-1.65 A∙m^2, -3.78 A∙m^2, 2.72 A∙m^2), (1.43 A∙m^2, 2.70 A∙m^2, 0.70 A∙m^2), and (-2.77 A∙m^2, 1.59 A∙m^2, -3.25 A∙m^2). Gaussian white noise with a mean of 0 and a standard deviation of 20 nT / m is added to the generated magnetic anomaly gradient data of the survey area.

[0019] The magnetic anomaly gradient data generated according to the survey line trajectory and Perform 2H low-pass filtering and Kriging interpolation processing to obtain the following Figure 2 and Figure 3 As shown in the magnetic anomaly gradient surface, it can be seen that the magnetic anomaly gradient signals of some dipoles are submerged by noise, and all targets cannot be identified from them.

[0020] Step S2 specifically includes: S21 uses the magnetic anomaly gradient model of the magnetic dipole to convert the vertical magnetic anomaly gradient at the measuring point into Represented as a set of basis functions A linear combination of , , , , , in, is the magnetic dipole position To the measuring point The distance vector to S 22 According to the Schmidt orthogonalization principle, the basis function Perform two-dimensional orthogonalization to obtain the corresponding unit orthogonal basis function ; , , Unit orthogonal basis functions It's about variables and variables The function has orthogonality and identity, satisfying the following formula , S23 uses unit orthogonal basis functions Magnetic anomaly gradient Refactored to: , in , The magnetic anomaly gradient Projection to the corresponding orthogonal basis function The basis function coefficients in space, is the vacuum permeability, is the magnetic moment modulus.

[0021] Basis function coefficients The target magnetic moment direction is included , the direction of the geomagnetic field and the distance from the target to the measurement plane ,because is unknown, so the basis function coefficients It cannot be obtained by direct calculation.

[0022] Step S3 specifically includes: S31 uses unit orthogonal basis functions The unit orthogonal basis function properties, calculate the basis function coefficients ,formula: , S32 Since the magnetic anomaly gradient data measured by the magnetic measurement system is discrete, the basis function coefficient The continuous integral of is approximately expressed as the discrete integral shown below.

[0023] , in and is the horizontal grid size, P and Q are the control ranges of numerical integration, and are the midpoints of the two horizontal directions of the numerical integration region, then The unit orthogonal basis function coefficients representing the center point of the integration region.

[0024] S33 sets the window size to 3 to 6 times the distance z from the measurement plane to the target. Since the actual distance z is unknown, the middle value of z within the target depth range is generally selected as the preset value. The coefficients contain unknown parameters , so by multiplying both sides of the equation Eliminating its influence, the actual calculated basis function coefficients can be expressed as: , In one embodiment, S4 calculates the vertical magnetic gradient energy of the survey line and obtains the number of targets and the corresponding horizontal position estimation of the targets through local energy peak detection and mutation value analysis on the energy surface. Specifically, it includes: S41 calculates the magnetic anomaly gradient perpendicular to the survey line and magnetic anomaly gradient Energy function , as shown below , in The energy value of the center of the numerical integration window is obtained by sliding the numerical integration window in the entire survey area grid to traverse the entire survey area and obtain the magnetic anomaly gradient and magnetic anomaly gradient Energy Surface S42 uses the energy function When locating the target, the positioning error of the target in the Y-axis direction is greater than that in the X-axis direction, so a new energy function is defined

[0025] , S43 detects the local energy peak of the energy surface. Due to the presence of noise, the detected local energy peak includes the energy peak of the target and the energy peak of the noise. The local energy peaks are arranged in ascending order to obtain an energy ascending sequence. , calculate the ratio between adjacent peaks and obtain the energy ratio sequence, where The energy ratio can be expressed as: , The energy generated by the S44 magnetic anomaly gradient signal is much greater than the energy generated by the noise. Therefore, when the energy ratio sequence mutates, the energy ratio of the mutation is considered to be the ratio of the energy of the magnetic anomaly gradient signal to the energy of the noise. In the energy mutation value Divide the energy ratio sequence into and Two segments, so that the cost function shown in the following formula is minimized.

[0026] , in and are the cost functions of the two segments before and after the energy mutation value, and their expressions are shown as follows: , , in, , .

[0027] S45 energy mutation value Corresponding to the energy ratio sequence The values, , then the energy of the target magnetic anomaly gradient signal is recorded as In the energy increasing sequence When the energy value is greater than or equal to When , it is determined that the energy peak is generated by the target gradient signal, and its horizontal position on the energy surface is the target horizontal position .

[0028] In this embodiment, the energy surface at the detection plane is as follows: Figure 4 As shown in the figure, the energy surface has been normalized for ease of display. There are two obvious energy peaks, and the energy generated by other targets has been completely obscured.

[0029] First, the local peak of the energy surface is detected. Figure 4 The results shown in the figure show that the identified local energy peaks contain both the energy generated by the dipole gradient signal and the energy generated by the noise. The energy peaks are arranged in ascending order, and the results are shown in the figure. Figure 5 Then, the energy ratio sequence is calculated , the results are as follows Figure 6 As shown in the figure, the energy ratio of the mutation point is 98.47, and the corresponding energy value is 6.09×10−4. Therefore, in the energy peak sequence In the equation, when the energy value is greater than or equal to 6.09×10−4, the horizontal position in the corresponding energy surface E is the horizontal position of the magnetic dipole.

[0030] A total of 5 targets were detected, and their horizontal positioning results ( They are (5.02m, 6.06m), (10.04m, 14.96m), (17.04m, 10.04m), (20.08m, 13.98m) and (25m, 7.92m) respectively.

[0031] S5 selects a single target and uses the dichotomy method to calculate the target depth corresponding to the extreme value of the vertical magnetic gradient energy of the survey line to estimate the target burial depth. Specifically, it includes: Since the basis function Estimated value of the distance z from the target to the measurement plane The closer to the true value, the energy at the target horizontal position The larger the energy It's about the estimated value It is a convex function with a single maximum value, so the target depth can be estimated by using the bisection method to find the extreme value.

[0032] Select the detected target, in the distance range from the target to the measurement plane ( Internal, change The estimated value of makes its energy approach the maximum value, corresponding to Is the true depth value of the target, where To measure the height of a plane.

[0033] Repeat the above steps until the depth estimation of all targets is completed.

[0034] In this example, the horizontal positioning results are substituted into the target depth inversion model, and the target depth estimation results are 0.41m, 0.35m, 0.74m, 0.97m and 0.86m respectively.

[0035] In S6, a linear equation system about the target magnetic moment is constructed using the vertical gradient data of the survey line and the values ​​of the two-dimensional orthogonal basis functions, and the target magnetic moment parameter estimation is obtained through the least squares method. Specifically, it includes: After completing the target horizontal position and depth estimation, S61 selects The target is intercepted on the magnetic anomaly gradient surface with its horizontal position as the center The magnetic anomaly window of the grid node in the window can be expressed as .

[0036] S62 due to magnetic anomaly gradient Basis functions can be used Therefore, the target magnetic moment can be separated and expressed as follows: , in, , , .

[0037] S63 will target position Substituting into the above formula, the target magnetic moment can be estimated .

[0038] S64 repeats the above operation, selecting all targets one by one until the magnetic moment estimation of all targets is completed. In this example, the target water position Substituting into the target magnetic moment inversion model, the target magnetic moment inversion results are (3.13 A∙m^2, -1.31 A∙m^2, -3.12 A∙m^2), (-1.80 A∙m^2, -2.14 A∙m^2, 3.47 A∙m^2), (2.07 A∙m^2, 2.37 A∙m^2, 0.84 A∙m^2), (-1.58 A∙m^2, -1.48 A∙m^2, 1.46 A∙m^2) and (-2.77 A∙m^2, 1.46 A∙m^2, -3.11 A∙m^2).

[0039] Considering the high noise level, these results are acceptable, which verifies the effectiveness of the proposed method and its strong noise suppression capability.

[0040] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A magnetic multi-target positioning and magnetic moment inversion method based on vertical gradient of survey line, characterized in that: The method includes: S1 performs low-pass filtering and interpolation on the magnetic anomaly gradient data perpendicular to the survey line to obtain two-dimensional magnetic anomaly gradient data of the survey area with uniform grid distribution; S2 obtains the unit orthogonal basis functions corresponding to each component of the magnetic anomaly gradient data perpendicular to the survey line according to the magnetic dipole model; S3 uses a two-dimensional sliding window to perform discrete integration on each component of the magnetic anomaly gradient data perpendicular to the survey line and the corresponding unit orthogonal basis function to obtain all basis function coefficients; S4 calculates the energy surface of the vertical magnetic anomaly gradient data of the survey line according to the basis function coefficients, detects the local energy peak of the energy surface, calculates the ratio between adjacent energy peaks, obtains the energy ratio sequence, and obtains the target number and the corresponding horizontal position estimation of the target according to the energy mutation value in the energy ratio sequence; S5 selects a single target and uses the dichotomy method to calculate the target depth corresponding to the extreme value of the vertical magnetic anomaly gradient energy of the survey line to estimate the target burial depth; S6 constructs a set of linear equations about the target magnetic moment using the vertical magnetic anomaly gradient data of the survey line and the unit orthogonal basis function, and obtains the target magnetic moment parameter estimation through the least squares method.

2. The magnetic multi-target positioning and magnetic moment inversion method based on the vertical gradient of the survey line according to claim 1 is characterized in that: S1 includes: S11 aligns the survey line in the survey area with the X direction and uses a magnetic total field gradiometer composed of four total field magnetometers perpendicular to the survey line direction to obtain magnetic anomaly gradient data along the pre-planned survey line in the survey area; S12 defines the distance between the two total field magnetometers in the Z direction as the baseline distance in the Z direction. The difference between the measured values ​​of the two total field magnetometers and the baseline distance in the Z direction is used to express the total magnetic field gradient in the Z direction. Similarly, the total magnetic field gradient in the Y direction is obtained. S13 uses a low-pass filter with a passband cutoff frequency of 2 Hz to denoise the magnetic anomaly gradient data, and then uses the Kriging interpolation method to interpolate to obtain two-dimensional magnetic anomaly gradient field data with uniformly distributed grid sizes.

3. The magnetic multi-target positioning and magnetic moment inversion method based on the vertical gradient of the survey line according to claim 1 is characterized in that: S2 includes: S21 uses the magnetic anomaly gradient model of the magnetic dipole to convert the vertical magnetic anomaly gradient at the measuring point into Represented as a set of basis functions A linear combination of S 22 According to the Schmidt orthogonalization principle, the basis function Perform two-dimensional orthogonalization to obtain the corresponding unit orthogonal basis function ; S23 uses unit orthogonal basis functions Reconstructing magnetic anomaly gradients Using unit orthogonal basis functions and basis function coefficients express.

4. The magnetic multi-target positioning and magnetic moment inversion method based on survey line vertical gradient according to claim 3 is characterized in that: S3 includes: S31 uses unit orthogonal basis functions The unit orthogonal basis function properties, calculate the basis function coefficients ; ， S32 basis function coefficients The continuous integral approximation of is: ， in and is the horizontal grid size, P and Q are the control ranges of numerical integration, and are the midpoints of the two horizontal directions of the numerical integration region, then Represents the basis function coefficients of the center point of the integration region; S33 sets the window size to 3 to 6 times the distance z from the measurement plane to the target by multiplying both sides of the equation by , the basis function coefficients are expressed as: 。 5. The magnetic multi-target positioning and magnetic moment inversion method based on survey line vertical gradient according to claim 4 is characterized in that: S4 specifically includes: Calculate the magnetic anomaly gradient perpendicular to the survey line and magnetic anomaly gradient Energy function ; By sliding the numerical integration window in the entire survey area grid, traversing the entire survey area, the magnetic anomaly gradient is obtained and magnetic anomaly gradient The energy surface of Detect local energy peaks of the energy surface, arrange the local energy peaks in ascending order to obtain an energy increasing sequence, calculate the ratio between adjacent energy peaks, and obtain an energy ratio sequence; In the energy ratio sequence, the energy mutation value divides the energy ratio sequence into two segments, so that the cost function is minimized; The energy mutation value corresponds to the first Value , then the energy of the target magnetic anomaly gradient signal is recorded as , in the energy increasing sequence, when the energy value is greater than or equal to When , it is determined that the energy peak is generated by the target gradient signal, and its horizontal position on the energy surface is the target horizontal position.

6. The magnetic multi-target positioning and magnetic moment inversion method based on survey line vertical gradient according to claim 5 is characterized in that: S5 includes: selecting the detected target, in the distance range from the target to the measurement plane ( Change the measurement plane distance range The estimated value of makes its energy approach the maximum value, corresponding to Is the true depth value of the target, where To measure the plane height; repeat the operation until the depth estimation of all targets is completed.

7. The method for magnetic multi-target positioning and magnetic moment inversion based on vertical gradient of survey line according to claim 6, characterized in that: S6 includes: After completing the target horizontal position and depth estimation, S61 selects Target, on the magnetic anomaly gradient surface The top is cut off at its horizontal position The magnetic anomaly window of the grid node in the window is expressed as ; S62 separates the target magnetic moment and expresses it as: ， in, , , ; is the unit direction vector of the Earth's magnetic field; is the basis function In the The value of each measuring point; S63 will target position Substitute basis functions , the target magnetic moment can be estimated ; S64 repeats the above operation, selecting all targets one by one until the magnetic moment estimation of all targets is completed.

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