A three-dimensional positioning method and device based on scalar magnetic anomaly and related equipment

Abstract

The application discloses a three-dimensional positioning method and device based on scalar magnetic anomaly and related equipment, and the method comprises the following steps: acquiring two-dimensional scalar magnetic anomaly data, and the record points of the two-dimensional scalar magnetic anomaly data are uniformly distributed in a detection area; based on the two-dimensional scalar magnetic anomaly data, acquiring energy level distribution data of two-dimensional scalar magnetic anomaly in a two-dimensional space formed by each group of unit orthogonal bases, wherein the unit orthogonal bases are obtained by decomposing two-dimensional scalar magnetic anomaly data generated by a magnetic dipole of a target body at an arbitrary height; based on the energy level distribution data, determining a horizontal positioning result and a vertical positioning result of the target body. The application converts two-dimensional scalar magnetic anomaly distribution into two-dimensional energy distribution, improves the signal-to-noise ratio of data, and does not involve the vector magnetic moment information of the target body in the three-dimensional positioning process of the target body, is not affected by the magnetic moment, and can improve the positioning accuracy in the vertical direction.

Description

Technical Field

[0001] This application relates to the field of magnetic exploration technology, and more specifically, to a three-dimensional positioning method, apparatus and related equipment based on scalar magnetic anomalies. Background Technology

[0002] Magnetic anomaly detection (MAD) is one of the most suitable geophysical techniques for locating and mapping the distribution of ferromagnetic metallic objects. It has wide applications in fields related to national economic security and cultural development, such as resource exploration, unexploded ordnance detection, underwater vehicle detection, and archaeology. Due to the advantages of scalar magnetic sensors, such as low noise levels and insensitivity to mechanical noise from rotation and vibration, scalar magnetic anomaly detection is currently the mainstream method. Locating ferromagnetic targets based on two-dimensional planar data of scalar magnetic anomalies is also an important research direction in magnetic anomaly detection. However, because planar data obtained from a single sensor has poor sensitivity to the target's burial depth, and the magnitude of the anomaly is simultaneously affected by the target's magnetic moment parameters, current research on two-dimensional magnetic anomaly localization methods mainly focuses on magnetic anomaly gradient data acquired by dual or multi-sensor arrays. Increasing the number of sensors also significantly increases the cost of magnetometers.

[0003] In the limited number of studies on locating magnetic targets using a single magnetic sensor, direct methods and iterative methods are the two main approaches. Direct methods mainly include Euler deconvolution and its derivatives. These methods can locate targets without being affected by magnetic moment parameters. However, with only one sensor, calculating the vertical gradient requires converting the data to the spatial frequency domain and then back to the spatial domain. This not only greatly increases the computational load but also introduces errors during the forward and inverse Fourier transforms. Iterative methods require assuming initial parameters such as the target's magnetic moment magnitude, tilt angle, deflection angle, and position. Then, nonlinear optimization theory is applied to iteratively solve for these parameters to fit the observed data. During the iteration process, the various parameters to be solved influence each other, leading to large errors in the estimation of position parameters, especially in the vertical direction. Furthermore, iterative methods are greatly affected by initial parameters, often resulting in non-convergence or convergence in the wrong direction. Summary of the Invention

[0004] In view of this, this application provides a three-dimensional positioning method, apparatus and related equipment based on scalar magnetic anomalies to achieve three-dimensional positioning of magnetic targets.

[0005] To achieve the above objectives, the first aspect of this application provides a three-dimensional localization method based on scalar magnetic anomalies, comprising:

[0006] Two-dimensional scalar magnetic anomaly data is acquired, wherein the recording points of the two-dimensional scalar magnetic anomaly data are evenly distributed in the detection area;

[0007] Based on the two-dimensional scalar magnetic anomaly data, the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of orthogonal units are obtained. The orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at any height.

[0008] Based on the energy level distribution data, the horizontal and vertical positioning results of the target are determined.

[0009] Preferably, the process of acquiring two-dimensional scalar magnetic anomaly data includes:

[0010] Using a magnetometer, the scalar magnetic field data of the magnetic field sensor in the detection area and the recording points are recorded along a preset scanning path to obtain multiple data items;

[0011] Diurnal variation correction is applied to the scalar total magnetic field data in each data item to obtain the scalar magnetic anomaly data for each data item;

[0012] Based on the scalar magnetic anomaly data and recording points of each data item, scalar magnetic anomaly data at each grid point of the grid is obtained using a preset interpolation method, wherein the grid is uniformly distributed in the detection area.

[0013] The two-dimensional scalar magnetic anomaly data is composed of scalar magnetic anomaly data at each grid point and the coordinates of each grid point.

[0014] Preferably, the energy distribution data includes first energy level distribution data and second energy level distribution data; the process of obtaining the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases based on the two-dimensional scalar magnetic anomaly data includes:

[0015] Based on the two-dimensional scalar magnetic anomaly data, the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate, and the second modulus horizontal distribution data on each set of unit orthogonal bases under the second vertical coordinate are obtained.

[0016] Based on the first modulus level distribution data, obtain the first energy level distribution data;

[0017] Based on the second modulus level distribution data, the second energy level distribution data is obtained.

[0018] Preferably, the unity orthogonal basis includes:

[0019]

[0020] Where (x,y) are the horizontal coordinates of the recorded point, and z1 and z2 are the first vertical coordinate and the second vertical coordinate, respectively.

[0021] Preferably, the process of obtaining the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate, based on the two-dimensional scalar magnetic anomaly data, includes:

[0022] By using a window of a preset size to extract each set of unit orthogonal bases under the first vertical coordinate, multiple orthogonal base window functions are obtained;

[0023] The following equation is used to calculate the first modulus horizontal distribution data α of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases in the first vertical coordinate. 1,n (x0, y0):

[0024]

[0025] Where I and J are the sizes of the window in the x and y directions, respectively, (x0, y0) are the horizontal coordinates of the center point of the window, and ΔT(x i ,y j ) is in (x i ,y j Two-dimensional scalar magnetic anomaly data at the grid points at ().

[0026] Preferably, the process of obtaining the first energy level distribution data based on the first modulus level distribution data includes:

[0027] The following equation is used to calculate the initial horizontal distribution data E1(x,y) of the energy in the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases under the first vertical coordinate:

[0028]

[0029] Where, α n (x,y) represents the modulus level distribution data on the nth group of orthonormal basis;

[0030] The first original horizontal distribution data is normalized to obtain the first energy horizontal distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases under the first vertical coordinate.

[0031] Preferably, the process of determining the horizontal and vertical positioning results of the target based on the energy level distribution data includes:

[0032] Based on the first energy level distribution data, a first target sub-region containing a ferromagnetic target is determined in the detection area, and the value of the first energy level distribution data of the first target sub-region is greater than a preset threshold.

[0033] Obtain the first maximum value of the first original horizontal distribution data in the first target sub-region, and determine the horizontal coordinates of the point corresponding to the first maximum value as the first horizontal coordinates of the target body;

[0034] Based on the second energy level distribution data, a second target sub-region containing a ferromagnetic target is determined in the detection area, and the value of the second energy level distribution data of the second target sub-region is greater than the preset threshold.

[0035] Obtain the second maximum value of the second original horizontal distribution data in the second target sub-region, and determine the horizontal coordinates of the point corresponding to the second maximum value as the second horizontal coordinates of the target body;

[0036] The average of the first and second horizontal coordinates is determined as the horizontal coordinate of the target body;

[0037] Substituting the first maximum and the second maximum into the following equation, we obtain the vertical coordinate z of the target body:

[0038]

[0039] Among them, E 1max E is the first maximum value. 2ma The second maximum value is z1 and z2, which are the first and second vertical coordinates, respectively.

[0040] A second aspect of this application provides a three-dimensional positioning device based on scalar magnetic anomalies, comprising:

[0041] The data acquisition unit is used to acquire two-dimensional scalar magnetic anomaly data, wherein the recording points of the two-dimensional scalar magnetic anomaly data are evenly distributed in the detection area;

[0042] The energy acquisition unit is used to acquire, based on the two-dimensional scalar magnetic anomaly data, the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of orthogonal units, wherein the orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at any height;

[0043] The positioning calculation unit is used to determine the horizontal and vertical positioning results of the target body based on the energy level distribution data.

[0044] A third aspect of this application provides a three-dimensional positioning device based on scalar magnetic anomalies, comprising: a memory and a processor;

[0045] The memory is used to store programs;

[0046] The processor is used to execute the program to implement the various steps of the above-described three-dimensional localization method based on scalar magnetic anomalies.

[0047] A fourth aspect of this application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of the three-dimensional localization method based on scalar magnetic anomalies as described above.

[0048] As described in the above technical solution, this application first acquires two-dimensional scalar magnetic anomaly data, wherein the recording points of the two-dimensional scalar magnetic anomaly data are uniformly distributed in the detection area. Then, based on the two-dimensional scalar magnetic anomaly data, energy level distribution data of the two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by various sets of orthogonal units is acquired, wherein the orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at arbitrary heights. The energy level distribution data has a high signal-to-noise ratio, and regions with relatively strong energy, i.e., regions with higher values ​​of the energy level distribution data, indicate a greater likelihood of the presence of a ferromagnetic target body. Finally, based on the energy level distribution data, the horizontal and vertical positioning results of the target body are determined. This application transforms the two-dimensional scalar magnetic anomaly distribution into a two-dimensional energy distribution, improving the signal-to-noise ratio of the data. Furthermore, the three-dimensional positioning process of the target body does not involve the vector magnetic moment information of the target body and is unaffected by the magnetic moment, thus improving the positioning accuracy in the vertical direction. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the three-dimensional localization method based on scalar magnetic anomalies disclosed in an embodiment of this application;

[0051] Figure 2 A two-dimensional survey line distribution diagram for magnetic anomaly detection disclosed in an embodiment of this application is illustrated;

[0052] Figure 3 The example illustrates a two-dimensional magnetic total field gradient surface obtained after using the bitone and cubic interpolation method provided in the embodiments of this application;

[0053] Figure 4The present application provides an example of two-dimensional orthogonal basis surface plots calculated using the first group of orthogonal basis functions under different vertical coordinates.

[0054] Figure 5 The embodiments of this application illustrate two-dimensional orthogonal basis surface plots calculated using the second group of orthogonal basis functions under different vertical coordinates.

[0055] Figure 6 The embodiments of this application illustrate two-dimensional orthogonal basis surface plots calculated using the third group of orthogonal basis functions under different vertical coordinates.

[0056] Figure 7 The present application provides an example of a two-dimensional orthogonal basis surface plot obtained by calculating the fourth group of orthogonal basis functions under different vertical coordinates according to an embodiment of the present application.

[0057] Figure 8 The present application provides an example of a two-dimensional orthogonal basis surface plot obtained by calculating the fifth group of orthogonal basis functions under different vertical coordinates.

[0058] Figure 9 An example of the first energy distribution curve provided in an embodiment of this application is illustrated;

[0059] Figure 10 The second energy distribution curve provided in the embodiments of this application is illustrated;

[0060] Figure 11 This is a schematic diagram of a three-dimensional positioning device based on scalar magnetic anomalies disclosed in an embodiment of this application;

[0061] Figure 12 This is a schematic diagram of a three-dimensional positioning device based on scalar magnetic anomalies disclosed in an embodiment of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described 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 of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The following describes a three-dimensional localization method based on scalar magnetic anomalies provided in embodiments of this application. Please refer to [link to relevant documentation]. Figure 1 The three-dimensional localization method based on scalar magnetic anomalies provided in this application embodiment may include the following steps:

[0064] Step S101: Obtain two-dimensional scalar magnetic anomaly data.

[0065] Among them, the two-dimensional magnetic field anomaly data is obtained by applying data processing methods such as diurnal variation correction to the scalar magnetic field data of the magnetic field sensor set in the detection area. The recording points of the two-dimensional magnetic field anomaly data are evenly distributed in the detection area.

[0066] Specifically, after determining the detection area, a magnetic field sensor is deployed in the detection area. The magnetometer mounting platform (flying platform, water platform, or handheld, etc.) moves back and forth along several parallel measurement lines. The magnetometer simultaneously records the scalar magnetic field data and coordinate information of the magnetic field sensor in an interval sampling manner. Finally, after a series of processing such as diurnal variation correction, two-dimensional magnetic field anomaly data is calculated.

[0067] For example, it can be adopted as follows Figure 2 The two-dimensional survey line distribution map shown is used to collect scalar magnetic field data of the magnetic total field sensor. The survey line runs due north-south, with a distance of 0.1m between adjacent measuring points on the line and a distance of 1m between adjacent survey lines. The magnetic total field sensor is 1m above the ground. To verify the validity of this application, a magnetic anomaly is buried underground within the detection area, with spatial coordinates (10, 10, -0.5) and a magnetic moment of 1 Am. 2 The magnetic moment tilt angle is 5° and the deflection angle is -10°; the distance between the magnetic dipole and the observation plane is 1.5m; the background geomagnetic field tilt angle is 30° and the deflection angle is 0°. Scalar magnetic field data are acquired at each measuring point of the magnetic field sensor, and two-dimensional magnetic field anomaly data are calculated based on this scalar magnetic field data. Finally, interpolation processing is used to obtain two-dimensional magnetic field anomaly data uniformly distributed at each recording point in the detection area. For the scalar magnetic field data at each measuring point of the magnetic field sensor, in the practical application of this application, this data is collected using equipment such as a magnetometer; in the algorithm verification stage, the magnetic field anomaly data at each measuring point can be directly calculated using simulation calculation methods, and Gaussian white noise with a preset signal-to-noise ratio (e.g., 10dB) is added to simulate the actual observation data.

[0068] Step S102: Based on the two-dimensional scalar magnetic anomaly data, obtain the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases.

[0069] The orthonormal basis for each group is obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at arbitrary heights. The energy of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each orthonormal basis can be regarded as the square of the modulus value of the two-dimensional scalar magnetic anomaly on each orthonormal basis. By squaring the modulus value of the two-dimensional scalar magnetic anomaly, the difference between the two-dimensional scalar magnetic anomalies at each recording point in the detection area is amplified, thereby improving the signal-to-noise ratio. The modulus of the two-dimensional scalar magnetic anomaly on a set of orthonormal basis is defined as the projection of the two-dimensional scalar magnetic gradient onto that set of orthonormal basis.

[0070] Step S103: Based on the energy level distribution data, determine the horizontal and vertical positioning results of the target body.

[0071] This application first acquires two-dimensional scalar magnetic anomaly data, wherein the recording points of the two-dimensional scalar magnetic anomaly data are uniformly distributed in the detection area. Then, based on the two-dimensional scalar magnetic anomaly data, energy level distribution data of the two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by various sets of orthogonal units is acquired, wherein the orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target object at arbitrary heights. The energy level distribution data has a high signal-to-noise ratio, and regions with relatively strong energy, i.e., regions with higher energy level distribution data values, indicate a greater likelihood of the presence of a ferromagnetic target object. Finally, based on the energy level distribution data, the horizontal and vertical positioning results of the target object are determined. This application transforms the scalar magnetic anomaly distribution into an energy distribution, improving the signal-to-noise ratio of the data. Furthermore, the three-dimensional positioning process of the target object does not involve the vector magnetic moment information of the target object and is unaffected by the magnetic moment, thus improving the positioning accuracy in the vertical direction.

[0072] In some embodiments of this application, the process of obtaining two-dimensional scalar magnetic anomaly data in step S101 above may include:

[0073] S1. Using a magnetometer, the scalar magnetic field data and recording points of the magnetic field sensor in the detection area are recorded along a preset scanning path, resulting in multiple data items.

[0074] Each data item includes scalar total magnetic field data and a recording point. It can be understood that the recording point is the detection location when this data was detected.

[0075] S2, perform diurnal variation correction on the scalar total magnetic field data in each data item to obtain the scalar magnetic anomaly data for each data item.

[0076] The purpose of diurnal variation correction is to eliminate the influence of diurnal variations in the geomagnetic field on the observed data (scalar total magnetic field data). Typically, with current magnetic measurement accuracy, this correction is applied to data within 100 km.2 Within a certain range, the diurnal variation can be considered to be the same. Therefore, the diurnal variation data of the detection area can be obtained through self-observation or by requesting data from nearby geomagnetic observatories. In addition, the diurnal variation observation results can be plotted as diurnal variation curves for reference.

[0077] S3, based on the scalar magnetic anomaly data and record points of each data item, uses a preset interpolation method to obtain the scalar magnetic anomaly data at each grid point of the grid.

[0078] In actual magnetic detection missions, the spacing between measuring points is usually not equal to the spacing between measuring lines, such as... Figure 2 In the two-dimensional survey line distribution map shown, the spacing between measuring points is much smaller than the spacing between survey lines. Therefore, it is necessary to grid the area covered by the survey lines, and the resulting grid should be evenly distributed within the detection area to obtain grid subdivision data of the same size along two horizontal directions (i.e., horizontal and vertical). Commonly used two-dimensional gridding methods include bilinear interpolation, bicubic interpolation, spline interpolation, minimum curvature interpolation, and kriging interpolation. For the vertical distance calculation requirements involved in this application, a quadratic continuous two-dimensional interpolation method, such as bicubic interpolation or kriging interpolation, can be used. Figure 3 The scalar magnetic anomaly surface plot obtained by bicubic interpolation is provided for an embodiment of the present invention.

[0079] Thus, the scalar magnetic anomaly data and coordinates of each grid point (recording point) are obtained. The scalar magnetic anomaly data and coordinates of each grid point constitute two-dimensional scalar magnetic anomaly data.

[0080] To calculate the vertical coordinates of the target body, it is necessary to obtain the energy level distribution data of the two-dimensional scalar magnetic anomaly at different heights in the two-dimensional space spanned by each set of unit orthogonal bases.

[0081] Based on this, in some embodiments of this application, the energy distribution data mentioned in step S102 above may include first energy level distribution data and second energy level distribution data. Specifically, the first energy level distribution data is the energy level distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate; the second energy level distribution data is the energy level distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the second vertical coordinate.

[0082] Step S102 above, which involves obtaining the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases based on the two-dimensional scalar magnetic anomaly data, may include:

[0083] S1. Based on the two-dimensional scalar magnetic anomaly data, obtain the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate, and the second modulus horizontal distribution data on each set of unit orthogonal bases under the second vertical coordinate.

[0084] S2, based on the first modulus horizontal distribution data, obtain the first energy horizontal distribution data.

[0085] S3, based on the second modulus level distribution data, obtain the second energy level distribution data.

[0086] By decomposing the expression for the two-dimensional scalar magnetic gradient generated by the magnetic dipole of the target body at any observation height, a set of unity orthogonal basis function expressions corresponding to the height can be obtained. Based on this, in some embodiments of this application, assuming the magnetic dipole is located at the origin, the unity orthogonal basis mentioned in step S102 above can be expressed as the following two sets of unity orthogonal basis functions:

[0087]

[0088] Where (x,y) are the horizontal coordinates of the recorded point, and z1 and z2 are the first and second vertical coordinates, respectively; and for each unit orthogonal basis under each set of vertical coordinates, the following equation is satisfied:

[0089]

[0090] For example, in one embodiment, z1 = 1m and z2 = 2m are selected, and the above-mentioned sets of orthogonal basis functions are substituted. The final calculated two-dimensional orthogonal basis surface plot is shown below. Figures 4-8 As shown.

[0091] In some embodiments of this application, the process of obtaining the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate based on the two-dimensional scalar magnetic anomaly data may include:

[0092] S11, using a window of a preset size to extract each set of unit orthogonal bases under the first vertical coordinate, to obtain multiple orthogonal base window functions.

[0093] For example, a window of size 5 can be selected and used to truncate each group of orthogonal bases to obtain multiple orthogonal base window functions of finite length.

[0094] S12, using the following equation, calculate the first modulus horizontal distribution data α of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate. 1,n (x0, y0):

[0095]

[0096] Where I and J are the sizes of the window in the x and y directions, respectively, (x0, y0) are the horizontal coordinates of the center point of the window, and ΔT(x i ,y j ) is in (x i ,y j Two-dimensional scalar magnetic anomaly data at the grid points at ().

[0097] The above formula actually calculates the inner product of the obtained orthogonal basis window function along the gridded region point by point on the two-dimensional scalar magnetic anomaly data, thereby obtaining the horizontal distribution of the modulus of each set of unit orthogonal basis directions of the two-dimensional scalar magnetic anomaly under the first vertical coordinate.

[0098] Similarly, for the second modulus horizontal distribution data of each set of unit orthogonal basis in the second vertical coordinate of the two-dimensional scalar magnetic anomaly, the method of the above embodiment can be used, and will not be repeated here.

[0099] In some embodiments of this application, the process of obtaining the first energy level distribution data based on the first modulus level distribution data in S2 may include:

[0100] S21, using the following equation, the first original horizontal distribution data E1(x,y) of the energy in the two-dimensional scalar magnetic anomaly spanned by each set of unit orthogonal bases in the first vertical coordinate is calculated:

[0101]

[0102] Where, α n (x,y) represents the modulus level distribution data on the nth group of orthogonal unit bases.

[0103] S22, normalize the first original horizontal distribution data to obtain the first energy horizontal distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases under the first vertical coordinate.

[0104] This normalization process can eliminate the influence of the magnetic moment magnitude of the target object, which facilitates subsequent data processing.

[0105] Similarly, for the second energy level distribution data, the method described in the above embodiments can be used, and will not be repeated here.

[0106] For example, Figure 9 and Figure 10 The first and second energy level distribution data (i.e., energy distribution curves) are provided respectively. As can be seen from the figure, the energy level distribution data can largely distinguish the energy differences at different grid points.

[0107] In some embodiments of this application, the process of determining the horizontal and vertical positioning results of the target based on energy level distribution data in step S103 may include:

[0108] S1, based on the first energy level distribution data, determine the first target sub-region in the detection area where a ferromagnetic target exists.

[0109] Since the energy level distribution data has been normalized in the preceding steps, the energy values ​​within the detection area are all between 0 and 1. Based on the overall energy distribution, an appropriate threshold is selected, and areas with energy above the threshold are identified as the first target sub-region containing ferromagnetic targets. For example, this threshold can be set to 0.5. Figure 9 As shown, it is easy to obtain one target sub-region.

[0110] S2, obtain the first maximum value of the first original horizontal distribution data in the first target sub-region, and determine the horizontal coordinate of the point corresponding to the first maximum value as the first horizontal coordinate of the target body.

[0111] like Figure 9 In the energy level distribution data shown, by reading the horizontal coordinates of the energy maximum point in the first target sub-region, the horizontal coordinates of the target body can be obtained as (10, 10). In practical applications, at the cost of reduced computational efficiency, the positioning accuracy can be further improved by using a denser grid.

[0112] S3, based on the second energy level distribution data, determine the second target sub-region in the detection area where a ferromagnetic target exists.

[0113] Since the energy level distribution data has been normalized in the preceding steps, the energy values ​​within the detection area are all between 0 and 1. Based on the overall energy distribution, an appropriate threshold is selected, and areas with energy above the threshold are identified as the first target sub-region containing ferromagnetic targets. For example, this threshold can be set to 0.5. Figure 10 As shown, it is easy to obtain one target sub-region.

[0114] S4, obtain the second maximum value of the second original horizontal distribution data in the second target sub-region, and determine the horizontal coordinates of the point corresponding to the second maximum value as the second horizontal coordinates of the target body.

[0115] like Figure 10 In the energy level distribution data shown, by reading the horizontal coordinates of the energy maximum point in the second target sub-region, the horizontal coordinates of the target body can be obtained as (10, 10). In practical applications, at the cost of reduced computational efficiency, the positioning accuracy can be further improved by densifying the grid.

[0116] S5, the average of the first and second horizontal coordinates is determined as the horizontal coordinate of the target body.

[0117] S6. Substituting the first and second maxima into the following equation, we obtain the vertical coordinate z of the target body:

[0118]

[0119] Among them, E 1max E is the first maximum value. 2max The second maximum value is z1, and z2 are the first and second vertical coordinates, respectively.

[0120] exist Figure 9 and Figure 10 In the example shown, the z result is 1.5177m, which has a relative error of 1.18% compared to the actual 1.5m.

[0121] The following describes the three-dimensional positioning device based on scalar magnetic anomaly provided in the embodiments of this application. The three-dimensional positioning device based on scalar magnetic anomaly described below can be referred to in correspondence with the three-dimensional positioning method based on scalar magnetic anomaly described above.

[0122] Please see Figure 11 The three-dimensional positioning device based on scalar magnetic anomaly provided in this application embodiment may include:

[0123] Data acquisition unit 21 is used to acquire two-dimensional scalar magnetic anomaly data, wherein the recording points of the two-dimensional scalar magnetic anomaly data are evenly distributed in the detection area;

[0124] The energy acquisition unit 22 is used to acquire, based on the two-dimensional scalar magnetic anomaly data, the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of orthogonal units, wherein the orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at any height;

[0125] The positioning calculation unit 23 is used to determine the horizontal positioning result and the vertical positioning result of the target body based on the energy horizontal distribution data.

[0126] In some embodiments of this application, the process of the data acquisition unit 21 acquiring two-dimensional scalar magnetic anomaly data may include:

[0127] Using a magnetometer, the scalar magnetic field data of the magnetic field sensor in the detection area and the recording points are recorded along a preset scanning path to obtain multiple data items;

[0128] Diurnal variation correction is applied to the scalar total magnetic field data in each data item to obtain the scalar magnetic anomaly data for each data item;

[0129] Based on the scalar magnetic anomaly data and recording points of each data item, scalar magnetic anomaly data at each grid point of the grid is obtained using a preset interpolation method, wherein the grid is uniformly distributed in the detection area.

[0130] The two-dimensional scalar magnetic anomaly data is composed of scalar magnetic anomaly data at each grid point and the coordinates of each grid point.

[0131] In some embodiments of this application, the energy distribution data includes first energy level distribution data and second energy level distribution data; the process by which the energy acquisition unit 22 acquires the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases based on the two-dimensional scalar magnetic anomaly data may include:

[0132] Based on the two-dimensional scalar magnetic anomaly data, the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate, and the second modulus horizontal distribution data on each set of unit orthogonal bases under the second vertical coordinate are obtained.

[0133] Based on the first modulus level distribution data, obtain the first energy level distribution data;

[0134] Based on the second modulus level distribution data, the second energy level distribution data is obtained.

[0135] The functional expression of the orthonormal basis is consistent with the orthonormal basis function described above.

[0136] In some embodiments of this application, the process by which the energy acquisition unit 22 acquires the first modulus horizontal distribution data of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases under the first vertical coordinate based on the two-dimensional scalar magnetic anomaly data may include:

[0137] By using a window of a preset size to extract each set of unit orthogonal bases under the first vertical coordinate, multiple orthogonal base window functions are obtained;

[0138] The following equation is used to calculate the first modulus horizontal distribution data α of the two-dimensional scalar magnetic anomaly on each set of unit orthogonal bases in the first vertical coordinate. 1,n (x0, y0):

[0139]

[0140] Where I and J are the sizes of the window in the x and y directions, respectively, (x0, y0) are the horizontal coordinates of the center point of the window, and ΔT(x i ,y j ) is in (x i ,yj Two-dimensional scalar magnetic anomaly data at the grid points at ().

[0141] In some embodiments of this application, the process by which the energy acquisition unit 22 acquires the first energy level distribution data based on the first modulus level distribution data may include:

[0142] The following equation is used to calculate the initial horizontal distribution data E1(x,y) of the energy in the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases under the first vertical coordinate:

[0143]

[0144] Where, α n (x,y) represents the modulus level distribution data on the nth group of orthonormal basis;

[0145] The first original horizontal distribution data is normalized to obtain the first energy horizontal distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of unit orthogonal bases under the first vertical coordinate.

[0146] In some embodiments of this application, the process by which the positioning calculation unit 23 determines the horizontal and vertical positioning results of the target object based on the energy level distribution data may include:

[0147] Based on the first energy level distribution data, a first target sub-region containing a ferromagnetic target is determined in the detection area, and the value of the first energy level distribution data of the first target sub-region is greater than a preset threshold.

[0148] Obtain the first maximum value of the first original horizontal distribution data in the first target sub-region, and determine the horizontal coordinates of the point corresponding to the first maximum value as the first horizontal coordinates of the target body;

[0149] Based on the second energy level distribution data, a second target sub-region containing a ferromagnetic target is determined in the detection area, and the value of the second energy level distribution data of the second target sub-region is greater than the preset threshold.

[0150] Obtain the second maximum value of the second original horizontal distribution data in the second target sub-region, and determine the horizontal coordinates of the point corresponding to the second maximum value as the second horizontal coordinates of the target body;

[0151] The average of the first and second horizontal coordinates is determined as the horizontal coordinate of the target body;

[0152] Substituting the first maximum and the second maximum into the following equation, we obtain the vertical coordinate z of the target body:

[0153]

[0154] Among them, E 1ma E is the first maximum value. 2max The second maximum value is z1 and z2, which are the first and second vertical coordinates, respectively.

[0155] The 3D positioning device based on scalar magnetic anomaly provided in this application embodiment can be applied to 3D positioning equipment based on scalar magnetic anomaly, such as computers. Optionally, Figure 12 The hardware structure block diagram of a three-dimensional positioning device based on scalar magnetic anomalies is shown. (Refer to...) Figure 12 The hardware structure of a three-dimensional positioning device based on scalar magnetic anomalies may include: at least one processor 31, at least one communication interface 32, at least one memory 33, and at least one communication bus 34.

[0156] In this embodiment, the number of processor 31, communication interface 32, memory 33 and communication bus 34 is at least one, and processor 31, communication interface 32 and memory 33 communicate with each other through communication bus 34;

[0157] The processor 31 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0158] The memory 33 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0159] The memory 33 stores a program, and the processor 31 can call the program stored in the memory 33. The program is used for:

[0160] Two-dimensional scalar magnetic anomaly data is acquired, wherein the recording points of the two-dimensional scalar magnetic anomaly data are evenly distributed in the detection area;

[0161] Based on the two-dimensional scalar magnetic anomaly data, the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of orthogonal units are obtained. The orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at any height.

[0162] Based on the energy level distribution data, the horizontal and vertical positioning results of the target are determined.

[0163] Optionally, the refined and extended functions of the program can be found in the description above.

[0164] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0165] Two-dimensional scalar magnetic anomaly data is acquired, wherein the recording points of the two-dimensional scalar magnetic anomaly data are evenly distributed in the detection area;

[0166] Based on the two-dimensional scalar magnetic anomaly data, the energy level distribution data of the two-dimensional scalar magnetic anomaly in the two-dimensional space spanned by each set of orthogonal units are obtained. The orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target body at any height.

[0167] Based on the energy level distribution data, the horizontal and vertical positioning results of the target are determined.

[0168] Optionally, the refined and extended functions of the program can be found in the description above.

[0169] In summary:

[0170] This application first acquires two-dimensional scalar magnetic anomaly data, wherein the recording points of the two-dimensional scalar magnetic anomaly data are uniformly distributed in the detection area. Then, based on the two-dimensional scalar magnetic anomaly data, energy level distribution data of the two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by various sets of orthogonal units is acquired, wherein the orthogonal units are obtained by decomposing the two-dimensional scalar magnetic anomaly data generated by the magnetic dipole of the target object at arbitrary heights. The energy level distribution data has a high signal-to-noise ratio, and regions with relatively strong energy, i.e., regions with higher energy level distribution data values, indicate a greater likelihood of the presence of a ferromagnetic target object. Finally, based on the energy level distribution data, the horizontal and vertical positioning results of the target object are determined. This application transforms the two-dimensional scalar magnetic anomaly distribution into a two-dimensional energy distribution, improving the signal-to-noise ratio of the data. Furthermore, the three-dimensional positioning process of the target object does not involve the vector magnetic moment information of the target object and is unaffected by the magnetic moment, thus improving the positioning accuracy in the vertical direction.

[0171] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0172] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0173] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional positioning method based on scalar magnetic anomaly, characterized in that, The method comprises the following steps: acquiring two-dimensional scalar magnetic anomaly data, the recording points of which are uniformly distributed in a detection area; based on the two-dimensional scalar magnetic anomaly data, acquiring energy level distribution data of two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by each group of unit orthogonal bases, the unit orthogonal bases being obtained by decomposing two-dimensional scalar magnetic anomaly data generated by a magnetic dipole of a target body at an arbitrary height, the energy level distribution data comprising first energy level distribution data and second energy level distribution data; the specific steps comprising: based on the two-dimensional scalar magnetic anomaly data, acquiring first modulus level distribution data of each group of unit orthogonal bases under a first vertical coordinate and second modulus level distribution data of each group of unit orthogonal bases under a second vertical coordinate; based on the first modulus level distribution data, acquiring the first energy level distribution data; based on the second modulus level distribution data, acquiring the second energy level distribution data; the unit orthogonal bases comprising: ； wherein, is a horizontal coordinate of the recording point, , is a first vertical coordinate and a second vertical coordinate, respectively; based on the energy level distribution data, determining horizontal positioning results and vertical positioning results of the target body.

2. The method of claim 1, wherein, The process of acquiring two-dimensional scalar magnetic anomaly data comprises: using a magnetometer to record scalar total magnetic field data of a magnetic total field sensor and recording points in the detection area along a preset scanning path, to obtain a plurality of data items; performing daily variation correction processing on the scalar total magnetic field data in each data item, to obtain scalar magnetic anomaly data of each data item; based on the scalar magnetic anomaly data and the recording points of each data item, using a preset interpolation method to acquire scalar magnetic anomaly data at each grid point of a grid, the grid being uniformly distributed in the detection area; the two-dimensional scalar magnetic anomaly data being composed of the scalar magnetic anomaly data at each grid point and the coordinates of each grid point.

3. The method of claim 1, wherein, The process of acquiring first modulus level distribution data of each group of unit orthogonal bases under a first vertical coordinate based on the two-dimensional scalar magnetic anomaly data comprises: using a window of a preset size to intercept each group of unit orthogonal bases under the first vertical coordinate, to obtain a plurality of orthogonal base window functions; The first modulus horizontal distribution data of each group of unit orthogonal bases of the two-dimensional scalar magnetic anomaly in the first vertical coordinate is calculated by using the following equation : ； wherein I, J are the size of the window in x and y direction respectively, is the horizontal coordinate of the center point of the window, is the two-dimensional scalar magnetic anomaly data of the grid point at .

4. The method of claim 1, wherein, The process of acquiring the first energy level distribution data based on the first modulus level distribution data comprises: The first raw horizontal distribution data of energy in the two-dimensional space spanned by the sets of unit orthonormal bases in the first vertical coordinate of the two-dimensional scalar magnetic anomaly is calculated using the following equation : ； wherein is the modulus level distribution data on the nth set of unit orthogonal bases; performing normalization processing on the first original level distribution data, to obtain first energy level distribution data of two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by each group of unit orthogonal bases under the first vertical coordinate.

5. The method of claim 3, wherein, The process of determining horizontal positioning results and vertical positioning results of the target body based on the energy level distribution data comprises: based on the first energy level distribution data, determining a first target sub-area in which a ferromagnetic target body exists in the detection area, the value of the first energy level distribution data of the first target sub-area being greater than a preset threshold value; acquiring a first maximum value of the first original level distribution data in the first target sub-area, and determining a horizontal coordinate of a point corresponding to the first maximum value as a first horizontal coordinate of the target body; determining a second target sub-region in the detection region where a ferromagnetic target exists based on the second energy level distribution data, a value of the second energy level distribution data of the second target sub-region being greater than the preset threshold value; obtaining a second maximum value of the second original level distribution data in the second target sub-region, and determining a horizontal coordinate of a point corresponding to the second maximum value as a second horizontal coordinate of the target; determining a mean value of the first horizontal coordinate and the second horizontal coordinate as the horizontal coordinate of the target; substituting the first maximum value and the second maximum value into the following equation to obtain the vertical coordinate of the target body : ； wherein is the first maximum value, is the second maximum value, , are respectively a first and a second vertical coordinate.

6. A device for locating a target in three dimensions based on scalar magnetic anomalies, characterized in that comprising: a data acquisition unit configured to acquire two-dimensional scalar magnetic anomaly data, the recording points of the two-dimensional scalar magnetic anomaly data being uniformly distributed in a detection region; an energy acquisition unit configured to acquire energy level distribution data of two-dimensional scalar magnetic anomaly in a two-dimensional space spanned by each set of unit orthogonal bases based on the two-dimensional scalar magnetic anomaly data, the unit orthogonal bases being obtained by decomposing two-dimensional scalar magnetic anomaly data generated by a magnetic dipole of a target at an arbitrary height, the energy distribution data including first energy level distribution data and second energy level distribution data; and specific steps include: acquiring first modulus level distribution data of each set of unit orthogonal bases under a first vertical coordinate and second modulus level distribution data of each set of unit orthogonal bases under a second vertical coordinate based on the two-dimensional scalar magnetic anomaly data; acquiring the first energy level distribution data based on the first modulus level distribution data; and acquiring the second energy level distribution data based on the second modulus level distribution data; the unit orthogonal bases including: ； wherein, is a horizontal coordinate of the recording point, , is a first vertical coordinate and a second vertical coordinate, respectively; a positioning calculation unit configured to determine horizontal positioning results and vertical positioning results of a target based on the energy level distribution data.

7. A device for three-dimensional localization of a target based on scalar magnetic anomalies, characterized in that it comprises: comprising: a memory and a processor; the memory is configured to store a program; the processor is configured to execute the program to implement each step of the three-dimensional positioning method based on scalar magnetic anomaly according to any one of claims 1-5.

8. A storage medium having stored thereon a computer program, characterized in that the computer program is executed by the processor to implement each step of the three-dimensional positioning method based on scalar magnetic anomaly according to any one of claims 1-5.

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