A 3D electromagnetic inversion method based on point cloud technology to decouple unstructured forward and inversion grids

Abstract

The invention discloses a three-dimensional electromagnetic inversion method for decoupling unstructured forward and inversion grids based on point cloud technology, comprising: acquiring inversion data, presetting an inversion iteration stop threshold; constructing a forward model and performing tetrahedral grid division; establishing an overall inversion model and performing tetrahedral grid division; calculating evenly distributed points in each tetrahedral grid in the forward model, and obtaining the intersection volume of each tetrahedral grid in each forward model and the tetrahedral grid of the inversion model; establishing a mapping relationship between forward grid parameters and inversion model grid parameters; each forward model independently calculates the objective function and the objective function gradient of the measurement points contained therein; calculating the inversion data fitting difference, and mapping the objective function gradients calculated by all forward grids to the inversion grid by using the mapping relationship of the forward and inversion grid parameters; updating the inversion parameters of the inversion grid and mapping the updated model parameters to the forward grid for iterative inversion until the convergence condition is met.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical exploration, in particular to a three-dimensional electromagnetic inversion method based on point cloud technology to decouple unstructured forward and inversion grids. Background Art

[0002] As we all know, controlled source electromagnetic method has the advantages of high transmission power and strong anti-interference ability, and has been widely used in mineral exploration and engineering geology. Compared with magnetic source emission, electrical source emission has a large exploration depth, is more sensitive to high-resistance media, and has good vertical resolution. It is widely used in electromagnetic exploration fields such as semi-aerial electromagnetic method and marine electromagnetic method. As the exploration environment becomes more and more complex and the exploration accuracy requirements become higher and higher, conventional one- and two-dimensional inversion methods can no longer meet the exploration requirements. At present, three-dimensional inversion methods have become a hot spot and difficulty in research in this field. However, the number of grids in three-dimensional inversion is large, the amount of calculation is large, and the inversion process is very time-consuming. Therefore, it is very necessary to develop a fast three-dimensional inversion method.

[0003] At present, the finite element method based on unstructured tetrahedral grids can accurately simulate complex models and can be used for detailed detection of complex terrain areas. It is one of the most commonly used methods in electromagnetic inversion. For the finite element method, in order to obtain an accurate solution, it is necessary to finely divide the area near the receiving point. However, there are many receiving points in electromagnetic exploration, and the number of finely divided grids can reach millions or even tens of millions, which consumes huge computing resources and has low computing efficiency. At present, the research in the existing technology uses a consistent grid for forward and inversion calculations. The grid contains all receiving points, resulting in a huge amount of grids and reduced computing efficiency.

[0004] For example, the Chinese invention patent with the patent publication number of CN114970290A and the name of a parallel inversion method and system for ground transient electromagnetic method collects observed transient electromagnetic response data; divides the inversion space into unstructured tetrahedral grids and sets the conductivity value, constructs the initial inversion model and the regularized objective function; calculates the product of the sensitivity matrix and the vector of the model parameters; uses the high-speed-Newton method equation to convert the objective function into a least squares problem, obtains the model update direction, and uses linear search to obtain the optimal model update step size and update the inversion model; performs three-dimensional transient electromagnetic forward modeling based on the vector finite element method to obtain predicted transient electromagnetic response data; uses normalized error to evaluate the fit between the predicted transient electromagnetic response data and the observed transient electromagnetic response data; if the normalized fit difference of the data reaches the preset threshold, the inversion method terminates. This technology uses a set of grids for forward and inversion. When there are too many receiving points, the large number of calculation grids leads to low calculation efficiency.

[0005] Another example is the Chinese invention patent with the patent publication number: CN115113286A, titled: Three-dimensional inversion method based on multi-component frequency domain airborne electromagnetic data fusion. First, based on the flexible modeling characteristics of tetrahedral grids, frequency domain airborne electromagnetic finite element three-dimensional forward simulation can be carried out under any complex structure and undulating terrain conditions, providing an effective prerequisite for refined three-dimensional inversion; secondly, based on the quasi-Newton method with limited memory, the approximation of the Hessian matrix is ​​realized, effectively speeding up the airborne electromagnetic inversion speed; after comparing the inversion results of a single data and the inversion results of multiple data fusion. Similarly, this technology uses a set of grids for forward and inversion. When there are too many receiving points, the large number of calculation grids leads to low calculation efficiency.

[0006] Another example is the Chinese invention patent with the patent publication number: CN115755199A, titled: A Practical Unstructured Grid Three-Dimensional Electromagnetic Inversion Smoothing Regularization Method, which includes: obtaining measured electromagnetic data; applying a smoothing regularization operator to construct a three-dimensional electromagnetic inversion regularization objective function, wherein the smoothing regularization operator is constructed based on a model parameter vector under a logarithmic constraint domain; forward calculating the electromagnetic response data of the current geological model; combining the electromagnetic response data and the measured electromagnetic data to calculate the first-order partial derivative of the three-dimensional electromagnetic inversion regularization objective function relative to the model parameter vector under the logarithmic constraint domain, and obtaining the gradient of the current geological model; updating the model parameter vector under the logarithmic constraint domain according to the gradient of the current geological model; converting the updated model parameter vector into the model parameter of the non-logarithmic constraint domain; iterative inversion until the iteration termination condition is met, and outputting the final updated model parameters. This technology mainly proposes a new type of smoothing regularization method, and the same forward and inversion grid is still used during the inversion process.

[0007] Another example is the Chinese invention patent "Patent Publication No.: CN116522713A, Name: Three-dimensional Inversion Method of Airborne Electromagnetic Data Partition Based on Block Coordinate Descent Algorithm", which is as follows: setting the scope of the three-dimensional inversion sub-area of ​​airborne electromagnetic data and the information of the measurement points; establishing the initial three-dimensional inversion model based on the partitioned electromagnetic data of the unstructured tetrahedral grid; defining the three-dimensional inversion objective function and deriving the inversion equation based on the "separability" of the airborne electromagnetic inversion independent variable; generating unstructured local grids for forward modeling and adjoint forward modeling based on the grid extension algorithm; three-dimensional forward modeling of the airborne electromagnetic system response and calculation of the adjoint forward sensitivity matrix; calculating the model update vector, iteratively correcting the model to obtain the inversion result. The disadvantage of this technology is that for the airborne electromagnetic method, there are many sampling points, but the distance between the transmitting source and the receiving point is close. This method extracts a single sampling point grid from the inversion overall grid and then uses the grid extension to establish a new grid, and the extracted grid is consistent with the inversion grid. However, for the electrical source method, the transmitting and receiving distance is large, and the number of calculation grids cannot be reduced after extracting the sampling point grid.

[0008] Therefore, there is an urgent need to propose a three-dimensional electromagnetic inversion method based on point cloud technology to decouple unstructured forward and inversion grids that is simple in logic, accurate and reliable, and can improve transportation efficiency. Summary of the invention

[0009] In view of the above problems, the purpose of the present invention is to provide a three-dimensional electromagnetic inversion method based on point cloud technology to decouple unstructured forward and inversion grids. The technical solution adopted by the present invention is as follows:

[0010] Step S01, obtaining inversion data of the detection area and presetting an inversion iteration stop threshold;

[0011] Step S02, dividing the measuring points into arrays according to the spatial position distribution of the measuring lines and measuring points of electromagnetic exploration, and constructing a forward model corresponding to the number of measuring point groups;

[0012] Step S03, performing tetrahedral meshing on any forward model to obtain a plurality of first tetrahedral forward meshes;

[0013] Step S04, establishing an overall inversion model of the detection area, and performing tetrahedral meshing to obtain a first tetrahedral inversion mesh;

[0014] Step S05, calculating N points uniformly distributed in any first tetrahedral forward modeling grid, and using the N points uniformly distributed to approximately calculate the intersection volume of any first tetrahedral forward modeling grid and the first tetrahedral inversion grid;

[0015] Step S06, establishing a mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh through the intersection volume;

[0016] Step S07, respectively calculating the objective function and the objective function gradient of any forward model including the measurement points;

[0017] Step S08, calculating the inversion data fitting error, using the mapping relationship between the model parameters of the first tetrahedral forward modeling grid and the model parameters of the first tetrahedral inversion grid, mapping the objective function gradient of any forward model to the first tetrahedral inversion grid, and obtaining a second tetrahedral inversion grid;

[0018] Step S09, updating the inversion parameters of the second tetrahedral inversion grid, and mapping the updated inversion parameters to the first tetrahedral forward grid for iterative inversion, until a preset inversion iteration stop threshold is met.

[0019] Preferably, in step S01, the inversion data is the observation data d obs , the preset inversion iteration stopping threshold is 1.

[0020] Preferably, in step S02, the measuring points are divided into N fgroups, establish N f There are forward models, and any forward model contains a set of measurement points.

[0021] Furthermore, in the step S05, the intersection volume of any first tetrahedral forward mesh and the first tetrahedral inversion mesh is approximately calculated using N points evenly distributed, which is expressed as:

[0022]

[0023] in, is the number of points in the first tetrahedral forward modeling grid of the pth forward model, V i p is the volume of the first tetrahedral forward mesh in the pth forward model, is the number of points in the i-th first tetrahedral forward mesh in the p-th forward model distributed in the j-th first tetrahedral inversion mesh in the inversion model, It is the intersection volume of the i-th first tetrahedral forward mesh in the p-th forward model and the j-th first tetrahedral inversion mesh in the inversion model.

[0024] Furthermore, in step S06, a mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh is established through the intersection volume, and the expression is:

[0025]

[0026] in, is the model parameter of the jth first tetrahedral forward mesh in the pth forward model, is the number of inversion grids that intersect with the jth first tetrahedral forward grid in the pth forward model, f(k) is the number corresponding to the kth first tetrahedral inversion grid that intersects with the jth first tetrahedral forward grid, are the model parameters of the first tetrahedral inversion grid of the f(k)th grid, is the kth mapping weight coefficient in the pth forward model.

[0027] Furthermore, the mapping weight coefficient The expression is:

[0028]

[0029] in, is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(k)th first tetrahedral inversion mesh in the inversion model; is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(l)th first tetrahedral inversion mesh in the inversion model; is the number of points in the jth first tetrahedral forward mesh in the pth forward model distributed in the f(k)th first tetrahedral inversion mesh in the inversion model, is the number of points in the jth first tetrahedral forward grid in the pth forward model distributed in the f(l)th first tetrahedral inversion grid in the inversion model.

[0030] Furthermore, in step S07, the objective function and the objective function gradient of any forward model including the measurement points are calculated respectively, and the expressions thereof are:

[0031]

[0032] Among them, φ p is the objective function, is the observed data corresponding to the pth forward model, is the predicted data corresponding to the pth forward model, is the data weighting matrix corresponding to the pth forward model, g p is the objective function gradient of the pth forward model, J p is the sensitivity matrix of the pth forward model, m p,F is the pth forward model parameter vector, and T is the transpose of the matrix.

[0033] Furthermore, in step S08, the inversion data fitting error rms is expressed as:

[0034]

[0035] Among them, φ p is the objective function.

[0036] Furthermore, in step S08, the objective function gradient of any forward model is mapped to the first tetrahedral inversion grid by using the mapping relationship between the model parameters of the first tetrahedral forward grid and the model parameters of the first tetrahedral inversion grid, and the expression is:

[0037]

[0038] Among them, m I is the inversion model parameter vector, g I is the objective function with respect to m I The gradient of Q p is the mapping matrix corresponding to the pth forward model, and its elements are the mapping weight coefficients ω p , φ represents the overall objective function.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] (1) The present invention constructs a forward model and an inversion model, and respectively divides them into a tetrahedral forward mesh and a tetrahedral inversion mesh, and decouples the forward and inversion meshes (i.e., respectively calculates the objective function and the objective function gradient of each forward model including the measurement points; calculates the fitting error of the inversion data, and uses the mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh to map the objective function gradient of any forward model to the first tetrahedral inversion mesh). Different grids can be used to calculate the responses at different receiving points, which can effectively reduce the number of calculation grids and facilitate parallel calculation, thereby greatly improving the calculation speed.

[0041] (2) The present invention calculates N points uniformly distributed in each first tetrahedral forward mesh, and uses the uniformly distributed N points to approximately calculate the intersection volume of each first tetrahedral forward mesh and the first tetrahedral inversion mesh. A mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh is established through the intersection volume, and model parameters equivalent to those calculated using the same forward and inverse meshes can be obtained. The present invention uses the tetrahedral mesh intersection relationship to establish mesh parameter mapping to ensure the inversion calculation accuracy.

[0042] In summary, the present invention has the advantages of simple logic and fast calculation speed. It is suitable for different electromagnetic exploration fields, such as marine electromagnetic exploration, semi-aerial electromagnetic exploration, etc., and has high practical value and promotion value in the field of geophysical exploration technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 It is a logic flow chart of the present invention.

[0045] Figure 2 It is a slice diagram of the real model at x=0m.

[0046] Figure 3 This is a slice diagram of the inversion result of the traditional inversion method at x=0m.

[0047] Figure 4 It is a slice diagram of the inversion result of the present invention at x=0m.

[0048] Figure 5 This is a slice diagram of the real model at y=300m.

[0049] Figure 6 This is a slice diagram of the inversion result of the traditional inversion method at y=300m.

[0050] Figure 7 This is a slice diagram of the inversion result of the present invention at y=300m.

[0051] Figure 8 This is a slice diagram of the real model at z=125m.

[0052] Fig. 9 This is a slice diagram of the inversion result of the traditional inversion method at z=125m.

[0053] Fig.10 This is a slice diagram of the inversion result of the present invention at z=125m. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of this application clearer, the present invention is further described below in conjunction with the accompanying drawings and embodiments, and the embodiments of the present invention include but are not limited to the following embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0055] In this embodiment, the term "and / or" is merely a term used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0056] The terms "first" and "second" in the description and claims of this embodiment are used to distinguish different objects rather than to describe a specific order of objects. For example, a first target object and a second target object are used to distinguish different target objects rather than to describe a specific order of target objects.

[0057] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0058] like Figures 1 to 10 As shown, this embodiment provides a three-dimensional electromagnetic inversion method for decoupling unstructured forward and inversion grids based on point cloud technology, which includes the following steps:

[0059] The first step is to obtain the inversion data d obs , the preset inversion iteration stopping threshold is 1.

[0060] The second step is to divide the receiving points (i.e., measuring points) into N f groups, establish N f There are forward models, each model contains a set of receiving points, and each forward model is independently tetrahedral meshed, that is, the first tetrahedral forward mesh.

[0061] The third step is to establish the overall inversion model and perform tetrahedral meshing to obtain the first tetrahedral inversion mesh.

[0062] The fourth step is to calculate the N points uniformly distributed in each first tetrahedral forward mesh in each forward model, and approximate the intersection volume of each first tetrahedral forward mesh in each forward model and the first tetrahedral inversion mesh in the inversion model through the uniformly distributed points, which is expressed as:

[0063]

[0064] in, is the number of points in the first tetrahedral forward modeling grid of the pth forward model, V i p is the volume of the first tetrahedral forward mesh in the pth forward model, is the number of points in the i-th first tetrahedral forward mesh in the p-th forward model distributed in the j-th first tetrahedral inversion mesh in the inversion model, It is the intersection volume of the i-th first tetrahedral forward mesh in the p-th forward model and the j-th first tetrahedral inversion mesh in the inversion model.

[0065] The fifth step is to establish the mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh through the intersection volume, and the expression is:

[0066]

[0067] in, is the model parameter of the jth first tetrahedral forward mesh in the pth forward model, is the number of inversion grids that intersect with the jth first tetrahedral forward grid in the pth forward model, f(k) is the number corresponding to the kth first tetrahedral inversion grid that intersects with the jth first tetrahedral forward grid, are the model parameters of the first tetrahedral inversion grid of the f(k)th grid, is the mapping weight coefficient.

[0068] Among them, the mapping weight coefficient The expression is:

[0069]

[0070] in, is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(k)th first tetrahedral inversion mesh in the inversion model; is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(l)th first tetrahedral inversion mesh in the inversion model; is the number of points in the jth first tetrahedral forward mesh in the pth forward model distributed in the f(k)th first tetrahedral inversion mesh in the inversion model, is the number of points in the jth first tetrahedral forward grid in the pth forward model distributed in the f(l)th first tetrahedral inversion grid in the inversion model.

[0071] The sixth step is to calculate the objective function of each measurement point contained in each forward model, and its expression is:

[0072]

[0073] Among them, φ p is the objective function, is the observed data corresponding to the pth forward model, is the predicted data corresponding to the pth forward model, is the data weighting matrix corresponding to the pth forward model.

[0074] Calculate the objective function gradient of each measurement point contained in each forward model, and its expression is:

[0075]

[0076] Among them, g p is the objective function gradient of the pth forward model, J p is the sensitivity matrix of the pth forward model, m p,F is the pth forward model parameter vector.

[0077] The seventh step is to calculate the inversion data fitting error, which is expressed as:

[0078]

[0079] Calculate the objective function gradient of the inversion grid, and its expression is:

[0080]

[0081] Among them, m I is the inversion model parameter vector, g I is the objective function with respect to m I The gradient of Q pis the mapping matrix corresponding to the pth forward model, and its elements are the mapping weight coefficients ω p .

[0082] In the eighth step, the inversion parameters of the inversion grid are updated and the updated model parameters are mapped to the forward grid for iterative inversion until rms ≤ 1.

[0083] Here is a real case:

[0084] This implementation case provides a three-dimensional electromagnetic rapid inversion based on point cloud technology to decouple unstructured forward and inversion grids. A 1000-meter-long wire source is used, and the transmitting current is 1A. The effective area of ​​the receiving coil is 1 square meter, the flight altitude of the aircraft is 50 meters, and the 8 survey lines are parallel to the long wire source. The receiving and transmitting distance is 200m-1300m, and the line spacing is 100m. Each survey line has 101 measuring points, and the distribution range is -500m-500m. The method of the present invention uses 4 forward modeling grids, each grid contains 2 survey lines, and the number of grid cells is 463,078, 464,010, 464,762 and 463,453 respectively. The number of inversion grid cells used in the method of the present invention is 181,259, and the processing time is about 24 hours. The traditional inversion method uses a consistent forward and inversion grid with a grid number of 1,053,184, and the inversion takes about 46 hours. In addition, from Figures 2 to 10 It can be seen that the resistivity inversion result of this example is basically consistent with the resistivity inversion of the traditional inversion method, and can well reflect the resistivity information of the underground. In summary, compared with the traditional three-dimensional inversion method in the prior art, the forward and inversion grid decoupling inversion method in the scheme of the present invention can effectively improve the calculation efficiency while ensuring the inversion accuracy. The above practical application results of the present invention show that the forward and inversion grid decoupling inversion method proposed by the present invention has important application value and practical significance in electromagnetic exploration.

[0085] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any changes that adopt the design principles of the present invention and are made through non-creative labor on this basis should fall within the protection scope of the present invention.

Claims

1. A three-dimensional electromagnetic inversion method based on point cloud technology to decouple unstructured forward and inversion grids, characterized in that: The following steps are involved: Step S01, obtaining inversion data of the detection area and presetting an inversion iteration stop threshold; Step S02, dividing the measuring points into arrays according to the spatial position distribution of the measuring lines and measuring points of electromagnetic exploration, and constructing a forward model corresponding to the number of measuring point groups; Step S03, performing tetrahedral meshing on any forward model to obtain a plurality of first tetrahedral forward meshes; Step S04, establishing an overall inversion model of the detection area, and performing tetrahedral meshing to obtain a first tetrahedral inversion mesh; Step S05, calculating N points uniformly distributed in any first tetrahedral forward modeling grid, and using the N points uniformly distributed to approximately calculate the intersection volume of any first tetrahedral forward modeling grid and the first tetrahedral inversion grid; Step S06, establishing a mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh through the intersection volume; Step S07, respectively calculating the objective function and the objective function gradient of any forward model including the measurement points; Step S08, calculating the inversion data fitting error, using the mapping relationship between the model parameters of the first tetrahedral forward modeling grid and the model parameters of the first tetrahedral inversion grid, mapping the objective function gradient of any forward model to the first tetrahedral inversion grid, and obtaining a second tetrahedral inversion grid; Step S09, updating the inversion parameters of the second tetrahedral inversion grid, and mapping the updated inversion parameters to the first tetrahedral forward grid for iterative inversion, until a preset inversion iteration stop threshold is met.

2. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grid according to claim 1 is characterized in that: In step S01, the inversion data is the observation data d obs , the preset inversion iteration stopping threshold is 1.

3. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grid according to claim 1 or 2, characterized in that: In step S02, the measuring points are divided into N f groups, establish N f There are forward models, and any forward model contains a set of measurement points.

4. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grid according to claim 3 is characterized in that: In the step S05, the intersection volume of any first tetrahedral forward mesh and the first tetrahedral inversion mesh is approximately calculated using uniformly distributed N points, which is expressed as: in, is the number of points in the first tetrahedral forward modeling grid of the pth forward model, V i p is the volume of the first tetrahedral forward mesh in the pth forward model, is the number of points in the i-th first tetrahedral forward mesh in the p-th forward model distributed in the j-th first tetrahedral inversion mesh in the inversion model, It is the intersection volume of the i-th first tetrahedral forward mesh in the p-th forward model and the j-th first tetrahedral inversion mesh in the inversion model.

5. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grid according to claim 4 is characterized in that: In step S06, a mapping relationship between the model parameters of the first tetrahedral forward mesh and the model parameters of the first tetrahedral inversion mesh is established through the intersection volume, and the expression is: in, is the model parameter of the jth first tetrahedral forward mesh in the pth forward model, is the number of inversion grids that intersect with the jth first tetrahedral forward grid in the pth forward model, f(k) is the number corresponding to the kth first tetrahedral inversion grid that intersects with the jth first tetrahedral forward grid, are the model parameters of the first tetrahedral inversion grid of the f(k)th grid, is the kth mapping weight coefficient in the pth forward model.

6. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grids according to claim 5 is characterized in that: The mapping weight coefficient The expression is: in, is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(k)th first tetrahedral inversion mesh in the inversion model; is the intersection volume of the jth first tetrahedral forward mesh in the pth forward model and the f(l)th first tetrahedral inversion mesh in the inversion model; is the number of points in the jth first tetrahedral forward mesh in the pth forward model distributed in the f(k)th first tetrahedral inversion mesh in the inversion model, is the number of points in the jth first tetrahedral forward grid in the pth forward model distributed in the f(l)th first tetrahedral inversion grid in the inversion model.

7. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grids according to claim 2 is characterized in that: In step S07, the objective function and the objective function gradient of any forward model including the measurement points are calculated respectively, and the expressions are as follows: Among them, φ p is the objective function, is the observed data corresponding to the pth forward model, is the predicted data corresponding to the pth forward model, is the data weighting matrix corresponding to the pth forward model, g p is the objective function gradient of the pth forward model, J p is the sensitivity matrix of the pth forward model, m p,F is the pth forward model parameter vector, and T is the transpose of the matrix.

8. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grids according to claim 7 is characterized in that: In step S08, the inversion data fitting error rms is expressed as: Among them, φ p is the objective function.

9. The three-dimensional electromagnetic inversion method based on point cloud technology decoupling unstructured forward and inversion grids according to claim 8 is characterized in that: In step S08, the objective function gradient of any forward model is mapped to the first tetrahedral inversion grid by using the mapping relationship between the model parameters of the first tetrahedral forward grid and the model parameters of the first tetrahedral inversion grid. The expression is: Among them, m I is the inversion model parameter vector, g I is the objective function with respect to m I The gradient of Q p is the mapping matrix corresponding to the pth forward model, and its elements are the mapping weight coefficients ω p , φ represents the overall objective function.

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