Gravity-magnetic joint inversion method and device and electronic equipment

Abstract

The invention discloses a gravity-magnetic joint inversion method, a gravity-magnetic joint inversion device and electronic equipment. The method comprises the following steps: for at least one exploration measuring point in a target exploration area, obtaining gravity abnormal data, magnetic force abnormal data and measuring point position information of each exploration measuring point; determining a gravity-magnetic joint inversion target function corresponding to the target exploration area according to the block position information of at least one block of the preset layer section of the target exploration area in the first direction, and the gravity anomaly data, the magnetic anomaly data and the measurement point position information of each exploration measurement point in the target exploration area; solving and processing the gravity-magnetic joint inversion objective function based on a conjugate gradient algorithm, and determining an inversion result; the inversion result includes magnetic distribution information and density distribution information of at least one block associated with the exploration point. According to the method, the gravity anomaly data and the magnetic anomaly data are subjected to joint inversion processing, so that the accuracy of a joint inversion result is improved, and a reliable basis is provided for subsequent geological interpretation of a target exploration area.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a gravity and magnetic joint inversion method, apparatus, and electronic device. Background Art

[0002] Three-dimensional inversion is used to invert the physical property distribution of underground three-dimensional geological bodies through gravity anomaly data and magnetic anomaly data. Among them, three-dimensional inversion of gravity anomaly data is used to determine the density distribution information of underground three-dimensional geological bodies. Three-dimensional inversion of magnetic anomaly data is used to determine the magnetic distribution information of underground three-dimensional geological bodies.

[0003] Currently, in the prior art, density distribution information is mainly obtained through inversion of single gravity anomaly data, and magnetic distribution information is obtained through inversion of single magnetic anomaly data. However, since inversion processing using single gravity anomaly data or magnetic anomaly data often has non-uniqueness, the density distribution information obtained through inversion of gravity anomaly data and the magnetic distribution information obtained through inversion of magnetic anomaly data often have significant differences, which is not conducive to subsequent geological interpretation by combining magnetic distribution information and density distribution information, thus affecting the processing in fields such as subsequent mineral resource exploration, oil and gas resource evaluation, and engineering geological survey. Summary of the Invention

[0004] The present invention provides a gravity and magnetic joint inversion method, apparatus, and electronic device. By performing joint inversion processing on gravity anomaly data and magnetic anomaly data, and introducing a correlation coefficient matrix into the structural constraint function of the joint inversion, the partition difference constraint based on pre-identification of homologous regions is realized, the accuracy of the joint inversion result is improved, and a reliable basis is provided for subsequent geological interpretation of the target exploration area.

[0005] According to one aspect of the present invention, a gravity and magnetic joint inversion method is provided. The method includes:

[0006] For at least one exploration point in the target exploration area, obtain the gravity anomaly data, magnetic anomaly data, and measuring point position information corresponding to each exploration point; wherein, the gravity anomaly data and magnetic anomaly data are determined based on a preset detection device, the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration point;

[0007] According to the block position information of at least one block in a preset layer segment of the target exploration area along the first direction, the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration point in the target exploration area, determine the gravity and magnetic joint inversion objective function corresponding to the target exploration area;

[0008] Solving and processing the objective function of gravity and magnetic joint inversion based on the conjugate gradient algorithm to determine the inversion result;

[0009] Among them, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration detection points.

[0010] According to another aspect of the present invention, a gravity and magnetic joint inversion device is provided, and the device includes:

[0011] A data acquisition module, configured to obtain, for at least one exploration detection point in the target exploration area, the gravity anomaly data, magnetic anomaly data, and measurement point position information corresponding to each exploration detection point; among them, the gravity anomaly data and magnetic anomaly data are determined based on preset detection devices, the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration detection point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration detection point;

[0012] A function determination module, configured to determine the objective function of gravity and magnetic joint inversion corresponding to the target exploration area according to the block position information of at least one block in the preset layer segment along the first direction of the target exploration area, the gravity anomaly data, magnetic anomaly data, and measurement point position information of each exploration detection point in the target exploration area;

[0013] An inversion result determination module, configured to solve and process the objective function of gravity and magnetic joint inversion based on the conjugate gradient algorithm to determine the inversion result;

[0014] Among them, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration detection points.

[0015] According to another aspect of the present invention, an electronic device is provided, and the electronic device includes:

[0016] At least one processor; and

[0017] A memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that at least one processor can execute the gravity and magnetic joint inversion method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the gravity and magnetic joint inversion method of any embodiment of the present invention when executed by a processor.

[0020] According to another aspect of the present invention, there is provided a computer program product including a computer program, characterized in that when the computer program is executed by a processor, it implements the gravity and magnetic joint inversion method according to any embodiment of the present invention.

[0021] The technical solution of the embodiment of the present invention provides data support for constructing the gravity and magnetic joint inversion objective function by obtaining the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration measuring point in the target exploration area. According to the block position information of at least one block in the preset section along the first direction in the target exploration area, the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration measuring point in the target exploration area, the gravity and magnetic joint inversion objective function corresponding to the target exploration area is constructed. The gravity and magnetic joint inversion objective function is solved by the conjugate gradient algorithm to obtain the inversion result, so as to perform exploration and development processing on the preset section or the target exploration area based on the magnetic distribution information and density distribution information of at least one block associated with the exploration measuring point in the inversion result. The present invention improves the accuracy of the inversion result by performing joint inversion processing on the gravity anomaly data and magnetic anomaly data, providing a reliable basis for subsequent geological interpretation of the target exploration area.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 is a flowchart of a gravity and magnetic joint inversion method provided by an embodiment of the present invention;

[0025] Figure 2 is a flowchart of a gravity and magnetic joint inversion method provided by an embodiment of the present invention;

[0026] Figure 3 is a schematic structural diagram of a gravity and magnetic joint inversion device provided by an embodiment of the present invention;

[0027] Figure 4 is a schematic structural diagram of an electronic device for implementing the gravity and magnetic joint inversion method of the embodiment of the present invention. Detailed Embodiments

[0028] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment 1

[0031] Figure 1 is a flowchart of a gravity and magnetic joint inversion method provided by Embodiment 1 of the present invention. This embodiment is applicable to the case of performing gravity and magnetic joint inversion on a target exploration area to determine the magnetic distribution information and density distribution information of at least one block at a preset layer segment along a first direction in the target exploration area. This method can be executed by a gravity and magnetic joint inversion device, which can be implemented in the form of hardware and / or software, and the gravity and magnetic joint inversion device can be configured in an electronic device such as a mobile phone, a computer, or a server. As Figure 1 shown, the method includes:

[0032] S110. For at least one exploration measurement point in the target exploration area, obtain the gravity anomaly data, magnetic anomaly data, and measurement point position information corresponding to each exploration measurement point.

[0033] Among them, the target exploration area can be understood as the area where gravity and magnetic joint inversion is currently required to determine the magnetic distribution information and density distribution information of the underground layer segment. The exploration measurement points can be pre-set exploration points in the target exploration area. Optionally, at least one exploration measurement point can be set in the target exploration area according to a preset interval distance or actual requirements.

[0034] The gravity anomaly data and the magnetic anomaly data are determined based on preset detection devices. Optionally, the preset detection device for obtaining the gravity anomaly data is different from the preset detection device for obtaining the magnetic anomaly data. Optionally, the corresponding gravity anomaly data can be obtained through preset detection devices such as ground gravimeters and mobile platform gravimeters. The corresponding magnetic anomaly data can be obtained through preset detection devices such as ground magnetic survey devices, mobile magnetic survey devices, or airborne magnetic survey devices.

[0035] The gravity anomaly data is used to characterize the difference value between the actual detected gravity data and the theoretical gravity data at the same exploration detection point. The actual detected gravity data can be understood as the gravity value actually detected and determined at the exploration detection point. The theoretical gravity data can be understood as the theoretical gravity value corresponding to the exploration detection point. The theoretical gravity data can be the gravity data corresponding to the exploration detection point determined according to the gravity formula corresponding to the Earth reference ellipsoid model. The magnetic anomaly data is used to characterize the difference value between the actual detected magnetic data and the theoretical magnetic data at the same exploration detection point. The actual detected magnetic data can be the magnetic value actually detected and determined at the exploration detection point. The theoretical magnetic data can be the magnetic data corresponding to the exploration detection point determined according to the preset magnetic formula and the International Geomagnetic Reference Field (IGRF). The measurement point location information can be understood as the longitude and latitude information corresponding to the exploration detection point.

[0036] Specifically, for at least one exploration detection point in the target exploration area, the gravity anomaly data and the magnetic anomaly data of each exploration detection point are determined according to the preset detection device, and the measurement point location information of each exploration detection point is determined, so as to construct a gravity and magnetic joint inversion objective function corresponding to the target exploration area through the gravity anomaly data, the magnetic anomaly data, and the measurement point location information of each exploration detection point.

[0037] In the embodiment of the present invention, the acquisition method of the gravity anomaly data and the magnetic anomaly data of each exploration detection point can be: for at least one exploration detection point in the target exploration area, the corresponding original gravity anomaly data and original magnetic anomaly data of each exploration detection point are acquired; the original gravity anomaly data and original magnetic anomaly data corresponding to all exploration detection points are respectively subjected to grid processing to obtain grid gravity anomaly data and grid magnetic anomaly data, and the grid gravity anomaly data of each exploration detection point is used as the gravity anomaly data of the exploration detection point; the grid magnetic anomaly data is subjected to reduction to the pole processing to obtain reduced-to-the-pole magnetic anomaly data, and the reduced-to-the-pole magnetic anomaly data is used as the magnetic anomaly data of the exploration detection point.

[0038] Among them, the original gravity anomaly data can be the gravity anomaly data collected by a preset detection device. Correspondingly, the original magnetic anomaly data can be the magnetic anomaly data collected by a preset detection device. To ensure the accuracy of subsequent data processing, the original gravity anomaly data and the original magnetic anomaly data can be gridded separately. It should be noted that when gridding the original gravity anomaly data and the original magnetic anomaly data separately, the consistency of the grid range and grid size needs to be ensured. Optionally, the gridding method can be at least one of bilinear interpolation method, Kriging method, and natural neighbor method. The gridded gravity anomaly data is the data obtained by gridding the original gravity anomaly data. Correspondingly, the gridded magnetic anomaly data is the data obtained by gridding the original magnetic anomaly data.

[0039] Since the gridded magnetic anomaly data is affected by the inclination and declination of the geomagnetic field, the anomaly pattern becomes complicated, which is not conducive to subsequent data processing. Based on this, the gridded magnetic anomaly data can be subjected to reduction to the pole. That is, the reduction to the pole is to convert the gridded magnetic anomaly data under the condition of inclined magnetization into the magnetic anomaly data under the condition of vertical magnetization. Correspondingly, the magnetic anomaly data under the condition of vertical magnetization is the reduced-to-pole magnetic anomaly data.

[0040] Specifically, for at least one exploration detection point in the target exploration area, the original gravity anomaly data and the original magnetic anomaly data corresponding to each exploration detection point are obtained through a preset detection device. Since the original gravity anomaly data and the original magnetic anomaly data are usually obtained through different preset detection devices, there will be data errors when directly performing subsequent data processing based on the original gravity anomaly data and the original magnetic anomaly data. Therefore, the original gravity anomaly data of all exploration detection points in the target exploration area can be gridded to obtain gridded gravity anomaly data, and the original magnetic anomaly data of all exploration detection points in the target exploration area can be gridded to obtain gridded magnetic anomaly data. Based on this, it is ensured that the gridded gravity anomaly data and the gridded magnetic anomaly data correspond under the same spatial coordinates, so as to avoid errors in subsequent data processing and improve the accuracy of geological interpretation of the target exploration area. The gridded gravity anomaly data is used as the gravity anomaly data of the exploration detection point. Since the gridded magnetic anomaly data is the magnetic anomaly data determined under the influence of the inclination and declination of the geomagnetic field, in order to eliminate the influence of the inclination and declination of the geomagnetic field, the gridded magnetic anomaly data under the condition of inclined magnetization can be subjected to reduction to the pole to obtain the reduced-to-pole magnetic anomaly data under the condition of vertical magnetization, and the reduced-to-pole magnetic anomaly data is used as the magnetic anomaly data of the exploration detection point.

[0041] Exemplarily, obtain the original gravity anomaly data and original magnetic anomaly data within the target exploration area. When ensuring that the grid range and grid size corresponding to the gridding process of the original gravity anomaly data and the original magnetic anomaly data are the same, perform gridding processing on the original gravity anomaly data to obtain gridded gravity anomaly data d1. Perform gridding processing on the original magnetic anomaly data to obtain gridded magnetic anomaly data d0. Perform reduction to the pole processing on the gridded magnetic anomaly data d0 to obtain reduced-to-the-pole magnetic anomaly data d2. Use the gridded gravity anomaly data d1 as the gravity anomaly data of the exploration detection points, and use the reduced-to-the-pole magnetic anomaly data d2 as the magnetic anomaly data of the exploration detection points.

[0042] S120. According to the block position information of at least one block in a preset layer segment along a first direction in the target exploration area, the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration detection point in the target exploration area, determine the joint gravity and magnetic inversion objective function corresponding to the target exploration area.

[0043] Among them, the first direction can be the direction perpendicular to the earth's center. The preset layer segment can be the layer segment corresponding to the preset depth range under the target exploration area. The preset depth range can be a preset depth value range. A block can be a cuboid space obtained by regular or irregular division of the preset layer segment. The preset layer segment can be correspondingly divided into at least one block. Use the central position information of each block as the block position information. The joint gravity and magnetic inversion objective function can be a function used to determine the density distribution information and magnetic distribution information of the blocks in the preset layer segment.

[0044] Specifically, according to the gravity anomaly data and magnetic anomaly data of each exploration detection point in the target exploration area, determine the gravity and magnetic correlation coefficient of each exploration detection point to quantitatively determine the gravity and magnetic homology of each exploration detection point. According to the gravity and magnetic correlation coefficient, gravity anomaly data, magnetic anomaly data, measuring point position information of each exploration detection point, and the block position information of each block, construct the joint gravity and magnetic inversion objective function corresponding to the target exploration area.

[0045] S130. Based on the conjugate gradient algorithm, solve the joint gravity and magnetic inversion objective function to determine the inversion result.

[0046] Among them, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration detection points. The conjugate gradient algorithm is to construct a set of conjugate directions and update the solution of the joint inversion function along these conjugate directions in each iteration to determine the optimal solution of the joint inversion function. Correspondingly, the optimal solution of the joint inversion function is the inversion result. The magnetic distribution information can be used to characterize the magnetic distribution of all blocks corresponding to the target exploration area. The density distribution information can be used to characterize the density distribution of all blocks corresponding to the target exploration area.

[0047] Specifically, the conjugate gradient algorithm is used to solve the joint gravity and magnetic inversion objective function, and the magnetic distribution information and density distribution information of at least one block associated with the exploration detection points in the target exploration area are obtained, so as to perform section analysis or exploration and development processing on the preset section through the magnetic distribution information and density distribution information of the block.

[0048] The technical solution of this embodiment provides data support for the subsequent construction of the joint gravity and magnetic inversion objective function by obtaining the gravity anomaly data, magnetic anomaly data and measuring point position information of each exploration detection point in the target exploration area. According to the block position information of at least one block in the preset section of the target exploration area along the first direction, the gravity anomaly data, magnetic anomaly data and measuring point position information of each exploration detection point in the target exploration area, the joint gravity and magnetic inversion objective function corresponding to the target exploration area is constructed. The conjugate gradient algorithm is used to solve the joint gravity and magnetic inversion objective function to obtain the inversion result, so as to perform exploration and development processing on the preset section or the target exploration area based on the magnetic distribution information and density distribution information of at least one block associated with the exploration detection point in the inversion result. By performing joint inversion processing on the gravity anomaly data and magnetic anomaly data, the present invention improves the accuracy of the inversion result and provides a reliable basis for subsequent geological interpretation of the target exploration area.

[0049] Embodiment 2

[0050] Figure 2 FIG. is a flowchart of a joint gravity and magnetic inversion method provided by Embodiment 2 of the present invention. This embodiment of the present invention is a preferred embodiment of the above embodiment. The specific implementation manner can refer to the technical solution of this embodiment. Among them, the same or corresponding technical terms as those in the above embodiment will not be described in detail here. As Figure 2 shown, the method includes:

[0051] S210. For at least one exploration detection point in the target exploration area, obtain the gravity anomaly data, magnetic anomaly data and measuring point position information corresponding to each exploration detection point.

[0052] Among them, the gravity anomaly data and magnetic anomaly data are determined based on preset detection equipment. The gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration detection point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration detection point.

[0053] S220. Determine the joint gravity and magnetic inversion objective function based on the first constraint term, the second constraint term, the third constraint term, the fourth constraint term, the fifth constraint term, and the preset weight coefficient corresponding to each constraint term. Among them, the first constraint term is determined based on the block position information of each block, the gravity anomaly data of each exploration measurement point, and the measurement point position information; the second constraint term is determined based on the block position information of each block, the magnetic anomaly data of each exploration measurement point, and the measurement point position information; the third constraint term is determined based on the first constraint term and the preset fitting function; the fourth constraint term is determined based on the second constraint term and the preset fitting function; the fifth constraint term is determined based on the correlation coefficient matrix and the preset structure constraint function, and the correlation coefficient matrix is determined based on the gravity anomaly data and the magnetic anomaly data of each exploration measurement point.

[0054] Among them, the preset weight coefficient can be pre-set, which is the weight coefficient corresponding to each constraint term in the joint gravity and magnetic inversion objective function. Optionally, the preset weight coefficients corresponding to different constraint terms are different. The first constraint term is a fitting term of the gravity anomaly data corresponding to the density distribution information of the block, determined according to the block position information of each block, the gravity anomaly data of each exploration measurement point, and the measurement point position information. The second constraint term is a fitting term of the magnetic anomaly data corresponding to the magnetic distribution information of the block, determined based on the block position information of each block, the magnetic anomaly data of each exploration measurement point, and the measurement point position information. The third constraint term is a density model fitting term corresponding to the density distribution information of the block, determined based on the first constraint term and the preset fitting function. The fourth constraint term is a magnetic model fitting term corresponding to the magnetic distribution information of the block, determined based on the second constraint term and the preset fitting function. The preset fitting function can be pre-set, which is a function used to fit the gravity anomaly data or the magnetic anomaly data.

[0055] The correlation coefficient matrix can be a matrix composed of correlation coefficients determined based on the gravity anomaly data and the magnetic anomaly data of each exploration measurement point. The correlation coefficient, that is, the gravity-magnetic correlation coefficient, is used to characterize the homology of the gravity anomaly data and the magnetic anomaly data. Through the correlation coefficient matrix, the homology of the gravity anomaly data and the magnetic anomaly data of any exploration measurement point can be quantitatively determined. The preset structure constraint function can be a constraint function corresponding to the structural similarity of the joint gravity and magnetic inversion. Correspondingly, the fifth constraint term can be understood as the structural similarity constraint term of the joint gravity and magnetic inversion.

[0056] Specifically, at least one relative position information corresponding to each exploration measurement point is determined according to the block position information of each block and the measurement point position information of each exploration measurement point. A first constraint term is determined according to the gravity anomaly data, the measurement point position information, and the relative position information of each exploration measurement point. Correspondingly, a second constraint term is determined according to the magnetic anomaly data, the measurement point position information, and the relative position information of each exploration measurement point. A third constraint term is determined according to the information in the first constraint term and a preset fitting function. A fourth constraint term is determined according to the information in the second constraint term and a preset structural constraint function. A correlation coefficient corresponding to each exploration measurement point is determined according to the gravity anomaly data and the magnetic anomaly data of each exploration measurement point. A correlation coefficient matrix corresponding to the target exploration area is determined according to the correlation coefficients of at least one exploration measurement point corresponding to the target exploration area. Using the correlation coefficient matrix as a weighted matrix, a fifth constraint term of the gravity and magnetic joint inversion objective function is constructed in combination with the preset structural constraint function, so as to achieve the distribution difference constraint based on homology recognition through the fifth constraint term. Optionally, in order to cooperate with the partition difference constraint, the fifth constraint term can adopt a sine function similarity constraint term. The gravity and magnetic joint inversion objective function is determined according to the first constraint term, the second constraint term, the third constraint term, the fourth constraint term, the fifth constraint term, and the preset weight coefficient corresponding to each constraint term. By constructing the gravity and magnetic joint inversion objective function through the fifth constraint term based on homology recognition, the structural constraint strength of the high-homology area can be enhanced, and at the same time, the structural constraint strength of the low-homology area can be weakened, making the structural constraint of the joint inversion more region-specific, thereby improving the accuracy of the gravity and magnetic joint inversion.

[0057] In the embodiment of the present invention, the determination methods of the first constraint term and the second constraint term may be: according to the measurement point position information of each exploration measurement point and the block position information of each block, determine the relative position information of each exploration measurement point relative to each block; wherein, the number of relative position information corresponding to each exploration measurement point is consistent with the number of blocks; according to the gravity anomaly data, the measurement point position information, and the relative position information of each exploration measurement point, determine at least one first forward operator corresponding to each exploration measurement point; and, according to the magnetic anomaly data, the measurement point position information, and the relative position information of each exploration measurement point, determine at least one second forward operator corresponding to each exploration measurement point; based on the first forward operators corresponding to all exploration measurement points in the target exploration area, determine a first forward operator matrix; and, based on the second forward operators corresponding to all exploration measurement points in the target exploration area, determine a second forward operator matrix; according to the first forward operator matrix and the gravity anomaly data of each exploration measurement point, determine the first constraint term corresponding to the density distribution information of the block; according to the second forward operator matrix and the magnetic anomaly data of each exploration measurement point, determine the second constraint term corresponding to the magnetic distribution information of the block.

[0058] Among them, the relative position information can be used to characterize the relative position information of the measurement point position information of each exploration measurement point with respect to the block position information of each block. The number of relative position information corresponding to each exploration measurement point is consistent with the number of blocks. That is, each exploration measurement point corresponds to at least one relative position information. The first forward operator is used to characterize the non-linear relationship between the density distribution information of the block and the gravity anomaly data of the exploration measurement point. Correspondingly, the second forward operator is used to characterize the non-linear relationship between the magnetic distribution information of the block and the magnetic anomaly data of the exploration measurement point. Correspondingly, the first forward operator matrix can be determined according to at least one first forward operator corresponding to each exploration measurement point, and is the forward operator matrix corresponding to all exploration measurement points. The second forward operator matrix can be determined according to at least one second forward operator corresponding to each exploration measurement point, and is the forward operator matrix corresponding to all exploration measurement points.

[0059] Specifically, according to the measurement point position information of each exploration measurement point and the block position information of each block, the relative position information of each exploration measurement point with respect to each block is determined. For at least one relative position information corresponding to each exploration measurement point, according to the current relative position information, gravity anomaly data and measurement point position information corresponding to each exploration measurement point, one first forward operator corresponding to each exploration measurement point is determined. Based on this, at least one first forward operator corresponding to each exploration measurement point can be determined. According to the first forward operators corresponding to all exploration measurement points in the target exploration area, the first forward operator matrix is determined. According to the first forward operator matrix and the gravity anomaly data of each exploration measurement point, the first constraint term corresponding to the density distribution information of the block is determined.

[0060] Optionally, the first constraint term can be determined by the following function:

[0061]

[0062] Among them, φ d1 represents the first constraint term, A1 represents the first forward operator, ρ represents the density parameter vector of the block to be solved, and the density distribution information of the block can be determined through the density parameter vectors of all blocks. d1 represents the gravity anomaly data.

[0063] For at least one relative position information corresponding to each exploration measurement point, according to the current relative position information, magnetic anomaly data and measurement point position information corresponding to each exploration measurement point, one second forward operator corresponding to each exploration measurement point is determined. Based on this, at least one second forward operator corresponding to each exploration measurement point can be determined. According to the second forward operators corresponding to all exploration measurement points in the target exploration area, the second forward operator matrix is determined. According to the second forward operator matrix and the magnetic anomaly data of each exploration measurement point, the second constraint term corresponding to the magnetic distribution information of the block is determined. Optionally, the second constraint term can be represented by the following function:

[0064]

[0065] Among them, φ d2 represents the second constraint term, A2 represents the second forward operator matrix, κ represents the magnetic parameter vector of the block to be solved, and the magnetic distribution information of the block can be determined through the magnetic parameter vectors of all blocks. d2 represents the magnetic force anomaly data.

[0066] In the embodiments of the present invention, the third constraint term and the fourth constraint term can be determined as follows: according to the first forward operator matrix in the first constraint term and the first transposed matrix corresponding to the first forward operator matrix, determine the first weighted matrix; and, according to the second forward operator matrix in the first constraint term and the second transposed matrix corresponding to the second forward operator matrix, determine the second weighted matrix; according to the first weighted matrix and a preset fitting function, determine the third constraint term corresponding to the density distribution information of the block; according to the second weighted matrix and the preset fitting function, determine the fourth constraint term corresponding to the magnetic distribution information of the block.

[0067] Among them, the first transposed matrix can be understood as the transposed matrix corresponding to the first forward operator matrix. The first weighted matrix is a matrix determined according to the first forward operator matrix and the first transposed matrix. Optionally, the first weighted matrix can be determined by the following function.

[0068] W ρ = diag(diag(A1 T A1) 1 / 2 )

[0069] Among them, W ρ represents the first weighted matrix, A1 represents the first forward operator matrix, and A1 T represents the first transposed matrix.

[0070] The second transposed matrix can be understood as the transposed matrix corresponding to the second forward operator matrix, and the second weighted matrix is a matrix determined according to the second forward operator matrix and the second transposed matrix. Optionally, the second weighted matrix can be determined by the following function.

[0071] W κ = diag(diag(A2 T A2) 1 / 2 )

[0072] Among them, W κ represents the second weighted matrix, A2 represents the second forward operator matrix, and A2 T represents the second transposed matrix.

[0073] The preset fitting function can be a pre-set function for processing the first weighting matrix or the second weighting matrix.

[0074] Correspondingly, the third constraint term can be determined by the following preset fitting function.

[0075]

[0076] Among them, φ ρ represents the third constraint term, W ρ represents the first weighting matrix, ρ represents the density parameter vector of the block to be solved, and the density distribution information of the block can be determined by the density parameter vectors of all blocks.

[0077] The fourth constraint term can be determined by the following preset fitting function.

[0078]

[0079] Among them, φ κ represents the fourth constraint term, W κ represents the second weighting matrix, κ represents the magnetic parameter vector of the block to be solved, and the magnetic distribution information of the block can be determined by the magnetic parameter vectors of all blocks.

[0080] Specifically, the first transposed matrix corresponding to the first forward operator matrix in the first constraint term is determined through the first forward operator matrix. The first weighting matrix is determined based on the first forward operator matrix and the first transposed matrix. The third constraint term corresponding to the density distribution information of the block is determined by processing the first weighting matrix through the preset fitting function. The second transposed matrix corresponding to the second forward operator matrix in the second constraint term is determined through the second forward operator matrix. The second weighting matrix is determined based on the second forward operator matrix and the second transposed matrix. The fourth constraint term corresponding to the magnetic distribution information of the block is determined by processing the second weighting matrix through the preset fitting function.

[0081] In an embodiment of the present invention, when determining the fifth constraint item, a correlation coefficient matrix may be first determined according to the gravity anomaly data and magnetic anomaly data of each exploration detection point. The determination method of the correlation coefficient matrix may be as follows: for at least one exploration detection point in the target exploration area, taking any one exploration detection point as the starting exploration detection point, determine the gravity anomaly data and magnetic anomaly data of at least one exploration detection point within a preset window centered on the starting exploration detection point; according to the at least one gravity anomaly data and at least one magnetic anomaly data within the preset window, determine the gravity standard deviation data, magnetic standard deviation data, and gravity-magnetic covariance data; determine the multiplication result between the gravity standard deviation data and the magnetic standard deviation data, and determine the ratio of the gravity-magnetic covariance data to the multiplication result, and determine the ratio as the correlation coefficient corresponding to the starting exploration detection point; based on the measurement point position information of the starting exploration detection point and the measurement point position information of other exploration detection points, determine the next starting exploration detection point, and repeat the process of determining the correlation coefficient of the starting exploration detection point until the correlation coefficients corresponding to all exploration detection points in the target exploration area are obtained; based on the correlation coefficients corresponding to all exploration detection points in the target exploration area, determine the correlation coefficient matrix.

[0082] Among them, the starting exploration detection point may be an exploration detection point determined from the target exploration area. The window size of the preset window may be set in advance. The preset window may correspond to at least one exploration detection point. For example, if the window size of the preset window is 3×3, that is, the preset window may include 9 exploration detection points. Correspondingly, based on the preset window, the gravity anomaly data and magnetic anomaly data of these 9 exploration detection points can be determined.

[0083] The gravity standard deviation data may be determined according to the gravity anomaly data corresponding to all exploration detection points within the preset window. Correspondingly, the magnetic standard deviation data may be determined according to the magnetic anomaly data corresponding to all exploration detection points within the preset window. The gravity-magnetic covariance data may be determined according to the gravity anomaly data and magnetic anomaly data of all exploration detection points within the preset window. The correlation coefficient, that is, the gravity-magnetic correlation coefficient, may be used to represent the ratio of the gravity-magnetic covariance data to the multiplication result between the gravity standard deviation data and the magnetic standard deviation data. The correlation coefficient matrix may be determined according to the correlation coefficients corresponding to all exploration detection points in the target exploration area.

[0084] Specifically, for at least one exploration detection point in the target exploration area, taking any exploration detection point as the starting exploration detection point, all exploration detection points within a preset window centered on the starting exploration detection point are determined. And the gravity anomaly data and magnetic anomaly data corresponding to each exploration detection point within the preset window are determined. According to the gravity anomaly data corresponding to each exploration detection point within the preset window, the gravity standard deviation data is determined. According to the magnetic anomaly data corresponding to each exploration detection point within the preset window, the magnetic standard deviation data is determined. According to the gravity anomaly data and magnetic anomaly data corresponding to each exploration detection point within the preset window, the gravity-magnetic covariance data is determined.

[0085] The gravity standard deviation data and the magnetic standard deviation data are multiplied to determine the multiplication result. According to the multiplication result and the gravity-magnetic covariance data, the ratio of the gravity-magnetic covariance data to the multiplication result is determined, and this ratio is used as the correlation coefficient. Optionally, the correlation coefficient can be determined by the following function.

[0086]

[0087] Among them, (i, j) represents the starting exploration detection point, and R(i, j) represents the correlation coefficient corresponding to the starting exploration detection point. If there are k exploration detection points within the preset window centered on the starting exploration detection point (i, j), w1 represents the gravity anomaly data corresponding to the k exploration detection points, w2 represents the magnetic anomaly data corresponding to the k exploration detection points, cov(w1, w2) represents the gravity-magnetic covariance data, and σ w1 represents the gravity standard deviation data, and σ w2 represents the magnetic standard deviation data.

[0088] According to the measurement point position information of the starting exploration detection point and the measurement point position information of other exploration detection points in the target exploration area, the adjacent exploration detection points corresponding to the current starting exploration detection point are determined, and the adjacent exploration detection points are used as the next starting exploration detection point, and the correlation coefficient determination process of the starting exploration detection point is repeated until the correlation coefficients corresponding to all exploration detection points in the target exploration area are obtained. According to the correlation coefficients corresponding to all exploration detection points in the target exploration area, a correlation coefficient matrix corresponding to the target exploration area is constructed.

[0089] By traversing all exploration detection points in the target exploration area through the sliding window technique to determine the gravity-magnetic correlation coefficient between the gravity anomaly data and the magnetic anomaly data of each exploration detection point, a correlation coefficient matrix corresponding to the target exploration area is obtained, realizing the quantitative evaluation of the homology of the gravity anomaly data and the magnetic anomaly data of each exploration detection point in the target exploration area.

[0090] In the embodiment of the present invention, after determining the correlation coefficient matrix, the fifth constraint term can be determined according to the correlation coefficient matrix and the preset structure constraint function. Among them, the preset structure constraint function can be expressed as:

[0091]

[0092] Among them, φ JN represents the fifth constraint term, R represents the correlation coefficient matrix, κ represents the magnetic parameter vector of the block associated with the exploration detection point, which is used to characterize the mean value of the magnetic parameter vector of the block associated with the exploration detection point, ρ represents the density parameter vector of the block associated with the exploration detection point, which represents the mean value of the density parameter vector used to characterize the block associated with the exploration detection point, and θ represents the angle between s and r. By introducing the correlation coefficient matrix into the preset structure constraint function, the partition difference constraint based on homology pre-identification is realized.

[0093] Among them, the magnetic parameter vector can be used to characterize the magnetic characteristics of the blocks in the preset layer section of the target exploration area. The mean value of the magnetic parameter vector is a vector mean determined according to the magnetic parameter vectors of all blocks in the target exploration area and the number of blocks. The density parameter vector can be used to characterize the density characteristics of the blocks in the preset layer section of the target exploration area. The mean value of the density parameter vector can be a vector mean determined according to the density parameter vectors of all blocks in the target exploration area and the number of blocks.

[0094] Specifically, the fifth constraint term is determined through the correlation coefficient matrix and the preset structure constraint function. By adding the correlation coefficient matrix in the form of a weighted matrix to the preset structure constraint function to determine the fifth constraint term, it is convenient for subsequent joint gravity and magnetic inversion with partition difference constraints based on the homology pre-identification results. Through the first constraint term, the second constraint term, the third constraint term, the fourth constraint term, the fifth constraint term, and the preset weight coefficient, the joint gravity and magnetic inversion objective function is determined, realizing the integration of the partition difference constraint of homology identification into the joint gravity and magnetic inversion, enhancing the structural constraint intensity of the high-homology area and weakening the structural constraint intensity of the low-homology area, making the subsequent joint gravity and magnetic inversion more regionally targeted and improving the accuracy of the joint inversion.

[0095] Optionally, the joint gravity and magnetic inversion objective function can be expressed as:

[0096] φ d = λ1φ d1 + λ2φ d2 + α1φ ρ + α2φ κ + εφ JN

[0097] Among them, φ d1 represents the first constraint term, λ1 represents the preset weight coefficient corresponding to the first constraint term, φd2 represents the second constraint term, λ2 represents the preset weight coefficient corresponding to the second constraint term, φ ρ represents the third constraint term, α1 represents the preset weight coefficient corresponding to the third constraint term, φ κ represents the fourth constraint term, α2 represents the preset weight coefficient corresponding to the fourth constraint term, φ JN represents the fifth constraint term, ε represents the preset weight coefficient corresponding to the fifth constraint term.

[0098] S230. Solve and process the joint gravity and magnetic inversion objective function based on the conjugate gradient algorithm to determine the inversion result.

[0099] Among them, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration detection point.

[0100] In the embodiment of the present invention, the way to determine the inversion result can be: solve and process the joint gravity and magnetic inversion objective function based on the conjugate gradient algorithm to determine the magnetic parameter vector and density parameter vector of the block associated with the exploration detection point; determine the magnetic distribution information of the block according to the magnetic parameter vector of at least one block associated with the exploration detection point; determine the density distribution information of the block according to the density parameter vector of at least one block associated with the exploration detection point; and determine the magnetic distribution information and density distribution information as the inversion result.

[0101] Among them, the density distribution information can be used to characterize the density distribution of the blocks in the preset layer section of the target exploration area. The magnetic distribution information can be used to characterize the magnetic distribution of the blocks in the preset layer section of the target exploration area.

[0102] Specifically, solve and process the joint gravity and magnetic inversion objective function based on the conjugate gradient algorithm to determine the magnetic parameter vector and density parameter vector of the block under the preset layer section of the target exploration area. Determine the magnetic distribution information of the block according to the magnetic parameter vector corresponding to at least one block associated with the exploration detection point. Determine the density distribution information of the block according to the density parameter vector corresponding to at least one block associated with the exploration detection point. Determine the magnetic distribution information and density distribution information as the inversion result.

[0103] The technical solution of this embodiment provides data support for constructing the joint gravity and magnetic inversion objective function by obtaining the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration measuring point in the target exploration area. The first constraint term, the second constraint term, the third constraint term, the fourth constraint term, and the fifth constraint term are constructed according to the gravity anomaly data, magnetic anomaly data, and measuring point position information, so as to determine the joint gravity and magnetic inversion objective function according to the above constraint terms and the preset weight coefficients corresponding to each constraint term. The joint gravity and magnetic inversion objective function is solved by the conjugate gradient algorithm to obtain the inversion result, so as to perform exploration and development processing on the preset section or the target exploration area according to the magnetic distribution information and density distribution information of at least one block associated with the exploration measuring point in the inversion result. By performing joint inversion processing on the gravity anomaly data and magnetic anomaly data, the present invention avoids the problems of under-constraint in the homologous area and over-constraint in the non-homologous area, improves the accuracy of the inversion result, and provides a reliable basis for subsequent geological interpretation of the target exploration area.

[0104] Embodiment III

[0105] Figure 3 It is a schematic structural diagram of a joint gravity and magnetic inversion device provided in Embodiment III of the present invention. As Figure 3 shown, the device includes: a data acquisition module 310, a function determination module 320, and an inversion result determination module 330.

[0106] The data acquisition module 310 is configured to obtain, for at least one exploration measuring point in the target exploration area, the gravity anomaly data, magnetic anomaly data, and measuring point position information corresponding to each exploration measuring point; wherein, the gravity anomaly data and magnetic anomaly data are determined based on a preset detection device, and the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration measuring point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration measuring point; the function determination module 320 is configured to determine the joint gravity and magnetic inversion objective function corresponding to the target exploration area according to the block position information of at least one block in the preset section of the target exploration area along the first direction, the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration measuring point in the target exploration area; the inversion result determination module 330 is configured to perform a solution process on the joint gravity and magnetic inversion objective function based on the conjugate gradient algorithm to determine the inversion result; wherein, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration measuring point.

[0107] The technical solution of this embodiment provides data support for constructing the joint gravity and magnetic inversion objective function in the subsequent process by obtaining the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration point in the target exploration area. According to the block position information of at least one block in the preset section along the first direction in the target exploration area, the gravity anomaly data, magnetic anomaly data, and measuring point position information of each exploration point in the target exploration area, the joint gravity and magnetic inversion objective function corresponding to the target exploration area is constructed. The joint gravity and magnetic inversion objective function is solved by the conjugate gradient algorithm to obtain the inversion result, so as to perform exploration and development processing on the preset section or the target exploration area based on the magnetic distribution information and density distribution information of at least one block associated with the exploration points in the inversion result. By performing joint inversion processing on the gravity anomaly data and magnetic anomaly data, the present invention improves the accuracy of the inversion result and provides a reliable basis for subsequent geological interpretation of the target exploration area.

[0108] Based on the above embodiment, optionally, the joint gravity and magnetic inversion objective function is determined based on the first constraint term, the second constraint term, the third constraint term, the fourth constraint term, the fifth constraint term, and the preset weight coefficient corresponding to each constraint term; wherein, the first constraint term is determined based on the block position information of each block, the gravity anomaly data of each exploration point, and the measuring point position information, the second constraint term is determined based on the block position information of each block, the magnetic anomaly data of each exploration point, and the measuring point position information, the third constraint term is determined based on the first constraint term and the preset fitting function, the fourth constraint term is determined based on the second constraint term and the preset fitting function, the fifth constraint term is determined based on the correlation coefficient matrix and the preset structure constraint function, and the correlation coefficient matrix is determined based on the gravity anomaly data and magnetic anomaly data of each exploration point.

[0109] Optionally, the function determination module includes: a first constraint term and a second constraint term determination unit, configured to determine the relative position information of each exploration measurement point with respect to each block according to the measurement point position information of each exploration measurement point and the block position information of each block; wherein, the number of relative position information corresponding to each exploration measurement point is the same as the number of blocks; determine at least one first forward operator corresponding to each exploration measurement point according to the gravity anomaly data, measurement point position information and relative position information of each exploration measurement point; and determine at least one second forward operator corresponding to each exploration measurement point according to the magnetic anomaly data, measurement point position information and relative position information of each exploration measurement point; determine a first forward operator matrix based on the first forward operators corresponding to all exploration measurement points within the target exploration area; and determine a second forward operator matrix based on the second forward operators corresponding to all exploration measurement points within the target exploration area; determine a first constraint term corresponding to the density distribution information of the block according to the first forward operator matrix and the gravity anomaly data of each exploration measurement point; determine a second constraint term corresponding to the magnetic distribution information of the block according to the second forward operator matrix and the magnetic anomaly data of each exploration measurement point.

[0110] Optionally, the function determination module includes: a third constraint term and a fourth constraint term determination unit, configured to determine a first weighted matrix according to the first forward operator matrix in the first constraint term and the first transposed matrix corresponding to the first forward operator matrix; and determine a second weighted matrix according to the second forward operator matrix in the first constraint term and the second transposed matrix corresponding to the second forward operator matrix; determine a third constraint term corresponding to the density distribution information of the block according to the first weighted matrix and a preset fitting function; determine a fourth constraint term corresponding to the magnetic distribution information of the block according to the second weighted matrix and a preset fitting function.

[0111] Optionally, the function determination module further includes: a correlation coefficient matrix determination unit, configured to, for at least one exploration measurement point within the target exploration area, with any one exploration measurement point as the starting exploration measurement point, determine the gravity anomaly data and magnetic anomaly data of at least one exploration measurement point within a preset window centered on the starting exploration measurement point; determine the gravity standard deviation data, magnetic standard deviation data and gravity-magnetic covariance data according to the at least one gravity anomaly data and at least one magnetic anomaly data within the preset window; determine the multiplication result between the gravity standard deviation data and the magnetic standard deviation data, and determine the ratio of the gravity-magnetic covariance data to the multiplication result, and determine the ratio as the correlation coefficient corresponding to the starting exploration measurement point; determine the next starting exploration measurement point according to the measurement point position information of the starting exploration measurement point and the measurement point position information of other exploration measurement points, and repeat the correlation coefficient determination process of the starting exploration measurement point until the correlation coefficients corresponding to all exploration measurement points within the target exploration area are obtained; determine a correlation coefficient matrix based on the correlation coefficients corresponding to all exploration measurement points within the target exploration area.

[0112] Optionally, the preset structural constraint function is expressed as:

[0113]

[0114] where φ JN represents the fifth constraint term, R represents the correlation coefficient matrix, κ represents the magnetic parameter vector of the block associated with the exploration detection point, which is used to characterize the mean value of the magnetic parameter vector of the block associated with the exploration detection point, ρ represents the density parameter vector of the block associated with the exploration detection point, which represents the mean value of the density parameter vector used to characterize the block associated with the exploration detection point, and θ represents the angle between s and r.

[0115] Optionally, the inversion result determination module is used to solve the joint gravity and magnetic inversion objective function based on the conjugate gradient algorithm, and determine the magnetic parameter vector and density parameter vector of the block associated with the exploration detection point; determine the magnetic distribution information of the block according to the magnetic parameter vector of at least one block associated with the exploration detection point; determine the density distribution information of the block according to the density parameter vector of at least one block associated with the exploration detection point; and determine the magnetic distribution information and density distribution information as the inversion result.

[0116] Optionally, the data acquisition module includes: a gravity anomaly data and magnetic anomaly data determination unit, which is used to obtain the original gravity anomaly data and original magnetic anomaly data corresponding to each exploration detection point for at least one exploration detection point in the target exploration area; perform grid processing on the original gravity anomaly data and original magnetic anomaly data corresponding to all exploration detection points respectively to obtain grid gravity anomaly data and grid magnetic anomaly data, and use the grid gravity anomaly data of each exploration detection point as the gravity anomaly data of the exploration detection point; perform reduction to the pole processing on the grid magnetic anomaly data to obtain reduced-to-the-pole magnetic anomaly data, and use the reduced-to-the-pole magnetic anomaly data as the magnetic anomaly data of the exploration detection point.

[0117] The joint gravity and magnetic inversion device provided by the embodiments of the present invention can execute the joint gravity and magnetic inversion method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0118] Embodiment 4

[0119] Figure 4FIG. 0 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as, for example, personal digital assistants, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as read only memory (ROM) 12, random access memory (RAM) 13, etc., wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0121] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0122] The processor 11 may be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the gravity and magnetic joint inversion method.

[0123] In some embodiments, the gravity and magnetic joint inversion method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the gravity and magnetic joint inversion method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the gravity and magnetic joint inversion method by any other suitable means (e.g., by means of firmware).

[0124] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The computer program for implementing the gravity and magnetic joint inversion method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing devices, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-mentioned functions defined in the method of the embodiment of the present invention are performed.

[0127] Embodiment 5

[0128] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a combined gravity and magnetic inversion method, the method comprising:

[0129] For at least one exploration point in a target exploration area, acquiring gravity anomaly data, magnetic anomaly data, and measurement point position information corresponding to each exploration point; wherein, the gravity anomaly data and the magnetic anomaly data are determined based on a preset detection device, the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration point; determining a combined gravity and magnetic inversion objective function corresponding to the target exploration area according to the block position information of at least one block in a preset layer segment along a first direction in the target exploration area, the gravity anomaly data, the magnetic anomaly data, and the measurement point position information of each exploration point in the target exploration area; solving the combined gravity and magnetic inversion objective function based on a conjugate gradient algorithm to determine an inversion result; wherein, the inversion result includes: magnetic distribution information and density distribution information of at least one block associated with the exploration point.

[0130] In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0132] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0133] A computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0134] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0135] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A gravity and magnetic joint inversion method, characterized in that, Including: For at least one exploration measurement point in the target exploration area, obtaining the gravity anomaly data, magnetic anomaly data, and measurement point position information corresponding to each exploration measurement point; wherein, the gravity anomaly data and the magnetic anomaly data are determined based on a preset detection device, the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration measurement point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration measurement point; According to the block position information of at least one block in a preset layer segment of the target exploration area along the first direction, the gravity anomaly data, magnetic anomaly data, and measurement point position information of each exploration measurement point in the target exploration area, determining the joint gravity and magnetic inversion objective function corresponding to the target exploration area; Based on the conjugate gradient algorithm, solving the joint gravity and magnetic inversion objective function to determine the inversion result; Wherein, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration measurement point.

2. The method according to claim 1, wherein The joint gravity and magnetic inversion objective function is determined based on a first constraint term, a second constraint term, a third constraint term, a fourth constraint term, a fifth constraint term, and a preset weight coefficient corresponding to each constraint term; Wherein, the first constraint term is determined based on the block position information of each block, the gravity anomaly data and measurement point position information of each exploration measurement point, the second constraint term is determined based on the block position information of each block, the magnetic anomaly data and measurement point position information of each exploration measurement point, the third constraint term is determined based on the first constraint term and a preset fitting function, the fourth constraint term is determined based on the second constraint term and the preset fitting function, the fifth constraint term is determined based on a correlation coefficient matrix and a preset structure constraint function, and the correlation coefficient matrix is determined based on the gravity anomaly data and magnetic anomaly data of each exploration measurement point.

3. The method according to claim 2, wherein The first constraint term and the second constraint term are respectively determined by the following methods: According to the measurement point position information of each exploration measurement point and the block position information of each block, determining the relative position information of each exploration measurement point relative to each block; wherein, the number of relative position information corresponding to each exploration measurement point is consistent with the number of blocks; According to the gravity anomaly data, measurement point position information, and the relative position information of each exploration measurement point, determining at least one first forward operator corresponding to each exploration measurement point; and, according to the magnetic anomaly data, measurement point position information, and the relative position information of each exploration measurement point, determining at least one second forward operator corresponding to each exploration measurement point; Based on the first forward operators corresponding to all exploration measurement points in the target exploration area, determining a first forward operator matrix; and, based on the second forward operators corresponding to all exploration measurement points in the target exploration area, determining a second forward operator matrix; According to the first forward operator matrix and the gravity anomaly data of each exploration measurement point, determining the first constraint term corresponding to the density distribution information of the block; Determine a second constraint term corresponding to the magnetic distribution information of the block according to the second forward operator matrix and the magnetic anomaly data of each exploration point.

4. The method according to claim 2, characterized in that, The third constraint term and the fourth constraint term are respectively determined in the following manner: Determine a first weighted matrix according to the first forward operator matrix in the first constraint term and the first transpose matrix corresponding to the first forward operator matrix; And determine a second weighted matrix according to the second forward operator matrix in the first constraint term and the second transpose matrix corresponding to the second forward operator matrix; Determine a third constraint term corresponding to the density distribution information of the block according to the first weighted matrix and a preset fitting function; Determine a fourth constraint term corresponding to the magnetic distribution information of the block according to the second weighted matrix and the preset fitting function.

5. The method according to claim 2, characterized in that, Based on the gravity anomaly data and the magnetic anomaly data of each exploration point, determine a correlation coefficient matrix, including: For at least one exploration point in the target exploration area, taking any one exploration point as the starting exploration point, determine the gravity anomaly data and the magnetic anomaly data of at least one exploration point within a preset window centered on the starting exploration point; Determine the gravity standard deviation data, the magnetic standard deviation data, and the gravity-magnetic covariance data according to the at least one gravity anomaly data and the at least one magnetic anomaly data within the preset window; Determine the multiplication result between the gravity standard deviation data and the magnetic standard deviation data, and determine the ratio of the gravity-magnetic covariance data to the multiplication result, and determine the ratio as the correlation coefficient corresponding to the starting exploration point; According to the measurement point position information of the starting exploration point and the measurement point position information of other exploration points, determine the next starting exploration point, and repeat the process of determining the correlation coefficient of the starting exploration point until the correlation coefficients corresponding to all exploration points in the target exploration area are obtained; Based on the correlation coefficients corresponding to all exploration points in the target exploration area, determine a correlation coefficient matrix.

6. The method according to claim 2, wherein The preset structure constraint function is expressed as: Among them, φ JN represents the fifth constraint term, R represents the correlation coefficient matrix, κ represents the magnetic parameter vector of the block associated with the exploration detection point, which is used to characterize the mean value of the magnetic parameter vector of the block associated with the exploration detection point; ρ represents the density parameter vector of the block associated with the exploration detection point, represents the mean value of the density parameter vector used to characterize the block associated with the exploration detection point, and θ represents the angle between s and r.

7. The method according to claim 1, characterized in that, The process of solving the gravity-magnetic joint inversion objective function based on the conjugate gradient algorithm to determine the inversion result includes: Based on the conjugate gradient algorithm, solve the gravity-magnetic joint inversion objective function to determine the magnetic parameter vector and the density parameter vector of the block associated with the exploration point; Determine the magnetic distribution information of the block according to the magnetic parameter vectors of at least one block associated with the exploration point; Determine the density distribution information of the block according to the density parameter vectors of at least one block associated with the exploration point; Determine the magnetic distribution information and the density distribution information as the inversion result.

8. The method according to claim 1, wherein Obtain the gravity anomaly data and the magnetic anomaly data corresponding to each exploration point, including: For at least one exploration point in the target exploration area, obtain the original gravity anomaly data and the original magnetic anomaly data corresponding to each exploration point; Grid processing is respectively performed on the original gravity anomaly data and the original magnetic anomaly data corresponding to all exploration detection points to obtain grid gravity anomaly data and grid magnetic anomaly data, and the grid gravity anomaly data of each exploration detection point is used as the gravity anomaly data of the exploration detection point; Pole reduction processing is performed on the grid magnetic anomaly data to obtain pole-reduced magnetic anomaly data, and the pole-reduced magnetic anomaly data is used as the magnetic anomaly data of the exploration detection point.

9. A gravity and magnetic joint inversion device, characterized in that, It includes: A data acquisition module, configured to acquire, for at least one exploration detection point in a target exploration area, the gravity anomaly data, magnetic anomaly data, and measurement point position information corresponding to each exploration detection point; wherein, the gravity anomaly data and the magnetic anomaly data are determined based on a preset detection device, the gravity anomaly data is used to represent the difference value between the actual detected gravity data and the theoretical gravity data of the same exploration detection point, and the magnetic anomaly data is used to represent the difference value between the actual detected magnetic data and the theoretical magnetic data of the same exploration detection point; A function determination module, configured to determine the joint gravity and magnetic inversion target function corresponding to the target exploration area according to the block position information of at least one block in a preset layer segment along a first direction of the target exploration area, the gravity anomaly data, magnetic anomaly data, and measurement point position information of each exploration detection point in the target exploration area; An inversion result determination module, configured to solve the joint gravity and magnetic inversion target function based on the conjugate gradient algorithm to determine the inversion result; Wherein, the inversion result includes: the magnetic distribution information and density distribution information of at least one block associated with the exploration detection point.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the joint gravity and magnetic inversion method according to any one of claims 1-8.

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