Underground Target Imaging Method, Device and Computer Equipment

Abstract

The present application relates to an underground target imaging method, apparatus, and computer device. The method includes: first, constructing an initialization objective function corresponding to the grid sub-regions in each key sub-region within the surface detection area by using the first norm, and optimizing each initialization objective function to obtain the initial underground grid parameter values corresponding to each key sub-region; then, constructing a standard objective function for the grid sub-regions in each key sub-region by using the second norm, inputting the respective initial underground grid parameter values into the corresponding standard objective functions, optimizing each standard objective function to obtain the underground grid parameter values of the grid sub-regions in each key sub-region, and performing three-dimensional imaging on the underground targets corresponding to each key sub-region according to the respective underground grid parameter values. By using this method, specific optimization methods can be used to obtain the underground grid parameter values of the grid sub-regions in each key sub-region within the surface detection area, thereby improving the success rate of three-dimensional underground target imaging.

Description

Technical Field

[0001] The present application relates to the technical field of geophysical exploration, and particularly to a method and device for underground target imaging and a computer device. Background Art

[0002] With the development of geological exploration technology, three-dimensional imaging technology has gradually become an important technology in geological exploration, which can help explorers more accurately understand information such as the location and structure of underground targets. Therefore, three-dimensional imaging of underground targets is a key link in the process of geological exploration.

[0003] In the related art, mainly based on the magnetic positioning data of the surface detection area corresponding to the underground detection area, three-dimensional imaging is performed on at least one underground target in the underground detection area.

[0004] However, in the related art, there is a problem of low success rate in three-dimensional imaging of underground targets. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method and device for underground target imaging and a computer device that can improve the success rate of three-dimensional imaging of underground targets.

[0006] In a first aspect, an embodiment of the present application provides a method for underground target imaging, including:

[0007] Obtain at least one key sub-region in the surface detection area, and according to the magnetic positioning data of each key sub-region and a plurality of grid sub-regions in the underground detection area corresponding to each key sub-region, construct an initialization objective function corresponding to the grid sub-region in each key sub-region by using the first norm, and perform optimization processing on each initialization objective function to obtain the initial underground grid parameter values of the grid sub-region in each key sub-region;

[0008] According to the forward matrix between the grid sub-region corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values of the grid sub-region in each key sub-region in the previous state, construct a standard objective function of the grid sub-region in each key sub-region by using the second norm;

[0009] Respectively input the initial underground grid parameter values of the grid sub-region in each key sub-region into the standard objective function of the grid sub-region in the corresponding key sub-region, and perform optimization processing on the standard objective function of the grid sub-region in each key sub-region to obtain the underground grid parameter values of the grid sub-region in each key sub-region;

[0010] According to the underground grid parameter values of the grid sub-region in each key sub-region, perform three-dimensional imaging on the underground target corresponding to each key sub-region to obtain the three-dimensional imaging of each underground target.

[0011] In one embodiment, obtaining at least one key sub-region in the surface detection area includes:

[0012] Performing interpolation processing on the surface data of the surface detection area to obtain the processed surface data of the surface detection area;

[0013] Performing convolution processing on the processed surface data to obtain a convolution result;

[0014] Performing threshold processing based on the convolution result to obtain at least one key sub-region in the surface detection area.

[0015] In one embodiment, performing threshold processing based on the convolution result to obtain at least one key sub-region in the surface detection area includes:

[0016] Comparing the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result;

[0017] Obtaining target scan points in the surface detection area corresponding to multiple elements whose absolute values are greater than the preset threshold in the comparison result;

[0018] Determining at least one key sub-region in the surface detection area according to each target scan point.

[0019] In one embodiment, the above method further includes:

[0020] Performing estimation processing on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region.

[0021] In one embodiment, according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state, constructing a standard objective function for the grid sub-regions in each key sub-region by using the second norm, includes:

[0022] Constructing a data objective function for each key sub-region according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data; and constructing a model objective function for the grid sub-regions in each key sub-region by using the second norm according to the magnetic positioning data of each key sub-region and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state;

[0023] Constructing a standard objective function corresponding to the grid sub-regions in each key sub-region according to the data objective function and the model objective function corresponding to the grid sub-regions in each key sub-region.

[0024] In one embodiment, the data objective function includes a scalar data objective function and a vector data objective function; according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, and the grid sub-regions in each key sub-region, constructing the data objective function for each key sub-region, including:

[0025] Obtain the surface vector data corresponding to each key sub-region;

[0026] According to the forward matrix corresponding to each key sub-region and the surface vector data of each key sub-region, construct the scalar data objective function of the grid sub-regions in each key sub-region;

[0027] According to the surface vector data corresponding to each key sub-region, the angle of the surface vector data, and the number of scan points, construct the vector data objective function of the grid sub-regions in each key sub-region.

[0028] In one embodiment, according to the magnetic positioning data of each key sub-region and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state, construct the model objective function of the grid sub-regions in each key sub-region by using the second norm, including:

[0029] Obtain the forward function between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data;

[0030] According to the forward function between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state, construct the model objective function of the grid sub-regions in each key sub-region by using the second norm.

[0031] In one embodiment, according to the underground grid parameter values of the grid sub-regions in each key sub-region, perform three-dimensional imaging on the underground targets corresponding to each key sub-region to obtain the three-dimensional imaging of each underground target, including:

[0032] For each key sub-region, obtain at least one target underground grid parameter value whose underground grid parameter value is greater than the preset parameter threshold from the underground grid parameter values of the grid sub-regions in the key sub-region;

[0033] Construct the grid sub-region models corresponding to each target underground grid parameter value;

[0034] According to each grid sub-region model, obtain the three-dimensional imaging of the underground target corresponding to the key sub-region.

[0035] In a second aspect, an embodiment of the present application provides an underground target imaging device, and the device includes:

[0036] The first optimization processing module is used to obtain at least one key sub-region in the surface detection area, and construct an initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm according to the magnetic positioning data of each key sub-region and multiple grid sub-regions in the underground detection area corresponding to each key sub-region, and perform optimization processing on each initialization objective function to obtain the initial underground grid parameter values of the grid sub-regions in each key sub-region;

[0037] The objective function construction module is used to construct a standard objective function for the grid sub-regions in each key sub-region by using the second norm according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state;

[0038] The second optimization processing module is used to input the initial underground grid parameter values of the grid sub-regions in each key sub-region into the standard objective functions of the grid sub-regions in the corresponding key sub-regions respectively, and perform optimization processing on the standard objective functions of the grid sub-regions in each key sub-region to obtain the underground grid parameter values of the grid sub-regions in each key sub-region;

[0039] The three-dimensional imaging module is used to perform three-dimensional imaging on the underground targets corresponding to each key sub-region according to the underground grid parameter values of the grid sub-regions in each key sub-region to obtain the three-dimensional imaging of each underground target.

[0040] In a third aspect, an embodiment of the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0041] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0042] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0043] The underground target imaging method, device and computer equipment provided by the embodiments of the present application include: obtaining at least one key sub-region in the surface detection region, and according to the magnetic positioning data of each key sub-region and a plurality of grid sub-regions in the underground detection region corresponding to each key sub-region, constructing an initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm, and optimizing each initialization objective function to obtain the initial underground grid parameter values of the grid sub-regions in each key sub-region. According to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state, constructing a standard objective function for the grid sub-regions in each key sub-region by using the second norm, inputting the initial underground grid parameter values of the grid sub-regions in each key sub-region into the standard objective function of the grid sub-regions in the corresponding key sub-region respectively, and optimizing the standard objective function of the grid sub-regions in each key sub-region to obtain the underground grid parameter values of the grid sub-regions in each key sub-region. According to the underground grid parameter values of the grid sub-regions in each key sub-region, performing three-dimensional imaging on the underground targets corresponding to each key sub-region to obtain the three-dimensional imaging of each underground target. The above method can use the initial underground grid parameter values of the grid sub-regions in each key sub-region optimized from the initialization objective function corresponding to the grid sub-regions in each key sub-region constructed by using the first norm as the initialization of the standard objective function of the grid sub-regions in each key sub-region constructed by using the second norm, which can improve the success rate of optimizing the standard objective function of the grid sub-regions in each key sub-region, and thus improve the success rate of three-dimensional imaging of underground targets. At the same time, the above method has no restrictions on the hidden depth, magnetization enhancement and volume of underground targets underground, so as to improve the wide applicability of the underground target imaging method. In addition, the above method can first obtain the key sub-regions in the surface detection region, and then only process the underground detection region under the key sub-regions to realize three-dimensional imaging of the underground targets corresponding to the surface detection region. Compared with the traditional technology that processes the entire detection region, it can reduce the amount of data processed in the underground target imaging process, thus accelerating the speed of underground target imaging. Moreover, in the above method, the magnetic positioning data of each key sub-region in the surface detection region is used as the prior information for underground target imaging, which can alleviate the problem of insufficient basic information required in the underground target imaging process caused by fewer scanning points of surface data in the traditional technology. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0045] Figure 1 is an application environment diagram of the underground target imaging method in an embodiment;

[0046] Figure 2 is a schematic flowchart of the underground target imaging method in an embodiment;

[0047] Figure 3 is a schematic flowchart of the underground target imaging method in another embodiment;

[0048] Figure 4 is a schematic flowchart of the underground target imaging method in another embodiment;

[0049] Figure 5 is a schematic flowchart of the underground target imaging method in another embodiment;

[0050] Figure 6 is a schematic flowchart of the underground target imaging method in another embodiment;

[0051] Figure 7 is a schematic flowchart of the underground target imaging method in another embodiment;

[0052] Figure 8 is a schematic flowchart of the underground target imaging method in another embodiment;

[0053] Figure 9 is a structural block diagram of the underground target imaging device in an embodiment;

[0054] Figure 10 is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0055] In order to make the objectives, technical solutions and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0056] The underground target imaging method provided by the embodiments of this application can be applied to, for example Figure 1In the application environment shown, the underground target imaging system includes a surface data scanner and a computer device. There can be a communication connection between the surface data scanner and the computer device, and this communication method can be connection methods such as Bluetooth, mobile data, Wifi, etc. At the same time, the computer device can be an electronic device such as a personal computer, a smart phone, a computer, a smart TV, a smart watch, etc. In the embodiments of the present application, the above surface data can include Total Magnetic Anomaly (TMA) data and magnetic gradient tensor data. In the following embodiments, the computer device in the underground target imaging system is used as the execution subject to introduce the specific process of the underground target imaging method.

[0057] In an exemplary embodiment, as Figure 2 shown, a method for underground target imaging is provided. Taking the method applied to the Figure 1 computer device in it as an example for illustration, this method can be implemented through the following steps:

[0058] S100. Obtain at least one key sub-region in the surface detection area. According to the magnetic positioning data of each key sub-region and multiple grid sub-regions in the corresponding underground detection area of each key sub-region, use the first norm to construct the initialization objective function corresponding to the grid sub-regions in each key sub-region, and perform optimization processing on each initialization objective function to obtain the initial underground grid parameter values of the grid sub-regions in each key sub-region.

[0059] In practical applications, the computer device can divide the surface detection area according to a preset detection area division rule, and determine at least one key sub-region from the divided surface detection areas; optionally, the detection area division rule can include information such as the position and division conditions of the key sub-region.

[0060] In addition, the computer device can also call a region division tool, send the corner point positions of the surface detection area to the region division tool, and the region division tool outputs at least one key sub-region in the surface detection area after performing key area division on the surface detection area.

[0061] Specifically, the computer device can pre-train an algorithm model first, and then for any key sub-region, input the first norm, the magnetic positioning data of each key sub-region, and multiple grid sub-regions in the corresponding underground detection area of each key sub-region into the algorithm model, and the algorithm model uses the first norm to construct the initialization objective function corresponding to the grid sub-regions in each key sub-region. In the embodiments of the present application, the above first norm can be the L2 norm.

[0062] In addition, the computer device can also adopt the objective function construction method to construct the initialization objective function corresponding to the grid sub-regions in each key sub-region according to the first norm based on the magnetic positioning data of each key sub-region and the multiple grid sub-regions in the underground detection region corresponding to each key sub-region. Optionally, the objective function construction method can be a direct modeling method based on the essence of the problem, a data-driven empirical modeling method, a modeling method based on physical laws or mechanisms, a multi-objective weighted summation method, etc.

[0063] Furthermore, the computer device can adopt an optimization algorithm to optimize each initialization objective function to obtain the initial underground grid parameter values of the grid sub-regions corresponding to each key sub-region. Optionally, the above optimization algorithm can be a gradient descent method, a Newton method, a quasi-Newton method, or a conjugate gradient method, etc.

[0064] In the embodiment of the present application, the process of obtaining at least one key sub-region in the surface detection region can be referred to as the preliminary exploration process in the underground target imaging process, and the subsequent processing process can be referred to as the re-exploration process in the underground target imaging process.

[0065] It should be noted here that for any key sub-region, the initialization objective function corresponding to the grid sub-region in the key sub-region can include the initialization data objective function and the initialization model objective function corresponding to the grid sub-region in the key sub-region.

[0066] In the embodiment of the present application, for any key sub-region, the computer device can obtain the forward matrix between the magnetic positioning data corresponding to the key sub-region and the underground grid parameter values of the grid sub-regions in the key sub-region, and the surface vector data of the key sub-region. Then, according to the forward matrix between the magnetic positioning data corresponding to the key sub-region and the underground grid parameter values of the grid sub-regions in the key sub-region and the surface vector data of the key sub-region, the computer device constructs the initialization data objective function of the grid sub-regions in the key sub-region, which can be expressed by the following formula (1):

[0067] (1)

[0068] Wherein, represents the weight matrix of the grid sub-regions in the key sub-region, represents the forward matrix between the magnetic positioning data corresponding to the key sub-region and the underground grid parameter values of the grid sub-regions in the key sub-region, represents the underground grid parameter values of the grid sub-regions in the key sub-region, represents the surface data or the processed surface data of the key sub-region.

[0069] Meanwhile, the computer device can construct an initialization model objective function corresponding to the grid sub-region in the key sub-region by using the first norm according to the magnetic positioning data of the key sub-region and multiple grid sub-regions in the corresponding underground detection region of the key sub-region. .

[0070] Furthermore, the computer device can perform arithmetic operation processing on the data objective function of the grid sub-region in the key sub-region and the initialization model objective function of the grid sub-region in the key sub-region to obtain an initialization objective function corresponding to the grid sub-region in the key sub-region. ; In the embodiments of the present application, can be equal to , where represents a regularization parameter.

[0071] S200. Construct a standard objective function for the grid sub-regions in each key sub-region by using the second norm according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state.

[0072] Meanwhile, for any key sub-region, the computer device can input the second norm, the forward matrix between each grid sub-region in the key sub-region and the magnetic positioning data, and the previous underground grid parameter values corresponding to the grid sub-regions in the key sub-region in the previous state into the algorithm model, and the algorithm model constructs a standard objective function for the grid sub-regions in the key sub-region by using the second norm.

[0073] Wherein, in the initial state, the previous underground grid parameter values corresponding to the grid sub-regions in the key sub-region in the previous state can be equal to the initial underground grid parameter values of the grid sub-regions in the key sub-region; subsequently, in the process of optimizing the standard objective function of the grid sub-regions in the key sub-region, the previous underground grid parameter values corresponding to the grid sub-regions in the key sub-region in the previous state can be equal to the previous underground grid parameter values corresponding to the grid sub-regions in the key sub-region determined after the previous iteration. In the embodiments of the present application, the above-mentioned second norm can be the L0 norm.

[0074] S300. Input the initial underground grid parameter values of the grid sub-regions in each key sub-region into the standard objective functions of the grid sub-regions in the corresponding key sub-regions respectively, and optimize the standard objective functions of the grid sub-regions in each key sub-region to obtain the underground grid parameter values of the grid sub-regions in each key sub-region.

[0075] Among them, the initial underground grid parameter values of the grid sub-regions in each key sub-region are respectively input into the standard objective functions of the grid sub-regions in the corresponding key sub-regions. The initial underground grid parameter values of the grid sub-regions in each key sub-region are used as the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state. The previous underground grid parameter values in the standard objective functions of the grid sub-regions in each key sub-region are optimized to obtain the underground grid parameter values of the grid sub-regions in each key sub-region after optimization.

[0076] It should be noted here that the standard objective function constructed by the second norm is a non-convex function. In the embodiments of the present application, the initial underground grid parameter values of the grid sub-regions in each key sub-region are used as the initial values of the standard objective function, which can solve the problem of falling into local optimality in the optimization process of the non-convex function, thereby improving the accuracy of the underground grid parameter values of the grid sub-regions in each key sub-region obtained after optimization.

[0077] S400. According to the underground grid parameter values of the grid sub-regions in each key sub-region, three-dimensional imaging is performed on the underground targets corresponding to each key sub-region to obtain three-dimensional images of the underground targets.

[0078] Specifically, the computer device can send the underground grid parameter values of the grid sub-regions in each key sub-region to a third-party device. The third-party device performs three-dimensional imaging on the underground targets in each key sub-region according to the underground grid parameter values of the grid sub-regions in each key sub-region, and feeds back the obtained three-dimensional images of the underground targets to the computer device.

[0079] In addition, the computer device can trigger a three-dimensional imaging instruction, perform three-dimensional imaging on the underground targets corresponding to each key sub-region according to the underground grid parameter values of the grid sub-regions in each key sub-region, and obtain three-dimensional images of the underground targets. In the embodiments of the present application, the depth, volume, and magnetization intensity of the above-mentioned underground targets hidden below the ground surface are not limited. That is, the above-mentioned underground targets can be large underground targets (such as minerals, geological structures, etc.) or small underground targets.

[0080] In the technical solution of the embodiment of the present application, at least one key sub-region in the surface detection region is obtained. According to the magnetic positioning data of each key sub-region and multiple grid sub-regions in the underground detection region corresponding to each key sub-region, the first norm is used to construct the initialization objective function corresponding to the grid sub-regions in each key sub-region, and each initialization objective function is optimized to obtain the initial underground grid parameter values of the grid sub-regions in each key sub-region. According to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state, the second norm is used to construct the standard objective function of the grid sub-regions in each key sub-region. The initial underground grid parameter values of the grid sub-regions in each key sub-region are respectively input into the standard objective function of the grid sub-regions in the corresponding key sub-region, and the standard objective function of the grid sub-regions in each key sub-region is optimized to obtain the underground grid parameter values of the grid sub-regions in each key sub-region. According to the underground grid parameter values of the grid sub-regions in each key sub-region, three-dimensional imaging of the underground targets corresponding to each key sub-region is performed to obtain the three-dimensional imaging of each underground target; the above method can use the initial underground grid parameter values of the grid sub-regions in each key sub-region optimized from the initialization objective function corresponding to the grid sub-regions in each key sub-region constructed by the first norm as the initialization of the standard objective function of the grid sub-regions in each key sub-region constructed by the second norm, which can improve the success rate of optimizing the standard objective function of the grid sub-regions in each key sub-region, thereby improving the success rate of three-dimensional imaging of underground targets; at the same time, the above method has no restrictions on the hidden depth, magnetization enhancement, and volume of underground targets underground, so as to improve the wide applicability of the underground target imaging method; in addition, the above method can first obtain the key sub-regions in the surface detection region, and then only process the underground detection region under the key sub-regions to realize three-dimensional imaging of the underground targets corresponding to the surface detection region. Compared with the traditional technology that processes the entire detection region, it can reduce the amount of data processed during the underground target imaging process, thereby accelerating the speed of underground target imaging; furthermore, in the above method, the magnetic positioning data of each key sub-region in the surface detection region is used as the prior information for underground target imaging, which can alleviate the problem of insufficient basic information required during the underground target imaging process caused by fewer scanning points in the surface data in the traditional technology.

[0081] The process of obtaining at least one key sub-region in the surface detection region described above will be described below. In one embodiment, as Figure 3 shown, the steps in S100 above can be implemented in the following manner:

[0082] S101. Perform interpolation processing on the surface data of the surface detection region to obtain the processed surface data of the surface detection region.

[0083] Among them, the above surface data may include TMA data and magnetic gradient tensor data. Specifically, the computer device may use an interpolation algorithm to perform interpolation processing on the surface data of the surface detection area to obtain the processed surface data of the surface detection area. Optionally, the above interpolation algorithm may be a linear interpolation method, a polynomial interpolation method, a spline interpolation method, or a radial basis function interpolation method.

[0084] In practical applications, the magnetic positioning data detector may be moved along a preset scanning route within the surface detection area, and the magnetic positioning data corresponding to the surface detection area may be scanned and obtained at preset distance intervals. Optionally, the scanning route may be a route composed of multiple broken lines or a curve, and the embodiments of the present application do not limit this. At the same time, the preset distance may be 1 meter, 1.05 meters, 1.1 meters, etc., but in the embodiments of the present application, it is described by taking the preset distance as 1.2 meters as an example.

[0085] S102. Perform convolution processing on the processed surface data to obtain a convolution result.

[0086] Optionally, the processed surface data may be converted into a matrix form and then convolution processing is performed. Further, the computer device may use a preset convolution kernel to perform convolution processing on the processed surface data to obtain a convolution result. Among them, the convolution result is a convolution matrix.

[0087] S103. Perform threshold processing according to the convolution result to obtain at least one key sub-region in the surface detection area.

[0088] In one embodiment, the computer device may pre-train a threshold processing model, and then input the convolution result into the threshold processing model. The threshold processing model performs threshold processing on the convolution result and outputs at least one key sub-region in the surface detection area. Optionally, the above threshold processing model may be implemented by at least one of a convolutional neural network model, a fully connected neural network model, a residual neural network model, a long short-term memory neural network model, etc.

[0089] In the technical solution of the embodiments of the present application, the processed surface data of the surface detection area is obtained by performing interpolation processing on the surface data of the surface detection area, the convolution result is obtained by performing convolution processing on the processed surface data, and at least one key sub-region in the surface detection area is obtained by performing threshold processing according to the convolution result; the above method can determine at least one key sub-region from the surface detection area through the magnetic positioning data of the surface detection area, and the processing process does not require the participation of complex algorithms, thereby being able to reduce the complexity of obtaining at least one key sub-region in the surface detection area and accelerate the speed of obtaining at least one key sub-region in the surface detection area.

[0090] The process of obtaining at least one key sub-region in the surface detection area by performing threshold processing on the convolution result described above will be described below. In one embodiment, as Figure 4 shown, the step of obtaining at least one key sub-region in the surface detection area by performing threshold processing on the convolution result in S130 above can be implemented in the following manner:

[0091] S131. Compare the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result.

[0092] In practical applications, a computer device can compare the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result.

[0093] S132. Obtain the target scan points in the surface detection area corresponding to multiple elements whose absolute values are greater than the preset threshold in the comparison result.

[0094] Specifically, a computer device can obtain multiple elements whose absolute values are greater than the preset threshold from the comparison result, and map these elements to the corresponding points in the surface detection area to obtain the target scan points in the surface detection area.

[0095] Optionally, the above preset threshold can be determined by the user's self-definition, or can be determined according to historical experience values. However, in the embodiments of the present application, taking the preset threshold equal to 4 times the standard deviation of each element in the above convolution result as an example for illustration.

[0096] S133. Determine at least one key sub-region in the surface detection area according to each target scan point.

[0097] Furthermore, at least one region formed by these target scan points can be determined as each key sub-region in the surface detection area. Among them, when the region formed by all target scan points is connected, it is determined as one key sub-region; when the region formed by all target scan points is not connected, it is determined as multiple independent key sub-regions.

[0098] In the technical solution of the embodiments of the present application, the absolute value of each element in the convolution result is compared with a preset threshold to obtain a comparison result, the target scan points in the surface detection area corresponding to multiple elements whose absolute values are greater than the preset threshold in the comparison result are obtained, and at least one key sub-region in the surface detection area is determined according to each target scan point; the above method does not require complex algorithms to participate in the processing process, thereby being able to reduce the complexity of obtaining at least one key sub-region in the surface detection area and accelerating the speed of obtaining at least one key sub-region in the surface detection area.

[0099] In practical applications, before constructing the initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm based on the magnetic positioning data of each key sub-region and the multiple grid sub-regions in the underground detection region corresponding to each key sub-region, it is necessary to obtain the magnetic positioning data of each key sub-region first. The process of obtaining the magnetic positioning data of each key sub-region will be described below. In one embodiment, before performing the step of constructing the initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm based on the magnetic positioning data of each key sub-region and the multiple grid sub-regions in the underground detection region corresponding to each key sub-region in the above S100, the above method may further include: performing an estimation process based on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region.

[0100] In practical applications, a computer device can pre-train an estimation model, and then sequentially input the surface data of each key sub-region into the estimation model. After the estimation model performs an estimation process on the surface data of each key sub-region, it outputs the magnetic positioning data of each key sub-region.

[0101] Optionally, the above estimation model may be composed of at least one combination of a convolutional neural network model, a fully connected neural network model, a long short-term memory neural network model, a residual neural network model, a recurrent neural network model, etc.

[0102] In the embodiment of the present application, for any key sub-region, the computer device can construct an overdetermined equation between the position, background field, and magnetic gradient tensor data of the corresponding underground target according to the surface data of the key sub-region, and then solve the least squares solution of the overdetermined equation to obtain the magnetic positioning data of the key sub-region. Among them, the above magnetic positioning data may include magnetic moment, position, and background field.

[0103] It should be noted here that if the surface data at any position in the surface detection region is less affected by the corresponding underground target, the surface data at this position should be close to the background field and relatively smooth as a whole. Correspondingly, the absolute value of the corresponding element in the above convolution result is smaller; otherwise, the absolute value of the element at the corresponding position in the convolution result is larger.

[0104] The technical solution in the embodiment of the present application can perform an estimation process based on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region. The processing process does not require the participation of complex algorithms, thereby being able to reduce the complexity of obtaining the magnetic positioning data of the key sub-region and accelerating the speed of obtaining the magnetic positioning data of the key sub-region.

[0105] The process of constructing the initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm for the magnetic positioning data of each key sub-region and the multiple grid sub-regions in the corresponding underground detection region of each key sub-region will be described below. In one embodiment, as Figure 5 shown, the step of constructing the initialization objective function corresponding to the grid sub-regions in each key sub-region by using the first norm for the magnetic positioning data of each key sub-region and the multiple grid sub-regions in the corresponding underground detection region of each key sub-region in S100 can be implemented in the following manner:

[0106] S110. Construct the data objective function for each key sub-region according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data.

[0107] Specifically, for any key sub-region, the computer device can construct the data objective function of the key sub-region according to the forward matrix between the grid sub-regions corresponding to the key sub-region and the magnetic positioning data according to the first preset rule. Optionally, the above first preset rule can be formulated according to historical imaging experience.

[0108] In addition, the computer device can perform conversion processing according to the forward matrix between the grid sub-regions corresponding to the key sub-region and the magnetic positioning data to construct the data objective function of the key sub-region.

[0109] In practical applications, for any key sub-region, the computer device can establish the forward matrix between the grid sub-regions corresponding to the key sub-region and the magnetic positioning data according to the grid sub-regions corresponding to the key sub-region and the corresponding magnetic positioning data.

[0110] S120. Construct the model objective function of the grid sub-regions in each key sub-region by using the second norm according to the forward function between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state.

[0111] In practical applications, for any key sub-region, the computer device can perform arithmetic operation processing according to the forward function between the grid sub-regions corresponding to the key sub-region and the corresponding magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions within the key sub-region in the previous state, and construct the model objective function of the grid sub-regions in the key sub-region by using the first norm.

[0112] In the embodiments of the present application, the execution order of the above steps S110 and S120 can be interchanged, and the embodiments of the present application do not make any limitations thereto.

[0113] S130. Construct the standard objective function corresponding to the grid sub-regions in each key sub-region according to the data objective function and the model objective function corresponding to the grid sub-regions in each key sub-region.

[0114] Specifically, for any key sub-region, the computer device can perform operations such as transformation processing and arithmetic operation processing on the data objective function and the model objective function corresponding to the grid sub-regions in the key sub-region to obtain the standard objective function corresponding to the grid sub-regions in the key sub-region.

[0115] In one embodiment, the data objective function includes a scalar data objective function and a vector data objective function; as Figure 6 shown, the step of constructing the data objective function of each key sub-region according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data in the above S110 may include:

[0116] S111. Obtain the surface vector data corresponding to each key sub-region.

[0117] In practical applications, the computer device can measure the surface vector data corresponding to each key sub-region in real time.

[0118] S112. Construct the scalar data objective function of the grid sub-regions in each key sub-region according to the forward matrix corresponding to each key sub-region and the surface vector data of each key sub-region.

[0119] Specifically, for any key sub-region, the computer device can perform arithmetic operation processing according to the forward matrix corresponding to the key sub-region and the surface vector data of the key sub-region to obtain the scalar data objective function of the grid sub-region. Optionally, the above arithmetic operations may include at least one of addition operation, subtraction operation, multiplication operation, division operation, exponential operation, logarithmic operation, etc.

[0120] In the embodiment of the present application, for any key sub-region, the computer device constructs the scalar data objective function of the grid sub-region in the key sub-region according to the forward matrix between the magnetic positioning data corresponding to the key sub-region and the underground grid parameter value of the grid sub-region in the key sub-region and the surface vector data of the key sub-region. The expression form of the scalar data objective function is the same as the expression form of the above formula (1), and will not be elaborated here.

[0121] S113. Construct the vector data objective function of the grid sub-regions in each key sub-region according to the surface vector data corresponding to each key sub-region, the angle of the surface vector data, and the number of scan points.

[0122] Meanwhile, for any key sub-region, the computer device can perform arithmetic operation processing based on the surface vector data corresponding to the key sub-region, the angle of the surface vector data, and the number of scan points within the key sub-region, to obtain the vector data objective function of the grid sub-region in the key sub-region.

[0123] In the embodiments of the present application, the computer device can construct the vector data objective function of the grid sub-region in each key sub-region according to the surface vector data corresponding to the key sub-region, the angle of the surface vector data, and the number of scan points within the key sub-region.

[0124]

[0125] (2)

[0126] Among them, represents the measured surface vector data of the key sub-region, represents the angle of the theoretical surface vector data of the key sub-region obtained from the underground grid parameter value of the grid sub-region in the key sub-region, represents the angle of the measured surface vector data of the key sub-region, represents the error objective function between the angle of the theoretical surface vector data of the key sub-region and the angle of the surface vector data of the key sub-region, represents the number of scan points (or scan points) corresponding to the surface data or processed surface data of the key sub-region.

[0127] In one embodiment, as Figure 7 shown, the step of constructing the model objective function of the grid sub-region in each key sub-region by using the second norm according to the magnetic positioning data of each key sub-region and the previous underground grid parameter value of the grid sub-region in the previous state in S120 may include:

[0128] S121. Obtain the forward function between the grid sub-region corresponding to each key sub-region and the magnetic positioning data.

[0129] Specifically, for any key sub-region, the computer device can perform arithmetic operation processing based on the grid sub-region in the key sub-region and the magnetic positioning data of the key sub-region, to establish the forward function between the grid sub-region corresponding to the key sub-region and the corresponding magnetic positioning data. Optionally, the above arithmetic operations may be addition operation, subtraction operation, multiplication operation, division operation, logarithmic operation, and / or exponential operation, etc.

[0130] In addition, the computer device can pre-train an algorithm model, and then input both the grid sub-regions in the key sub-region and the magnetic positioning data of the key sub-region into the algorithm model, and the algorithm model establishes a forward function between the grid sub-region corresponding to the key sub-region and the magnetic positioning data.

[0131] S122. According to the forward function between the grid sub-region corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state, use the second norm to construct the model objective function of the grid sub-regions in each key sub-region.

[0132] Among them, for any key sub-region, the computer device constructs the model objective function of the grid sub-regions in the key sub-region according to the forward function between the grid sub-region corresponding to the key sub-region and the magnetic positioning data, and the previous underground grid parameter values corresponding to each grid sub-region in the key sub-region in the previous state can be expressed by the following formula (3):

[0133]

[0134] (3)

[0135] Among them, represents the weight of the grid sub-region in the key sub-region, represents the total size of the grid sub-region in the key sub-region , the gradient of the grid sub-region in the key sub-region in the x direction , the gradient of the grid sub-region in the key sub-region in the y direction and the overall weight of the gradient of the grid sub-region in the key sub-region in the z direction , represents the previous underground grid parameter value corresponding to the grid sub-region in the key sub-region in the previous state, represents the forward function between the grid sub-region corresponding to the key sub-region and the corresponding magnetic positioning data, represents the weight of, represents the weight of, represents the weight of, represents the weight of.

[0136] Furthermore, in the embodiments of the present application, for any key sub-region, the computer device constructs the standard objective function corresponding to the grid sub-region in the key sub-region according to the data objective function and the model objective function corresponding to the grid sub-region in the key sub-region It can be expressed by the following formula (4):

[0137] (4)

[0138] In the technical solution of the embodiment of the present application, according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, a data objective function for each key sub-region is constructed; and, according to the magnetic positioning data of each key sub-region and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state, a model objective function for the grid sub-regions in each key sub-region is constructed using the second norm, and according to the data objective function and the model objective function corresponding to the grid sub-regions in each key sub-region, a standard objective function corresponding to the grid sub-regions in each key sub-region is constructed; the above method can construct the standard objective function corresponding to each key sub-region from different dimensions, making the constructed standard objective function more comprehensive and accurate, so as to prepare for obtaining more accurate underground grid parameter values of the grid sub-regions in each key sub-region in the future; at the same time, in the above method, the data objective function in the standard objective function is divided into a scalar data objective function and a vector data objective function, which can realize the decoupling of scalar data and vector data in the underground target imaging process, so that both accurate scalar data can be utilized to ensure the reliability of subsequent underground target imaging, and vector data can be utilized to improve the accuracy of subsequent underground target imaging.

[0139] The following describes the process of three-dimensional imaging of the underground targets corresponding to each key sub-region based on the underground grid parameter values of the grid sub-regions in each key sub-region to obtain the three-dimensional imaging of each underground target. In one embodiment, as Figure 8 shown, the steps in the above S400 can be implemented in the following manner:

[0140] S410. For each key sub-region, obtain at least one target underground grid parameter value whose underground grid parameter value is greater than a preset parameter threshold from the underground grid parameter values of the grid sub-regions in the key sub-region.

[0141] In practical applications, for each key sub-region, the computer device can compare the underground grid parameter values of the grid sub-regions in the key sub-region with the preset parameter threshold, and obtain the underground grid parameter values greater than the preset parameter threshold from the underground grid parameter values of the grid sub-regions in the key sub-region as the target underground grid parameter values according to the comparison result.

[0142] Optionally, the above preset parameter threshold can be determined by the user's self-definition, or can be determined according to historical experience values, and the embodiments of the present application do not make any limitations thereto.

[0143] S420. Construct a grid sub-region model corresponding to each target underground grid parameter value.

[0144] Specifically, the computer device can draw a grid sub-region model corresponding to each target underground grid parameter value, that is, a three-dimensional model corresponding to the grid sub-region corresponding to each target underground grid parameter value, so as to complete the construction of the grid sub-region model corresponding to each target underground grid parameter value.

[0145] S430. Obtain a three-dimensional imaging of the underground target corresponding to the key sub-region according to each grid sub-region model.

[0146] Furthermore, the grid sub-region models corresponding to the key sub-region can be combined to generate a three-dimensional imaging of the underground target in the underground detection region corresponding to the key sub-region.

[0147] In the technical solution of the embodiment of the present application, for each key sub-region, at least one target underground grid parameter value with an underground grid parameter value greater than a preset parameter threshold is obtained from the underground grid parameter values of the grid sub-regions in the key sub-region, and a grid sub-region model corresponding to each target underground grid parameter value is constructed. According to each grid sub-region model, a three-dimensional imaging of the underground target corresponding to the key sub-region is obtained; the above method can implement a three-dimensional imaging of at least one underground target corresponding to the surface detection region according to the underground grid parameter values of the grid sub-regions in each key sub-region in the surface detection region. The processing process is relatively simple and does not require complex algorithms to participate, so as to be able to accelerate the speed of underground target imaging and improve the efficiency of underground target imaging.

[0148] In one embodiment, the embodiment of the present application further provides an underground target imaging method, which is applied to a computer device. The method includes the following processes:

[0149] (1) Perform interpolation processing on the surface data of the surface detection region to obtain the processed surface data of the surface detection region.

[0150] (2) Perform convolution processing on the processed surface data to obtain a convolution result.

[0151] (3) Compare the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result.

[0152] (4) Obtain the target scan points in the surface detection region corresponding to multiple elements with an absolute value greater than the preset threshold in the comparison result.

[0153] (5) Determine at least one key sub-region in the surface detection region according to each target scan point.

[0154] (6) Perform estimation processing on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region.

[0155] (7) Based on the magnetic positioning data of each key sub-region and multiple grid sub-regions in the corresponding underground detection region of each key sub-region, construct the initialization objective function corresponding to the grid sub-region in each key sub-region using the first norm, and optimize each initialization objective function to obtain the initial underground grid parameter values of the grid sub-region in each key sub-region;

[0156] (8) Obtain the surface vector data corresponding to each key sub-region;

[0157] (9) Based on the forward matrix corresponding to each key sub-region and the surface vector data of each key sub-region, construct the scalar data objective function of the grid sub-region in each key sub-region;

[0158] (10) Based on the surface vector data corresponding to each key sub-region, the angle of the surface vector data, and the number of scan points, construct the vector data objective function of the grid sub-region in each key sub-region;

[0159] (11) Obtain the forward function between the grid sub-region corresponding to each key sub-region and the magnetic positioning data;

[0160] (12) Based on the forward function between the grid sub-region corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values of the grid sub-region in each key sub-region in the previous state, construct the model objective function of the grid sub-region in each key sub-region using the second norm;

[0161] (13) Based on the scalar data objective function, vector data objective function, and model objective function corresponding to the grid sub-region in each key sub-region, construct the standard objective function corresponding to the grid sub-region in each key sub-region;

[0162] (14) Input the initial underground grid parameter values of the grid sub-region in each key sub-region into the standard objective function of the grid sub-region in the corresponding key sub-region respectively, and optimize the standard objective function of the grid sub-region in each key sub-region to obtain the underground grid parameter values of the grid sub-region in each key sub-region;

[0163] (15) For each key sub-region, obtain at least one target underground grid parameter value whose underground grid parameter value is greater than the preset parameter threshold from the underground grid parameter values of the grid sub-region in the key sub-region;

[0164] (16) Construct the grid sub-region model corresponding to each target underground grid parameter value;

[0165] (17) Based on each grid sub-region model, obtain the three-dimensional imaging of the underground target corresponding to the key sub-region.

[0166] The execution process of the above steps (1) to (17) can specifically refer to the description of the above embodiments. Their implementation principles and technical effects are similar, and will not be elaborated here.

[0167] It should be understood that although each step in the flowcharts involved in the above-described embodiments is sequentially shown according to the indication of the arrows, these steps are not necessarily executed sequentially according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0168] Based on the same inventive concept, an embodiment of the present application further provides an underground target imaging device for implementing the underground target imaging method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the underground target imaging device provided below can refer to the limitations on the underground target imaging method in the above text, and will not be elaborated here.

[0169] In one embodiment, Figure 9 is a schematic structural diagram of an underground target imaging device in an embodiment of the present application. The underground target imaging device provided by the embodiment of the present application can be applied to an electronic device. As Figure 9 shown, the underground target imaging device provided by the embodiment of the present application includes: a first optimization processing module 11, a target function construction module 12, a second optimization processing module 13, and a three-dimensional imaging module 14, where:

[0170] The first optimization processing module 11 is configured to obtain at least one key sub-region in the surface detection area, and according to the magnetic positioning data of each key sub-region and a plurality of grid sub-regions in the corresponding underground detection area of each key sub-region, construct an initialization target function corresponding to the grid sub-region in each key sub-region by using the first norm, and perform optimization processing on each initialization target function to obtain an initial underground grid parameter value of the grid sub-region in each key sub-region;

[0171] The target function construction module 12 is configured to construct a standard target function of the grid sub-region in each key sub-region by using the second norm according to the forward matrix between the grid sub-region corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter value of the grid sub-region in each key sub-region in the previous state;

[0172] The second optimization processing module 13 is configured to respectively input the initial underground grid parameter values of the grid sub-regions in each key sub-region into the standard objective functions of the grid sub-regions in the corresponding key sub-regions, perform optimization processing on the standard objective functions of the grid sub-regions in each key sub-region, and obtain the underground grid parameter values of the grid sub-regions in each key sub-region;

[0173] The three-dimensional imaging module 14 is configured to perform three-dimensional imaging on the underground targets corresponding to each key sub-region according to the underground grid parameter values of the grid sub-regions in each key sub-region, and obtain the three-dimensional imaging of each underground target.

[0174] The underground target imaging device provided by the embodiment of the present application can be used to execute the technical solutions in the above-mentioned embodiment of the underground target imaging method of the present application. The implementation principle and technical effects are similar, and will not be elaborated here.

[0175] In one embodiment, the first optimization processing module 11 includes: a data interpolation processing unit, a convolution processing unit, and a threshold processing unit, where:

[0176] The data interpolation processing unit is configured to perform interpolation processing according to the surface data of the surface detection area to obtain the processed surface data of the surface detection area;

[0177] The convolution processing unit is configured to perform convolution processing on the processed surface data to obtain a convolution result;

[0178] The threshold processing unit is configured to perform threshold processing according to the convolution result to obtain at least one key sub-region in the surface detection area.

[0179] The underground target imaging device provided by the embodiment of the present application can be used to execute the technical solutions in the above-mentioned embodiment of the underground target imaging method of the present application. The implementation principle and technical effects are similar, and will not be elaborated here.

[0180] In one embodiment, the threshold processing unit includes: a comparison processing subunit, a first acquisition subunit, and a determination subunit, where:

[0181] The comparison processing subunit is configured to compare the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result;

[0182] The first acquisition subunit is configured to acquire the target scan points in the surface detection area corresponding to multiple elements whose absolute values are greater than the preset threshold in the comparison result;

[0183] The determination subunit is configured to determine at least one key sub-region in the surface detection area according to each target scan point.

[0184] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar, and will not be elaborated here.

[0185] In one embodiment, the underground target imaging device further includes: an estimation processing module, where:

[0186] The estimation processing module performs estimation processing based on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region.

[0187] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar, and will not be elaborated here.

[0188] In one embodiment, the first optimization processing module 11 includes: a first construction unit and a first construction unit, where:

[0189] The first construction unit is configured to construct a data objective function for each key sub-region according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data; and construct a model objective function for the grid sub-regions in each key sub-region by using the second norm according to the magnetic positioning data of each key sub-region and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state.

[0190] The first construction unit is configured to construct a standard objective function for the grid sub-regions in each key sub-region according to the data objective function and the model objective function corresponding to the grid sub-regions in each key sub-region.

[0191] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar, and will not be elaborated here.

[0192] In one embodiment, the data objective function includes a scalar data objective function and a vector data objective function; the first construction unit includes: a second acquisition subunit, a first construction subunit, and a second construction subunit, where:

[0193] The second acquisition subunit is configured to acquire the surface vector data corresponding to each key sub-region.

[0194] The first construction subunit is configured to construct a scalar data objective function for the grid sub-regions in each key sub-region according to the forward matrix corresponding to each key sub-region and the surface vector data of each key sub-region.

[0195] A second construction subunit, configured to construct a vector data objective function for grid sub-regions in each key sub-region according to the surface vector data corresponding to each key sub-region, the angle of the surface vector data, and the number of scan points.

[0196] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0197] In one embodiment, the first construction unit includes: a forward function acquisition subunit and a third construction subunit, where:

[0198] The forward function acquisition subunit is configured to acquire the forward function between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data;

[0199] The third construction subunit is configured to construct a model objective function for the grid sub-regions in each key sub-region by using the second norm according to the forward function between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions in each key sub-region in the previous state.

[0200] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0201] In one embodiment, the three-dimensional imaging module 14 includes: an acquisition unit, a model construction unit, and a three-dimensional imaging acquisition unit, where:

[0202] The acquisition unit is configured to, for each key sub-region, acquire at least one target underground grid parameter value whose underground grid parameter value is greater than a preset parameter threshold from the underground grid parameter values of the grid sub-regions in the key sub-region;

[0203] The model construction unit is configured to construct a grid sub-region model corresponding to each target underground grid parameter value;

[0204] The three-dimensional imaging acquisition unit is configured to obtain a three-dimensional image of the underground target corresponding to the key sub-region according to each grid sub-region model.

[0205] The underground target imaging device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the underground target imaging method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.

[0206] Each module in the above underground target imaging device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0207] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for the processor to exchange information with external devices. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC), or other technologies. The computer program, when executed by the processor, implements an underground target imaging method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0208] Those skilled in the art can understand that Figure 10 the structure shown in

[0209] is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0210] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the technical solution of the above-mentioned underground target imaging method of the present application is implemented. The implementation principle and technical effects are similar and will not be elaborated here.

[0211] In one embodiment, a computer program product is further provided, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned underground target imaging method of the present application is implemented. The implementation principle and technical effects are similar and will not be elaborated here.

[0212] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., and are not limited thereto.

[0213] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0214] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. An underground target imaging method, characterized in that, The method includes: Obtaining at least one key sub-region in the surface detection region, and according to the magnetic positioning data of each key sub-region and a plurality of grid sub-regions in the underground detection region corresponding to each key sub-region, constructing an initialization objective function corresponding to the grid sub-region in each key sub-region by using the first norm, and optimizing each initialization objective function to obtain the initial underground grid parameter values of the grid sub-regions in each key sub-region; According to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state, constructing a standard objective function for the grid sub-regions in each key sub-region by using the second norm; Respectively inputting the initial underground grid parameter values of the grid sub-regions in each key sub-region into the standard objective function of the grid sub-regions in the corresponding key sub-region, and optimizing the standard objective function of the grid sub-regions in each key sub-region to obtain the underground grid parameter values of the grid sub-regions in each key sub-region; According to the underground grid parameter values of the grid sub-regions in each key sub-region, performing three-dimensional imaging on the underground targets corresponding to each key sub-region to obtain the three-dimensional imaging of each underground target.

2. The method according to claim 1, wherein The obtaining at least one key sub-region in the surface detection region includes: Performing interpolation processing on the surface data of the surface detection region to obtain the processed surface data of the surface detection region; Performing convolution processing on the processed surface data to obtain a convolution result; Performing threshold processing on the convolution result to obtain at least one key sub-region in the surface detection region.

3. The method according to claim 2, wherein The performing threshold processing on the convolution result to obtain at least one key sub-region in the surface detection region includes: Comparing the absolute value of each element in the convolution result with a preset threshold to obtain a comparison result; Obtaining the target scan points in the surface detection region corresponding to a plurality of elements whose absolute values are greater than the preset threshold in the comparison result; Determining at least one key sub-region in the surface detection region according to each target scan point.

4. The method according to any one of claims 1 to 3, characterized in that The method further includes: Performing estimation processing on the surface data of each key sub-region to obtain the magnetic positioning data of each key sub-region.

5. The method according to any one of claims 1-3, characterized in that, The constructing a standard objective function for the grid sub-regions in each key sub-region by using the second norm according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data, and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state includes: Constructing a data objective function for each key sub-region according to the forward matrix between the grid sub-regions corresponding to each key sub-region and the magnetic positioning data; and constructing a model objective function for the grid sub-regions in each key sub-region by using the second norm according to the magnetic positioning data of each key sub-region and the previous underground grid parameter values of the grid sub-regions in each key sub-region in the previous state. Construct a standard objective function corresponding to each grid sub-region in each of the key sub-regions according to the data objective function and the model objective function corresponding to the grid sub-regions in each of the key sub-regions.

6. The method according to claim 5, wherein The data objective function includes a scalar data objective function and a vector data objective function; constructing the data objective function for each of the key sub-regions according to the forward matrix between the grid sub-regions corresponding to each of the key sub-regions and the magnetic positioning data and the grid sub-regions in each of the key sub-regions includes: Obtain the surface vector data corresponding to each of the key sub-regions; Construct a scalar data objective function for each grid sub-region in each of the key sub-regions according to the forward matrix corresponding to each of the key sub-regions and the surface vector data of each of the key sub-regions; Construct a vector data objective function for each grid sub-region in each of the key sub-regions according to the surface vector data corresponding to each of the key sub-regions, the angle of the surface vector data, and the number of scan points.

7. The method according to claim 5, wherein Constructing the model objective function for each grid sub-region in each of the key sub-regions by using the second norm according to the magnetic positioning data of each of the key sub-regions and the previous underground grid parameter values corresponding to the grid sub-regions in each of the key sub-regions in the previous state includes: Obtain the forward function between the grid sub-regions corresponding to each of the key sub-regions and the magnetic positioning data; Construct a model objective function for each grid sub-region in each of the key sub-regions by using the second norm according to the forward function between the grid sub-regions corresponding to each of the key sub-regions and the magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions in each of the key sub-regions in the previous state.

8. The method according to any one of claims 1-3, characterized in that Performing three-dimensional imaging on the underground targets corresponding to each of the key sub-regions according to the underground grid parameter values of the grid sub-regions in each of the key sub-regions to obtain three-dimensional imaging of each of the underground targets, including: For each key sub-region, obtain at least one target underground grid parameter value with an underground grid parameter value greater than a preset parameter threshold from the underground grid parameter values of the grid sub-regions in the key sub-region; Construct a grid sub-region model corresponding to each of the target underground grid parameter values; Obtain three-dimensional imaging of the underground target corresponding to the key sub-region according to each of the grid sub-region models.

9. An underground target imaging device, characterized in that, The device includes: A first optimization processing module, configured to obtain at least one key sub-region in the surface detection area, construct an initialization objective function corresponding to each grid sub-region in each of the key sub-regions by using the first norm according to the magnetic positioning data of each of the key sub-regions and multiple grid sub-regions in the underground detection area corresponding to each of the key sub-regions, and perform optimization processing on each of the initialization objective functions to obtain the initial underground grid parameter values of each grid sub-region in each of the key sub-regions; An objective function construction module, configured to construct a standard objective function for each grid sub-region in each of the key sub-regions by using the second norm according to the forward matrix between the grid sub-regions corresponding to each of the key sub-regions and the magnetic positioning data and the previous underground grid parameter values corresponding to the grid sub-regions in each of the key sub-regions in the previous state; The second optimization processing module is configured to respectively input the initial underground grid parameter values of the grid sub-regions in each of the key sub-regions into the standard objective functions of the grid sub-regions in the corresponding key sub-regions, perform optimization processing on the standard objective functions of the grid sub-regions in each of the key sub-regions, and obtain the underground grid parameter values of the grid sub-regions in each of the key sub-regions; The three-dimensional imaging module is configured to perform three-dimensional imaging on the underground targets corresponding to each of the key sub-regions according to the underground grid parameter values of the grid sub-regions in each of the key sub-regions, and obtain the three-dimensional imaging of each of the underground targets.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

Images