Self-adaptive three-dimensional gravity inversion method, medium and system

Abstract

The invention provides a self-adaptive three-dimensional gravity inversion method, medium and system, and belongs to the technical field of geophysics, and the method comprises the steps: extracting residual gravity anomalies according to the gravity anomalies of an inversion target area, and carrying out the mesh generation of an inversion space, so as to construct a model; calculating a normalized vertical guide number analysis signal amplitude value of the residual gravity anomaly in the inversion space by using the residual gravity anomaly of the inversion target area; analyzing a signal amplitude value according to the normalized vertical guide to calculate a self-adaptive p value variable of an Lp norm; linearly representing the Lp norm by adopting a Lawson formula, and correcting system deviation generated by the self-adaptive p value variable so as to construct a self-adaptive Lp norm objective function; and solving the adaptive Lp norm objective function by adopting an iterative reweighted least square algorithm to obtain the residual density distribution of the inversion space. The technical problem that in the prior art, the p value with the highest inversion precision cannot be selected, so that the inversion adaptability is reduced is solved.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical technology, and in particular, relates to an adaptive three-dimensional gravity inversion method, medium and system. Background Art

[0002] As an important geophysical exploration method, gravity exploration has the advantages of low cost, high efficiency, large exploration depth and rapid acquisition of area information. In the fields of deep density structure research and mineral resource exploration, three-dimensional gravity inversion is a key technology for quantitative interpretation. Traditional three-dimensional gravity inversion is based on the L2 norm, which tends to obtain smoother inversion results. It is suitable for target geological bodies with gentle density changes, but it is difficult to accurately describe target geological bodies with significant density changes or steep boundaries. Therefore, the Lp norm (0≤p<2) has gradually become a development trend. Among them, the L1 norm and the L0 norm are more commonly used to obtain sparse models or compact inversion results. However, in practical applications, it is difficult to obtain the density change characteristics of unknown underground geological bodies, or there may be multiple geological bodies with different density change characteristics, which makes it difficult to select a suitable p value for inversion and cannot accurately describe the density distribution characteristics of the target geological body.

[0003] In order to reasonably select the p value for inversion and obtain the inversion results that can more accurately reflect the density distribution characteristics of the target geological body, the researchers used adaptive Lp norm, spatially varying Lp norm, mixed Lp norm, etc. for inversion. The research ideas of these methods are basically the same. They all select the p value based on the characteristics of the abnormal field and use statistical algorithms or edge recognition methods. Generally, the p value is 0, 1, or 2.

[0004] Although these methods have balanced the inversion effects of geological bodies with different density change characteristics to a certain extent, the selection range of p-values ​​is limited and is not a continuous change space, resulting in the inability to select the p-value with the highest inversion accuracy, which reduces the adaptability of the inversion. Summary of the invention

[0005] In view of this, the present invention provides an adaptive three-dimensional gravity inversion method, medium and system, which can solve the technical problem in the prior art that the p value with the highest inversion accuracy cannot be selected, resulting in reduced adaptability of the inversion.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides an adaptive three-dimensional gravity inversion method, comprising the following steps:

[0008] S10, extracting the residual gravity anomaly according to the gravity anomaly of the inversion target area, and performing grid division on the inversion space to construct a model;

[0009] S20, using the residual gravity anomaly in the inversion target area, calculating the normalized vertical derivative analytical signal amplitude value of the residual gravity anomaly in the inversion space, recorded as the NVDR-ASA value;

[0010] S30, calculating an adaptive p-value variable of the Lp norm according to the amplitude value of the normalized vertical derivative analytical signal;

[0011] S40, using the Lawson formula to linearly represent the Lp norm, and correcting the system deviation generated by the adaptive p-value variable, thereby constructing an adaptive Lp norm objective function;

[0012] S50, using an iterative reweighted least squares algorithm to solve the adaptive Lp norm objective function to obtain the residual density distribution in the inversion space.

[0013] Preferably, calculating the adaptive p-value variable of the Lp norm according to the amplitude value of the normalized vertical derivative analytical signal specifically includes:

[0014] The adaptive p-value variable is calculated based on the principle that the Lp norm value and the degree of abnormal gradient change are mirror images; the calculation formula of the adaptive p-value variable is:

[0015] p(ξ,η)=α[1-NVDR-ASA(ξ,η)];

[0016] Among them, p(ξ,η) represents the p-value variable of the subdivision unit corresponding to the horizontal position (ξ,η) of the observation surface, NVDR-ASA(ξ,,η,) represents the amplitude of the normalized vertical derivative analytical signal at the position (ξ,,η) of the observation surface, and the amplitude range is [0,1]. The larger the amplitude, the greater the change gradient of the residual gravity anomaly field. α is a parameter for adjusting the value space of p(ξ,η), which can take a value between (0,2], so that the value space of p(ξ,η) is between [0,2], thereby taking into account the distribution characteristics of different inversion target geological bodies.

[0017] Preferably, the Lawson formula is used to linearly represent the Lp norm, and the system deviation generated by the adaptive p-value variable is corrected, so as to construct an adaptive Lp norm objective function, including:

[0018] The Lawson formula is used to linearize the Lp norm; the Lawson formula linearization expression of the Lp norm is:

[0019]

[0020] in, represents the linearized Lp norm constructed at the kth iteration, f(m) (k)represents the value to be solved for the kth iteration, f(m) represents the (k-1)th iteration value, and ε is a very small value used to prevent singularities, which can be a value between [1E-10, 1E-2].

[0021] Correct the systematic deviation caused by the different amplitude variation range of the first-order derivative of the constructed linearized Lp norm when taking different p values ​​due to the introduction of the p-value variable. At this time, the correction value is constructed based on the maximum value of the first-order derivative of the linearized Lp norm, so as to perform normalization processing on it; the calculation formula of the correction value is:

[0022]

[0023] in, represents the square of the correction value of k iterations, g(m) max(k)p is a parameter related to the maximum value of the first-order derivative of the linearized Lp norm constructed at the kth iteration, and its value is the same as f(m) (k-1) It is determined by finding the zero point of the second-order derivative of the linearized Lp norm. When g(m) is taken max(k)p When f(m) (k-1) Take The calculation formula is:

[0024]

[0025] Among them, f(m) max is the maximum value of f(m).

[0026] According to the Lawson formula, the Lp norm is linearized and the correction value is introduced to construct the adaptive Lp norm objective function; among them, the adaptive Lp norm objective function The expression formula is:

[0027]

[0028] in, represents the data fitting project standard function, represents the model constraint item target function constructed using the adaptive Lp norm, α is the regularization factor, m is the residual density vector, G is the kernel function matrix, d is the gravity anomaly vector, Wd is the data item constraint matrix, Wm is the model item constraint matrix, and Wmkp represents the adaptive Lp norm coefficient matrix after correction at the kth iteration. The expression is:

[0029]

[0030] Among them, w mkpi (m) represents the corrected adaptive Lp norm coefficient used by the i-th segmentation unit, γ kpi is the correction value used for the ith mesh element, mi(k-1) It represents the residual density of the i-th mesh element calculated by the (k-1)th iteration.

[0031] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned adaptive three-dimensional gravity inversion method.

[0032] A third aspect of the present invention provides an adaptive three-dimensional gravity inversion system, comprising the above-mentioned computer-readable storage medium.

[0033] Compared with the prior art, the adaptive three-dimensional gravity inversion method, medium and system provided by the present invention have the following beneficial effects: by utilizing the normalized vertical derivative analytical signal amplitude to select the p value in the adaptive Lp norm, constructing the Lp norm based on the Lawson formula, and introducing a correction value to eliminate the system deviation caused by the p value variable, and finally constructing an adaptive Lp norm objective function, the adaptability of three-dimensional gravity inversion in practical applications is improved, and the technical problem that the p value with the highest inversion accuracy cannot be selected in the prior art, resulting in reduced adaptability of the inversion, is solved, and a more reliable inversion basis is provided for deep density structure research, mineral resource exploration, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] like Figure 1 FIG. 1 is a flow chart of an adaptive three-dimensional gravity inversion method provided by the present invention. The method comprises the following steps:

[0037] S10, extracting the residual gravity anomaly according to the gravity anomaly of the inversion target area, and performing grid division on the inversion space to construct a model;

[0038] S20, using the residual gravity anomaly in the inversion target area, calculating the normalized vertical derivative analytical signal amplitude value of the residual gravity anomaly in the inversion space, recorded as the NVDR-ASA value;

[0039] S30, calculating an adaptive p-value variable of the Lp norm according to the amplitude value of the normalized vertical derivative analytical signal;

[0040] S40, using Lawson's formula to linearize the Lp norm and correcting the system deviation generated by the adaptive p-value variable, thereby constructing an adaptive Lp norm objective function;

[0041] S50, using an iterative reweighted least squares algorithm to solve the adaptive Lp norm objective function to obtain the residual density distribution in the inversion space.

[0042] The adaptive p-value variable of the Lp norm is calculated according to the amplitude value of the normalized vertical derivative analytical signal, specifically including:

[0043] The adaptive p-value variable is calculated based on the principle that the Lp norm value and the degree of abnormal gradient change are mirror images; the calculation formula of the adaptive p-value variable is:

[0044] p(ξ,η)=α[1-NVDR-ASA(ξ,η)];

[0045] Among them, p(ξ,η) represents the p-value variable of the subdivision unit corresponding to the horizontal position (ξ,η) of the observation surface, NVDR-ASA(ξ,,η,) represents the amplitude of the normalized vertical derivative analytical signal at the position (ξ,,η) of the observation surface, and the amplitude range is [0,1]. The larger the amplitude, the greater the change gradient of the residual gravity anomaly field. α is a parameter for adjusting the value space of p(ξ,η), which can take a value between (0,2], so that the value space of p(ξ,η) is between [0,2], thereby taking into account the distribution characteristics of different inversion target geological bodies.

[0046] Preferably, the Lawson formula is used to linearly represent the Lp norm, and the system deviation generated by the adaptive p-value variable is corrected, so as to construct an adaptive Lp norm objective function, including:

[0047] The Lawson formula is used to linearize the Lp norm; the Lawson formula linearization expression of the Lp norm is:

[0048]

[0049] in, represents the linearized Lp norm constructed at the kth iteration, f(m) (k) represents the value to be solved for the kth iteration, f(m) represents the (k-1)th iteration value, and ε is a very small value used to prevent singularities, which can be a value between [1E-10, 1E-2].

[0050] Correct the systematic deviation caused by the different amplitude variation range of the first-order derivative of the constructed linearized Lp norm when taking different p values ​​due to the introduction of the p-value variable. At this time, the correction value is constructed based on the maximum value of the first-order derivative of the linearized Lp norm, so as to perform normalization processing on it; the calculation formula of the correction value is:

[0051]

[0052] in, represents the square of the correction value of k iterations, g(m) max(k)p is a parameter related to the maximum value of the first-order derivative of the linearized Lp norm constructed at the kth iteration, and its value is the same as f(m) (k-1) It is determined by finding the zero point of the second-order derivative of the linearized Lp norm. When g(m) is taken max(k)p When f(m) (k-1) Take The calculation formula is:

[0053]

[0054] Among them, f(m) max is the maximum value of f(m).

[0055] According to the Lawson formula, the Lp norm is linearized and the correction value is introduced to construct the adaptive Lp norm objective function; among them, the adaptive Lp norm objective function The expression formula is:

[0056]

[0057] in, represents the data fitting project standard function, represents the model constraint item target function constructed using the adaptive Lp norm, α is the regularization factor, m is the residual density vector, G is the kernel function matrix, d is the gravity anomaly vector, Wd is the data item constraint matrix, Wm is the model item constraint matrix, and Wmkp represents the adaptive Lp norm coefficient matrix after correction at the kth iteration. The expression is:

[0058]

[0059] Among them, w mkpi (m) represents the corrected adaptive Lp norm coefficient used by the i-th segmentation unit, γ kpi is the correction value used for the ith mesh element, m i(k-1) It represents the residual density of the i-th mesh element calculated by the (k-1)th iteration.

[0060] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned adaptive three-dimensional gravity inversion method.

[0061] A third aspect of the present invention provides an adaptive three-dimensional gravity inversion system, comprising the above-mentioned computer-readable storage medium.

[0062] A specific embodiment 1 of the present invention is provided below, and this embodiment 1 comprises the following steps:

[0063] In step S10, the three-dimensional gravity inversion requires the input of gravity anomaly data and the corresponding observation point coordinate data. The gravity anomaly data, observation surface elevation and terrain elevation are collected through a public website, and the data are all input in the grid file (.grd) format; the residual gravity anomaly is extracted and the three-dimensional inversion space below the terrain is determined and regular grid division is performed to obtain the division unit of the inversion space, wherein the horizontal coordinate of the center point of the division unit corresponds to the horizontal coordinate of the grid node of the observation network.

[0064] Step S20: Calculation of Normalized Vertical Derivative Analytic Signal Amplitude: For the residual gravity anomaly extracted in step S10, the Normalized Vertical Derivative Analytic Signal Amplitude (NVDR-ASA) of the residual gravity anomaly is calculated.

[0065] Step S30: Calculation of adaptive p-value variables: For the residual gravity anomaly NVDR-ASA calculated in step S20, an adaptive p-value variable of the Lp norm is calculated according to the NVDR-ASA result.

[0066] In step S30, for the residual gravity anomaly NVDR-ASA calculated in step S20, an adaptive p-value variable is calculated based on the principle that the Lp norm value and the degree of change of the anomaly gradient are in a mirror relationship.

[0067] Step S40: Adaptive Lp norm objective function construction: Based on the adaptive p-value variable obtained in step S30, the Lawson formula is used to linearly represent the Lp norm, and the system deviation generated by the adaptive p-value variable is corrected, so as to construct an adaptive Lp norm objective function.

[0068] In step S40, based on the adaptive p-value variable obtained in step S30, the Lawson formula is further used to linearly represent the Lp norm, and the system deviation caused by the different amplitude variation ranges of the first-order derivative of the constructed linearized Lp norm when taking different p values ​​due to the introduction of the p-value variable is corrected. At this time, a correction value is constructed based on the maximum value of the first-order derivative of the linearized Lp norm, so as to normalize it. Finally, the Lp norm is linearized according to the Lawson formula and the correction value is introduced to construct an adaptive Lp norm objective function.

[0069] Step S50: Iterative reweighted least squares algorithm solution: The adaptive Lp norm objective function constructed in step S40 is introduced, and the adaptive Lp norm objective function is solved by using the iterative reweighted least squares algorithm to obtain the residual density distribution of the inversion space.

[0070] From the above description, it can be seen that the present invention achieves the following technical effects: the present invention selects the p value in the adaptive Lp norm by utilizing the normalized vertical derivative analytical signal amplitude, constructs the Lp norm based on the Lawson formula, and introduces a correction value to eliminate the system deviation caused by the p value variable, and finally constructs an adaptive Lp norm objective function, thereby improving the adaptability of three-dimensional gravity inversion in practical applications, and providing a more reliable inversion basis for deep density structure research, mineral resource exploration, etc.

[0071] In order to better understand and implement the present invention, the following is an example 2 of a specific application scenario of the present invention: a three-dimensional gravity anomaly detection is carried out in a certain area to evaluate the mineral resource potential of the area. The geological structure of the area is relatively complex, and there are multiple target geological bodies with large density variations. In order to accurately characterize the density distribution characteristics of these geological bodies, the adaptive three-dimensional gravity inversion method provided by the present invention is used to perform inversion analysis on the area. The specific implementation process is as follows:

[0072] 1. Data Preparation

[0073] The area is located in a province, and the area range is the coordinate range (ξ min ,η min ),=,(100,km,150,km),(ξ max ,η max ),=,(110,km,160,km). After geophysical exploration, the gravity anomaly data of the area was obtained, and the sampling interval was 100,m. The obtained gravity anomaly data was processed to remove the influence of noise, terrain, crust, etc., and the residual gravity anomaly data was obtained, as shown in Table 1.

[0074] Table 1. Residual gravity anomaly data in the study area

[0075]

[0076]

[0077] 2. Adaptive 3D Gravity Inversion

[0078] According to the above data, the adaptive three-dimensional gravity inversion method provided by the present invention is used to perform inversion analysis, and the specific steps are as follows:

[0079] 1. Constructing the inversion model

[0080] The study area is divided into 100,m,×,100,m,×,100,m,grids, totaling 101,×101,×,15,=,153015,grids. Assume that there are two high-density target geological bodies distributed underground in the study area. One target geological body with a deeper burial depth has a gentle internal density change and a gentle boundary with the surrounding rock. The other target geological body with a shallower burial depth has a rapid density change and a steep boundary. In order to better characterize the density distribution characteristics of these two geological bodies, an adaptive Lp norm inversion method is needed.

[0081] 2. Calculate the Normalized Vertical Derivative Analytical Signal Amplitude (NVDR-ASA)

[0082] According to the obtained residual gravity anomaly data, the normalized vertical derivative analytical signal amplitude (NVDR-ASA) value at each subdivision unit is calculated, as shown in Table 2.

[0083] Table 2, NVDR-ASA values ​​in the study area

[0084]

[0085]

[0086] 3. Calculate the adaptive p-value

[0087] According to the NVDR-ASA value, the adaptive p value of each subdivision unit is calculated, where α = 1.5. The calculation results are shown in Table 3.

[0088] Table 3. Adaptive p-values ​​in the study area

[0089] Horizontal position p-value (100,150) 1.42 (100,151) 1.37 (100,152) 0.98 (100,153) 0.90 (100,154) 1.00 (100,155) 1.09 (100,156) 1.20 (100,157) 1.15 (100,158) 1.36 (100,159) 1.33 (100,160) 1.29 ... ...

[0090] 4. Constructing an adaptive Lp norm objective function

[0091] The Lawson formula is used to linearize the Lp norm, and the correction value is introduced to construct the adaptive Lp norm objective function.

[0092]

[0093] in, represents the data fitting project standard function, represents the model constraint item target function constructed using the adaptive Lp norm, α is the regularization factor, m is the residual density vector, G is the kernel function matrix, d is the gravity anomaly vector, Wd is the data item constraint matrix, Wm is the model item constraint matrix, and Wmkp is the adaptive Lp norm coefficient matrix after correction at the kth iteration:

[0094]

[0095] Among them, γ kpi is the correction value used for the ith mesh element, m i(k-1) It represents the residual density of the i-th mesh element calculated by the (k-1)th iteration.

[0096] 5. Solve the adaptive Lp norm objective function

[0097] The adaptive Lp norm objective function is solved by iterative reweighted least squares algorithm. Solve and obtain the residual density distribution in the inversion space.

[0098] 3. Analysis of inversion results

[0099] The inversion results obtained by the adaptive three-dimensional gravity inversion method are shown in Table 4. As can be seen from the table, there are two high-density target geological bodies in the area, one located in the deep part (Z = 800-1200 m) and the other located in the shallow part (Z = 200-600 m). The deep geological body has a larger range and a slower density change, with a maximum density of about The shallow geological body is smaller in scope and has a more rapid density change, with a maximum density of approximately This is consistent with the geological structure characteristics of the area and provides an important basis for subsequent mineral resource exploration.

[0100] Table 4. Inversion results of the study area

[0101]

[0102] In summary, the adaptive three-dimensional gravity inversion method provided by the present invention can effectively characterize the target geological body with different density change characteristics in the complex geological structure area, providing an important basis for further mineral resource exploration. The method has strong adaptability and stability, and can obtain more accurate inversion results in complex geological environments, providing new technical support for related geophysical exploration.

[0103] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An adaptive three-dimensional gravity inversion method, characterized in that: The following steps are involved: S10, extracting the residual gravity anomaly according to the gravity anomaly of the inversion target area, and performing grid division on the inversion space to construct a model; S20, using the residual gravity anomaly in the inversion target area, calculating the normalized vertical derivative analytical signal amplitude value of the residual gravity anomaly in the inversion space, recorded as the NVDR-ASA value; S30, calculating an adaptive p-value variable of the Lp norm according to the amplitude value of the normalized vertical derivative analytical signal; S40, using the Lawson formula to linearly represent the Lp norm, and correcting the system deviation generated by the adaptive p-value variable, thereby constructing an adaptive Lp norm objective function; S50, using an iterative reweighted least squares algorithm to solve the adaptive Lp norm objective function to obtain the residual density distribution in the inversion space.

2. The adaptive three-dimensional gravity inversion method according to claim 1, characterized in that: Calculating the adaptive p-value variable of the Lp norm according to the amplitude value of the normalized vertical derivative analytical signal specifically includes: The adaptive p-value variable is calculated based on the principle that the Lp norm value and the degree of abnormal gradient change are mirror images; the calculation formula of the adaptive p-value variable is: p(ξ,η)=α[1-NVDR-ASA(ξ,η)]; Among them, p(ξ,η) represents the p-value variable of the subdivision unit corresponding to the horizontal position (ξ,η) of the observation surface, NVDR-ASA(ξ,,η,) represents the amplitude of the normalized vertical derivative analytical signal at the position (ξ,,η) of the observation surface, with an amplitude range of [0,1], and α is the parameter for adjusting the value space of p(ξ,η).

3. The adaptive three-dimensional gravity inversion method according to claim 2, characterized in that: α takes a value between (0,2], so that the value space of p(ξ,η) is between [0,2].

4. The adaptive three-dimensional gravity inversion method according to claim 3, characterized in that: The Lawson formula is used to linearize the Lp norm and correct the systematic deviation caused by the adaptive p-value variable to construct the adaptive Lp norm objective function, including: The Lawson formula is used to linearize the Lp norm; the Lawson formula linearization expression of the Lp norm is: in, represents the linearized Lp norm constructed at the kth iteration, f(m) (k) Indicates the value to be solved for the kth iteration, f(m) (k-1) represents the (k-1)th iteration value, and ε is the adjustment factor used to prevent the occurrence of singularities.

5. The adaptive three-dimensional gravity inversion method according to claim 4, characterized in that: ε takes a value between [1E-10,,1E-2].

6. The adaptive three-dimensional gravity inversion method according to claim 5, characterized in that: Correct the system deviation caused by the adaptive p-value variable. Specifically, correct the system deviation caused by the different amplitude variation ranges of the first-order derivative of the constructed linearized Lp norm when taking different p values ​​due to the introduction of the p-value variable. At this time, a correction value is constructed based on the maximum value of the first-order derivative of the linearized Lp norm, so as to perform normalization processing on it; wherein, the calculation formula of the correction value is: in, represents the square of the correction value of k iterations, g(m) max(k)p is a parameter related to the maximum value of the first-order derivative of the linearized Lp norm constructed at the kth iteration, and its value is the same as f(m) (k-1) It is determined by finding the zero point of the second-order derivative of the linearized Lp norm.

7. The adaptive three-dimensional gravity inversion method according to claim 6, characterized in that: When g(m) is taken max(k)p When the value is (k-1) Take The calculation formula is: Among them, f(m) max is the maximum value of f(m).

8. The adaptive three-dimensional gravity inversion method according to claim 7, characterized in that: Adaptive Lp norm objective function The expression formula is: in, represents the data fitting project standard function, represents the model constraint item target function constructed using the adaptive Lp norm, α is the regularization factor, m is the residual density vector, G is the kernel function matrix, d is the gravity anomaly vector, Wd is the data item constraint matrix, Wm is the model item constraint matrix, and Wmkp represents the adaptive Lp norm coefficient matrix after correction at the kth iteration. The expression is: Among them, w mkpi (m) represents the corrected adaptive Lp norm coefficient used by the i-th segmentation unit, γ kpi is the correction value used for the ith mesh element, m i(k-1) It represents the residual density of the i-th mesh element calculated by the (k-1)th iteration.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the adaptive three-dimensional gravity inversion method according to any one of claims 1 to 8.

10. An adaptive three-dimensional gravity inversion system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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