Self-constraint magnetic anomaly physical property inversion method and system based on correlation coefficient

Abstract

The invention discloses a self-constraint magnetic anomaly physical property inversion method and system based on correlation coefficients, and relates to the field of geophysical data inversion interpretation, and the method comprises the steps: carrying out the grid processing of actual measurement data, and obtaining actual measurement two-dimensional data D; dividing the underground space into uniform grid units; correlation imaging calculation is carried out on the actually measured data, and a correlation imaging coefficient matrix C is calculated; obtaining a conversion coefficient matrix K according to the prior geological data, and calculating an inversion initial model M0 according to the conversion coefficient matrix K and the related imaging coefficient matrix C; and performing physical property inversion calculation on the inversion initial model M0 by using a conjugate gradient algorithm to obtain inversion magnetic information of the underground space. According to the method, the observation data correlation coefficient matrix is introduced as a self-constraint condition, and the fitting degree and roughness of the conjugate gradient inversion model are self-constrained through the correlation coefficient matrix, so that the multiplicity of solutions of the inversion result can be reduced, the stability of the inversion result can be enhanced, and the resolution of the inversion result can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical data inversion interpretation, in particular to a self-constrained magnetic anomaly physical property inversion method and system based on correlation coefficient. Background Art

[0002] Deep concealed geological structures have important practical significance in the fields of geotectonic research, deep mineral resource exploration, mineralization prospect prediction, and oil and gas exploration. Magnetic exploration data contains certain deep information. Obtaining deep underground magnetic information based on magnetic exploration data and then interpreting concealed geological structures are extremely important for determining the location of deep ore bodies, mineralization prospect analysis, and predicting oil and gas resources.

[0003] Magnetic anomaly inversion is to reversely obtain the magnetic information of underground space based on the observed magnetic anomaly data. Among them, physical property inversion is to discretize the underground space into several grid units and solve the magnetic properties corresponding to each unit. Because it is easy to simulate complex geological bodies, it has become an important direction of magnetic anomaly inversion. Magnetic anomaly inversion is a process of solving high-dimensional data from low-dimensional data. Therefore, the inversion calculation is usually ill-posed, with multiple solutions and instability. In addition, another problem with the inversion calculation is the large calculation dimension and high calculation cost.

[0004] Around the above problems, domestic and foreign scholars have done a lot of research work. Magnetic anomaly inversion can be roughly divided into three categories according to the inversion method. One is the inversion method based on anomaly characteristics, which mainly achieves the inversion purpose through the characteristic lines and characteristic points of the anomaly curve. One is the inversion method based on potential field anomalies, which mainly starts from the potential field theory and achieves the rapid inversion of anomalies by establishing some mathematical models. This type of inversion method has high computational efficiency, but its results are mostly fuzzy and generally cannot be used as the final quantitative interpretation results. The other is the inversion method based on the prior model, which mainly gives the initial model, sets the convergence and exit conditions, and the computer automatically calculates and finally gives the quantitative interpretation results. It is a quantitative interpretation method. This type of inversion method needs to introduce a lot of geological information as constraints to convert the ill-posed problem into a well-posed problem, but there are still many problems in the extraction of constraints and their combination with the inversion process.

[0005] Combining the above three types of inversion methods, if you want to quickly realize the quantitative inversion of magnetic anomaly data, you need to use the correlation imaging method based on potential field anomalies and the conjugate gradient method based on a priori models.

[0006] The correlation imaging method based on potential field anomalies calculates the normalized correlation coefficient between the magnetic anomaly and the dipole scanning function to be imaged underground, and uses the correlation coefficient to equate the distribution of the magnetic properties of the underground geological body, thereby inverting the equivalent magnetic information of the underground space. The correlation imaging method does not require prior information and iterative calculations, and can quickly determine the spatial position and morphology of the geological body, but it cannot determine the magnetic information of the underground space.

[0007] The conjugate gradient method based on the prior model combines the conjugate direction and the gradient direction, constructs a set of conjugate directions using the gradient of the initial point, and obtains the optimal solution of the objective function by searching for the performance point of the objective function along the conjugate direction, thereby obtaining the magnetic information of the underground space. In order to reduce the multiplicity of solutions and improve the stability of solutions, the conjugate gradient method needs to use the prior information as much as possible in the inversion calculation to impose constraints on the model. The conjugate gradient method has a "skin effect", and to overcome the "skin effect", a depth weighting function needs to be introduced to participate in the calculation, but the amount of calculation involved in the prior information and the depth weighting function is huge, which increases the calculation cost of the conjugate gradient method.

[0008] The present invention provides a self-constrained magnetic anomaly physical property inversion method and system based on correlation coefficient, which can reduce the multiple solutions of inversion results, enhance the stability of inversion results, and improve the resolution of inversion results. Summary of the invention

[0009] In view of the above-mentioned problems, the present invention is proposed.

[0010] Therefore, the problem to be solved by the present invention is that the inversion result of the existing correlation imaging method based on potential field anomaly cannot determine the magnetic information of the underground space; the inversion process of the existing conjugate gradient method based on the prior model has a high computational cost.

[0011] To solve the above technical problems, the present invention provides the following technical solutions: a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient, which includes: preprocessing the collected data, gridding the measured data, and obtaining measured two-dimensional data D; dividing the underground space into uniform grid units; performing correlation imaging calculations on the measured data and calculating the correlation imaging coefficient matrix C; obtaining a conversion coefficient matrix K based on prior geological data, and calculating the inversion initial model M0 based on the conversion coefficient matrix K and the correlation imaging coefficient matrix C; performing physical property inversion calculations on the inversion initial model M0 using a conjugate gradient algorithm to obtain inverted magnetic information of the underground space; mapping the inverted magnetic information of the underground space using mapping software, and performing geological interpretation based on the mapping results.

[0012] As a preferred solution of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: the correlation imaging calculation is performed on the measured data to calculate the correlation imaging coefficient matrix C, including, according to the correlation imaging algorithm, the correlation imaging coefficient of the qth grid unit is,

[0013]

[0014] Among them, N s is the total number of observation points in the survey area, B(x i ,y i ,z i ) is in (x i ,y i ,z i ) point measured magnetic anomaly, B q (x i ,y i ,z i ) is the qth grid cell in (x i ,y i ,z i ) point, the formula is expressed as:

[0015] B q (x i ,y i ,z i )=B qx (x i ,y i ,z i )L0+B qy (x i ,y i ,z i )M0+B qz (x i ,y i ,z i )N0

[0016] Among them, B qx (x i ,y i ,z i ), B qy (x i ,y i ,z i ), B qz (x i ,y i ,z i ) are B q (x i ,y i ,z i) The magnetic field components in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions. If A represents the magnetic declination and I represents the magnetic inclination, the three direction cosines are expressed as L0=cosIcosA, M0=cosIsinA, and N0=sinI.

[0017] As a preferred solution of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: the correlation imaging calculation of the measured data, the calculation of the correlation imaging coefficient matrix C also includes, for a rectangular grid unit, if the three sides of the rectangular are a, b, c respectively, the vertex coordinates are (x0, y0, z0), the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the expression of the three components of the magnetic field of the cuboid unit is,

[0018]

[0019]

[0020]

[0021] Where r = [(ε-x i ) 2 +(η-y i ) 2 +(ζ-z i ) 2 ] 1 / 2 Represents the distance between any point in the space and any point in the cuboid; the correlation imaging coefficients of all grid cells constitute the correlation imaging coefficient matrix C.

[0022] As a preferred scheme of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: the conversion coefficient matrix K is obtained according to the prior geological data, including setting the conversion coefficient matrix K according to the prior geological data of the area to be inverted in combination with the corresponding imaging calculation results; the inversion initial model M0 is calculated according to the conversion coefficient matrix K and the correlation imaging coefficient matrix C, including combining the conversion coefficient matrix K with the correlation imaging coefficient matrix C, calculating the inversion initial model M0=K×C, and obtaining the inversion calculation initial model M0.

[0023] As a preferred solution of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: before the physical property inversion calculation of the inversion initial model M0 is performed using the conjugate gradient algorithm to obtain the inverted magnetic information of the underground space, the measured two-dimensional data D after gridding is converted into measured one-dimensional data d; according to the forward modeling formula of the rectangular parallelepiped magnetic anomaly intensity, the forward kernel function G is calculated, and the kernel function G is expressed by the formula:

[0024] G(x i ,y i ,z i )=G x (x i ,y i ,z i )L0+G y (x i ,y i ,z i )M0+G z (x i ,y i ,z i )N0

[0025] Among them, G x (x i ,y i ,z i ), G y (x i ,y i ,z i 0, G z (x i ,y i ,z i ) are G(x i ,y i ,z i ) in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions respectively; for a rectangular grid unit, if the three sides of the rectangular unit are a, b, and c respectively, and the vertex coordinates are (x0, y0, z0), the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the formula for the three components of the kernel function of the cuboid unit is expressed as,

[0026]

[0027] As a preferred solution of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: the use of the conjugate gradient algorithm to perform physical property inversion calculation on the inversion initial model M0 to obtain the inverted magnetic information of the underground space includes constructing the objective function of the conjugate gradient inversion using the L2 norm, and the objective function is expressed as:

[0028]

[0029] Apply the conjugate gradient algorithm to solve the objective function When the fitting error reaches the inversion requirement, the solution of the corresponding number of iterations is returned as the inversion result; in the process of solving the objective function, the corresponding correlation imaging calculation is performed on the forward data corresponding to the physical property model obtained each time, and the corresponding new correlation imaging coefficient is obtained, and the conversion coefficient is modified according to the new correlation imaging coefficient, and a new round of updating and constraining of the physical property parameters of the model is performed.

[0030] As a preferred scheme of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in the present invention, wherein: the inverted magnetic information of the underground space is mapped by mapping software, and geological interpretation is performed according to the mapping results, including using the model matrix M after physical property inversion to perform mapping analysis on the magnetic information of the underground space through mapping software, and performing corresponding geological interpretation according to the map.

[0031] Another object of the present invention is to provide a self-constrained magnetic anomaly property inversion system based on correlation coefficients, which can explore deep underground magnetic information.

[0032] To solve the above technical problems, the present invention provides the following technical solutions: a system of a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient, comprising: a data gridding module, an inversion module and an interpretation module; the data gridding module pre-processes the collected data, performs gridding processing on the measured data, obtains the measured two-dimensional data D, and divides the underground space into uniform grid units; the inversion module performs correlation imaging calculation on the measured data, calculates the correlation imaging coefficient matrix C, obtains the conversion coefficient matrix K according to the prior geological data, calculates the inversion initial model M0 according to the conversion coefficient matrix K and the correlation imaging coefficient matrix C, and performs physical property inversion calculation on the inversion initial model M0 using the conjugate gradient algorithm to obtain the inverted magnetic information of the underground space; the interpretation module uses mapping software to map the inverted magnetic information of the underground space, and performs geological interpretation according to the mapping results.

[0033] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the self-constrained magnetic anomaly property inversion method based on correlation coefficient are implemented as described above.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for self-constrained magnetic anomaly physical property inversion based on correlation coefficients.

[0035] The beneficial effects of the present invention are as follows: (1) The present invention introduces the observation data correlation coefficient matrix as a self-constraint condition, and uses the correlation coefficient matrix to self-constrain the fitting degree and roughness of the conjugate gradient inversion model, which can reduce the multi-solution of the inversion result, enhance the stability of the inversion result, and improve the resolution of the inversion result.

[0036] (2) The present invention suppresses the "skin effect" of potential field anomalies by combining the correlation coefficient matrix with the conversion coefficient matrix, which can further improve the resolution of the inversion results in the depth direction and more accurately determine the upper and lower boundaries of the geological body.

[0037] (3) The present invention introduces the correlation coefficient matrix as a self-constraint condition, avoiding the calculation of the depth weighting function, sensitivity matrix and regularization parameter of the conventional conjugate gradient inversion algorithm. When the amount of data to be processed is large, the calculation efficiency is still very high. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0039] Figure 1 This is a flow chart of a self-constrained magnetic anomaly property inversion method based on correlation coefficient in Example 1.

[0040] Figure 2 This is a schematic diagram of the model structure of a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient in Example 1.

[0041] Figure 3 This is a diagram of the calculation results of the magnetic anomaly physical property inversion based on the traditional conjugate gradient inversion method in Example 2.

[0042] Figure 4 This is a diagram of the calculation results of the magnetic anomaly property inversion of a self-constrained magnetic anomaly property inversion method based on correlation coefficient in Example 2.

[0043] Figure 5 This is a module structure diagram of a self-constrained magnetic anomaly property inversion system based on correlation coefficient in Example 3. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and which provides a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient, including: Figure 1 As shown:

[0047] Step 1: Preprocess the collected data and grid the measured data to obtain the measured two-dimensional data D.

[0048] In this embodiment, a synthetic model is used to demonstrate the effect of the self-constrained physical property inversion method in this patent. It consists of two cuboids, such as Figure 2 As shown, the magnetization intensity of the two cuboids is 0.2A / m. Assume that the inclination angle of the total magnetization intensity direction is 90° and the deflection angle is 0°. Forward calculation is performed on the synthetic model to obtain simulated magnetic anomaly data.

[0049] In this embodiment, the simulated magnetic anomaly data is gridded to obtain two-dimensional magnetic anomaly data D by gridding the simulated data into 20*20 grid data.

[0050] Step 2: Divide the underground space into uniform grid cells.

[0051] In this embodiment, the simulated underground space is evenly divided into 20*20*10 grid units.

[0052] Step 3: Perform correlation imaging calculation on the measured data and calculate the correlation imaging coefficient matrix C.

[0053] According to the correlation imaging algorithm, the correlation imaging coefficient of the qth grid unit is,

[0054]

[0055] Among them, N s is the total number of observation points in the survey area, B(x i ,y i ,z i ) is in (x i ,y i ,zi ) point measured magnetic anomaly, B q (x i ,y i ,z i ) is the qth grid cell in (x i ,y i ,z i ) point, the formula is expressed as:

[0056] B q (x i ,y i ,z i )=B qx (x i ,y i ,z i )L0+B qy (x i ,y i ,z i )M0+B qz (x i ,y i ,z i )N0

[0057] Among them, B qz (x i ,y i ,z i ), B qy (x i ,y i ,z i ), B qz (x i ,y i ,z i ) are B q (x i ,y i ,z i ) The magnetic field components in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions. If A represents the magnetic declination and I represents the magnetic inclination, the three direction cosines are expressed as L0=cosIcosA, M0=cosIsinA, and N0=sinI.

[0058] For a rectangular grid unit, if the three sides of the rectangular block are a, b, and c, the vertex coordinates are (x0, y0, z0), and the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the expression of the three components of the magnetic field of the cuboid unit is,

[0059]

[0060]

[0061]

[0062] Where r = [(ε-x i ) 2 +(η-y i ) 2 +(ζ-z i ) 2 ] 1 / 2 Represents the distance between any point in space and any point in the cuboid.

[0063] The correlation imaging coefficients of all grid cells constitute the correlation imaging coefficient matrix C.

[0064] The correlation imaging coefficients of all grid cells constitute a correlation imaging coefficient matrix C. In this embodiment, the correlation coefficient matrix C is a three-dimensional matrix of 20*20*10.

[0065] Step 4: Based on the prior geological data, the conversion coefficient matrix K is obtained, and the inversion initial model M0 is calculated based on the conversion coefficient matrix K and the related imaging coefficient matrix C.

[0066] According to the prior geological data of the area to be inverted and the corresponding imaging calculation results, the conversion coefficient matrix K is set.

[0067] The conversion coefficient matrix K is combined with the correlation imaging coefficient matrix C to calculate the inversion initial model M0 = K×C, and the inversion calculation initial model M0 is obtained.

[0068] Step 5: Use the conjugate gradient algorithm to perform physical property inversion calculations on the inversion initial model M0 to obtain the inverted magnetic information of the underground space.

[0069] The gridded measured two-dimensional data D is converted into measured one-dimensional data d;

[0070] According to the forward modeling formula of rectangular magnetic anomaly intensity, the forward kernel function G is calculated. The kernel function G is expressed as follows:

[0071] G(x i ,y i ,z i )=G x (x i ,y i ,z i )L0+G y (x i ,y i ,z i )M0+G z (x i ,yi ,z i )N0

[0072] Among them, G x (x i ,y i ,z i ), G y (x i ,y i ,z i 0, G z (x i ,y i ,z i ) are G(x i ,y i ,z i ) components in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions, respectively;

[0073] For a rectangular grid unit, if the three sides of the rectangular block are a, b, and c, the vertex coordinates are (x0, y0, z0), and the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the formula for the three components of the kernel function of the cuboid unit is expressed as,

[0074]

[0075]

[0076] The objective function of conjugate gradient inversion is constructed using the L2 norm. The objective function is expressed as:

[0077]

[0078] Apply the conjugate gradient algorithm to solve the objective function When the fitting error reaches the inversion requirement, the solution corresponding to the number of iterations is returned as the inversion result.

[0079] In the process of solving the objective function, the corresponding correlation imaging calculation is performed on the forward data corresponding to the physical property model obtained each time, and the corresponding new correlation imaging coefficient is obtained. The conversion coefficient is modified according to the new correlation imaging coefficient, and a new round of updating and constraining of the physical property parameters of the model is carried out.

[0080] In this embodiment, the conjugate gradient algorithm is applied to calculate the inversion magnetization intensity matrix M of the underground space model, and the matrix is ​​a three-dimensional matrix of 20*20*10.

[0081] Step 6: Use mapping software to map the inverted magnetic information of the underground space and perform geological interpretation based on the mapping results.

[0082] The model matrix M after physical property inversion is used to conduct mapping analysis of underground space magnetic information through mapping software, and the corresponding geological interpretation is performed based on the map.

[0083] Embodiment 2 is the second embodiment of the present invention, which is different from the first embodiment in that: a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient also includes: in order to verify and illustrate the technical effect adopted in this method, this embodiment adopts traditional technical solutions and the method of the present invention for comparative testing, and compares the test results by means of scientific demonstration to verify the real effect of this method.

[0084] In this embodiment, two groups of inversion comparison experiments are conducted on the model data. In the first group, the traditional conjugate gradient inversion method is used for inversion calculation, and the magnetic anomaly inversion information results obtained at the vertical section Z = 50m and the horizontal section X = 100m are as follows: Figure 3 As shown in the second group, the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient is used for inversion calculation, and the magnetic anomaly inversion information results obtained at the vertical section Z = 50m and the horizontal section X = 100m are as follows Figure 4 Among them, both groups use Surfer software to map the inversion calculation results. The obtained mapping results can show the spatial position and shape of the geological body, so as to further analyze the faults and structural boundaries existing underground.

[0085] From the comparison of the two groups of inversion results, it can be seen that the inversion results of the self-constrained magnetic anomaly physical property inversion method based on correlation coefficient of the present invention have significantly improved depth resolution compared with the traditional conjugate gradient inversion method, and can more accurately determine the spatial position and upper and lower boundaries of the geological body.

[0086] Example 3, reference Figure 5, which is the third embodiment of the present invention, and is different from the previous two embodiments in that: a system of a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient includes a data gridding module 100, an inversion module 200 and an interpretation module 300; the data gridding module 100 pre-processes the collected data, grids the measured data, obtains the measured two-dimensional data D, and divides the underground space into uniform grid units; the inversion module 200 performs correlation imaging calculation on the measured data, calculates the correlation imaging coefficient matrix C, obtains the conversion coefficient matrix K according to the prior geological data, calculates the inversion initial model M0 according to the conversion coefficient matrix K and the correlation imaging coefficient matrix C, and uses the conjugate gradient algorithm to perform physical property inversion calculation on the inversion initial model M0 to obtain the inverted magnetic information of the underground space; the interpretation module 300 uses mapping software to map the inverted magnetic information of the underground space, and performs geological interpretation according to the mapping results.

[0087] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0089] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0090] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A self-constrained magnetic anomaly property inversion method based on correlation coefficient, characterized by: include, Preprocess the collected data, grid the measured data, and obtain measured two-dimensional data D; Divide the underground space into uniform grid units; Perform correlation imaging calculation on the measured data and calculate the correlation imaging coefficient matrix C; According to the prior geological data, the conversion coefficient matrix K is obtained, and the inversion initial model M0 is calculated according to the conversion coefficient matrix K and the related imaging coefficient matrix C; The conjugate gradient algorithm is used to perform physical property inversion calculation on the initial inversion model M0 to obtain the inversion magnetic information of the underground space; The inverted magnetic information of the underground space is mapped using mapping software, and geological interpretation is performed based on the mapping results.

2. A method for inversion of physical properties of self-constrained magnetic anomalies based on correlation coefficients as claimed in claim 1, characterized in that: The method of performing correlation imaging calculation on the measured data and calculating the correlation imaging coefficient matrix C includes: according to the correlation imaging algorithm, the correlation imaging coefficient of the qth grid unit is: Among them, N s is the total number of observation points in the survey area, B(x i ,y i ,z i ) is in (x i ,y i ,z i ) point measured magnetic anomaly, B q (x i ,y i ,z i ) is the qth grid cell in (x i ,y i ,z i ) point, the formula is: B q (x i ,y i ,z i )=B qx (x i ,y i ,z i )L0+B qy (x i ,y i ,z i )M0+B qz (x i ,y i ,z i )N0 Among them, B qx (x i ,y i ,z i ), B qy (x i ,y i ,z i ), B qz (x i ,y i ,z i ) are B q (x i ,y i ,z i ) The magnetic field components in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions. If A represents the magnetic declination and I represents the magnetic inclination, the three direction cosines are expressed as L0=cosIcosA, M0=cosIsinA, and N0=sinI.

3. A method for inversion of physical properties of self-constrained magnetic anomalies based on correlation coefficients as claimed in claim 2, characterized in that: The correlation imaging calculation of the measured data and the calculation of the correlation imaging coefficient matrix C also include, for a rectangular grid unit, if the three sides of the rectangular grid unit are a, b, and c respectively, the vertex coordinates are (x0, y0, z0), and the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the expression of the three components of the magnetic field of the cuboid unit is, Where r = [(ε-x i ) 2 +(η-y i ) 2 +(ζ-z i ) 2 ] 1 / 2 Indicates the distance between any point in space and any point in the cuboid; The correlation imaging coefficients of all grid cells constitute the correlation imaging coefficient matrix C.

4. A method for inversion of physical properties of self-constrained magnetic anomalies based on correlation coefficients as claimed in claim 3, characterized in that: The obtaining of the conversion coefficient matrix K according to the prior geological data includes setting the conversion coefficient matrix K according to the prior geological data of the area to be inverted in combination with the corresponding imaging calculation results; The calculation of the inversion initial model M0 according to the conversion coefficient matrix K and the correlation imaging coefficient matrix C includes combining the conversion coefficient matrix K with the correlation imaging coefficient matrix C, calculating the inversion initial model M0=K×C, and obtaining the inversion calculation initial model M0.

5. A method for inversion of physical properties of self-constrained magnetic anomalies based on correlation coefficients as claimed in claim 4, characterized in that: Before the inversion initial model M0 is inverted by the conjugate gradient algorithm to obtain the inverted magnetic information of the underground space, the measured two-dimensional data D after gridding is converted into measured one-dimensional data d; According to the forward modeling formula of rectangular magnetic anomaly intensity, the forward kernel function G is calculated. The kernel function G is expressed as follows: G(x i ,y i ,z i )=G x (x i ,y i ,z i )L0+G y (x i ,y i ,z i )M0+G z (x i ,y i ,z i )N0 Among them, G x (x i ,y i ,z i ), G y (x i ,y i ,z i 0, G z (x i ,y i ,z i ) are G(x i ,y i ,z i ) components in the x, y, and z directions, L0, M0, and N0 are the direction cosines of the magnetic field in the three directions, respectively; For a rectangular grid unit, if the three sides of the rectangular block are a, b, and c, the vertex coordinates are (x0, y0, z0), and the coordinates of any point in space are (x i ,y i ,z i ), the coordinates of any point in the cuboid are (ε, η, ζ), then the formula for the three components of the kernel function of the cuboid unit is expressed as, 6. A method for inversion of physical properties of self-constrained magnetic anomalies based on correlation coefficients as claimed in claim 5, characterized in that: The method of using the conjugate gradient algorithm to perform physical property inversion calculation on the inversion initial model M0 to obtain the inverted magnetic information of the underground space includes constructing the objective function of the conjugate gradient inversion using the L2 norm. The objective function is expressed as: Apply the conjugate gradient algorithm to solve the objective function When the fitting error reaches the inversion requirement, the solution corresponding to the number of iterations is returned as the inversion result; In the process of solving the objective function, the corresponding correlation imaging calculation is performed on the forward data corresponding to the physical property model obtained each time, and the corresponding new correlation imaging coefficient is obtained. The conversion coefficient is modified according to the new correlation imaging coefficient, and a new round of updating and constraining of the physical property parameters of the model is performed.

7. A method for self-constrained magnetic anomaly physical property inversion based on correlation coefficient according to claim 6, characterized in that: The inverted magnetic information of underground space is mapped by mapping software, and the geological interpretation is performed according to the mapping results, including using the model matrix M after physical property inversion to map the magnetic information of underground space through mapping software, and performing corresponding geological interpretation according to the map.

8. A system using a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient as claimed in any one of claims 1 to 7, characterized in that: It includes a data gridding module (100), an inversion module (200) and an interpretation module (300); The data gridding module (100) pre-processes the collected data, performs gridding processing on the measured data, obtains measured two-dimensional data D, and divides the underground space into uniform grid units; The inversion module (200) performs correlation imaging calculation on measured data, calculates a correlation imaging coefficient matrix C, obtains a conversion coefficient matrix K based on prior geological data, calculates an initial inversion model M0 based on the conversion coefficient matrix K and the correlation imaging coefficient matrix C, and performs physical property inversion calculation on the initial inversion model M0 using a conjugate gradient algorithm to obtain inversion magnetic information of the underground space; The interpretation module (300) uses mapping software to map the inverted magnetic information of the underground space and performs geological interpretation based on the mapping results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a self-constrained magnetic anomaly physical property inversion method based on correlation coefficient described in any one of claims 1 to 7 are implemented.

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