A method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence

Abstract

The application discloses a kind of based on spatial intelligence's active fault three-dimensional model automatic construction method, belong to geological exploration and earthquake engineering technical field;Method includes: collection data, analysis and extract minimum complete subdirectory;Through adaptive threshold hierarchical clustering combined with improved RANSAC algorithm, the exclusive small earthquake cluster of each fault is automatically identified;Based on local weighted regression, three-dimensional automatic slice is made to small earthquake cluster, and each profile fault interpretation line is fitted by moving least square method;Finally, active fault three-dimensional fine model is constructed;Test model rationality and output model file;The application realizes the automation, quantification of fault modeling whole process, can be fused multi-source data and realize multi-element constraint modeling, adapt complex fault zone modeling, and the fine three-dimensional model constructed can provide key data support for fault present-day deformation inversion, three-dimensional potential source research, earthquake geological disaster assessment, with higher engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration and earthquake engineering technology, and in particular relates to an automatic construction method for three-dimensional models of active faults based on spatial intelligence. Background Technology

[0002] Following a major earthquake, numerous minor earthquakes often occur along the fault plane and in its vicinity. The spatial distribution characteristics of these clusters of minor earthquakes can accurately characterize the three-dimensional geometry of the active fault. Constructing a three-dimensional model of an active fault using multi-source data such as fault surface traces, precise earthquake location, and focal mechanism solutions has become an important tool for constraining the structure of the deep and shallow parts of the fault. The key to constructing the three-dimensional structure of an active fault based on an earthquake catalog lies in accurately extracting clusters of minor earthquakes closely related to the target fault from a vast amount of seismic events and quantitatively inverting information such as the fault's attitude, dip, and dip angle. Traditional methods mainly rely on manual qualitative identification of minor earthquake clusters and visual interpretation of fault attitude, resulting in a highly subjective, unrepeatable, and inefficient model construction process.

[0003] Therefore, there is an urgent need for an automated, quantitative, and complex-structure-adaptable method for 3D modeling of active faults to improve the objectivity, efficiency, and accuracy of modeling, and to achieve standardized and intelligent construction from seismic data to 3D fault models. Summary of the Invention

[0004] The purpose of this invention is to provide an automatic method for constructing 3D models of active faults based on spatial intelligence. This addresses the problems in existing technologies where the construction of 3D models of active faults relies excessively on manual qualitative identification of small earthquake clusters and visual interpretation of fault attitude and structure. This leads to strong subjectivity, poor repeatability, and low overall efficiency in the modeling process. Furthermore, traditional methods lack unified quantitative screening and judgment standards, easily causing distortion of basic modeling data and making it impossible to compare results across studies. This invention introduces artificial intelligence technology. First, it uses adaptive threshold hierarchical clustering to automatically identify the overall trend of small earthquake clusters. Then, it designs an improved RANSAC (Random Sample Consensus) algorithm to screen local inliers, automatically identifying small earthquake clusters for each fault based on inlier cluster labels. Subsequently, based on high-density 3D automatic slicing technology, it uses moving least squares to fit fault interpretation lines. Further, it integrates fault points on the fault surface traces and generates a multi-constrained 3D fault structure through spatial interpolation. Finally, it performs quantitative quality control based on the distance distribution from small earthquake clusters to the target fault, thereby generating a refined 3D fault model.

[0005] To achieve the above objectives, this invention provides a method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence, comprising the following steps: S1. Collect data and organize and analyze the collected data; S2. Based on the data analyzed in S1, identify fault-related small earthquake clusters using adaptive threshold hierarchical clustering combined with the improved RANSAC algorithm. S3. Based on the small earthquake clusters obtained from S2, local weighted regression is used in combination with custom slice step size and overlap to perform three-dimensional high-density automatic slicing and to fit the fault interpretation line based on the moving least squares method. S4. Based on the fault interpretation lines obtained in S3, and combined with surface rupture data points, construct a three-dimensional fine model of the active fault. S5. Optimize the active fault 3D model constructed in S4 and output the fault 3D model file.

[0006] Preferably, the specific content of S1 is as follows: S101. Collect fault surface traces, focal mechanism solutions, and earthquake precise location data in the epicenter area. ,in, , This represents the total number of earthquakes. S102. Based on precise earthquake location data, analyze the minimum complete magnitude of the original earthquake catalog using the maximum curvature method, and extract the minimum complete sub-catalog. The expression is as follows: ; S103. Based on the spatial nearest neighbor index method, analyze whether the minimum complete subdirectory has the clustering property of a fault three-dimensional structure.

[0007] Preferably, the specific content of S2 is as follows: S201. Based on the minimum complete sub-directory obtained in S1, the overall trend of all small seismic clusters is identified by adaptive threshold hierarchical clustering, and the label of each small seismic cluster is obtained. S202. Based on the overall trend results of all small earthquake clusters obtained in S201, set the corresponding RANSAC iteration number N and the threshold for the distance between small earthquakes and faults. , RANSAC interior points and the remaining number of minor earthquakes ; S203. Construct a KD-Tree by randomly selecting three non-collinear points from the preprocessed minimal complete subdirectory S. Define the initial plane as follows: ; S204. Calculate the initial plane normal vector of the fault. and intercept And determine the plane equation; S205. Use KD-Tree to find candidate points near the sampling points and calculate the distance from the candidate points to the plane. Filter out those with a distance less than the threshold The point is expressed as follows: ; like Then point Marked as a local interior point; S206. Repeat S201-S205 above and iterate N times. Select the plane with the most interior points as the initial fault plane and output the corresponding set of interior points. S207. Based on the cluster labels of the in-points of the RANSAC model, search and merge all small earthquake events with corresponding cluster labels in the adaptive threshold hierarchical clustering results, and automatically identify the small earthquake clusters of each fault; combine the fault surface traces and focal mechanism information to determine the number of faults and identify the small earthquake clusters of each fault.

[0008] Preferably, S3 specifically includes: Based on the small earthquake clusters obtained from S2, high-density three-dimensional automatic slicing is performed by combining local weighted regression with a custom slice step size and overlap. S302. Robust fitting of fault interpretation lines based on moving least squares method.

[0009] Preferably, the specific content of S301 is as follows: S301. Based on the fault surface traces and focal mechanism solutions in the study area, the activity nature of each fault is determined, and the fault point data of each fault surface trace are obtained. By projecting the small seismic points onto the corresponding cross-sections, the projection point sets of each cross-section are obtained. The expression is as follows: ; In the formula, , For the first The number of projection points of each section; Preferably, the specific content of S302 is as follows: S302. For the projection point set of each two-dimensional profile, the fault interpretation line is fitted using the moving least squares method. For the first The set of projection points of each cross section An approximate function for fault interpretation lines is defined using first-order polynomial basis functions. The expression is as follows: ; In the formula, A vector of basis functions of a first-order polynomial; For coefficient vectors; Using Gaussian weights as local weights, construct a weighted residual sum of squares objective function. The expression is as follows: ; In the formula, It is a Gaussian weight function; For window width; Differentiating the target weighted residual sum of squares standard function and setting the derivative to 0 yields the coefficient vector. The expression is as follows: ; in: ; ; ; ; ; In the formula, The normal matrix; The load matrix; These are basis functions for a first-order polynomial; It is a diagonal weight matrix; The ordinate vector of the sample points; This is the symbol for a diagonal matrix; The closed-form solution of the coefficient vector Substituting the approximation function, we obtain the fault interpretation line of the k-th profile. Complete the interpretation line fitting for all profiles; Preferably, S4 specifically includes: S401. Construct the initial three-dimensional structure of the fault; S402. Based on fault interpretation lines and combined with surface rupture data points, construct a three-dimensional fine model of active faults.

[0010] Preferably, the specific content of S401 is as follows: Based on the fault points and fault interpretation lines obtained from S3, a three-dimensional initial structure of the fault is constructed using spatial interpolation. Calculate the distance from the cluster of minor earthquakes to the initial structure of the fault for each fault. ; Based on the Gaussian kernel function and using the kernel density estimation method, its spatial distribution characteristics are analyzed. The expression is as follows: ; In the formula, Let be the Gaussian kernel function, where It is an exponential function; Based on the characteristics of kernel density distribution, the parameters of the initial plane of the fault and the fitting parameters of the fault interpretation line are adjusted to make the density distribution of faults with small displacement approximately symmetrical.

[0011] Preferably, the specific content of S402 is as follows: For fault zones containing branch faults, S3 is performed on each fault separately; by combining seismic data and surface rupture data points, the cascade relationship of each fault is comprehensively analyzed and determined; the strike, dip, and dip angle of each fault are calculated to construct a three-dimensional fine model of active faults.

[0012] Preferably, the specific content of S5 is as follows: The output fault 3D model file includes the 3D model of each fault and a table of attitude parameters.

[0013] Therefore, the present invention employs the above-mentioned method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence, which has the following beneficial effects: (1) Based on artificial intelligence technologies such as adaptive threshold hierarchical clustering, improved RANSAC algorithm and moving least squares method, we can realize the quantitative automatic identification of small earthquake clusters, the automatic fitting of fault interpretation lines, and the quantitative construction of three-dimensional fault structure. This replaces the traditional manual qualitative judgment and visual interpretation methods, eliminates the result bias caused by human experience, and makes the modeling results consistent. It can realize effective comparison across regions and across studies. (2) A brand-new fully automated modeling system is formed, which can efficiently integrate multi-source data such as fault surface traces, earthquake precise location, and focal mechanism solution to realize multi-constraint modeling, accurately infer the strike, dip, dip angle and other attitude information and three-dimensional geometric morphology of the strata, solve the problems of insufficient quantitativeity and low accuracy of traditional modeling, and meet the needs of high-precision geological analysis; (3) It is not only suitable for three-dimensional modeling of a single active fault, but also can efficiently handle complex fault zone systems with multiple intersecting faults and branch / conjugate hierarchical relationships. By finely modeling each fault separately and then integrating the spatial relationships, it avoids the loss of branch fault details caused by overall modeling and restores the real geological structure of the fault zone. (4) The modeling cycle is greatly shortened and the overall work efficiency is improved by automating the process through algorithms, replacing the manual intervention in each step of the traditional process. At the same time, a standardized modeling method is formed, which reduces the technical operation threshold for three-dimensional modeling of active faults. (5) The constructed three-dimensional fine attribute model of active faults provides accurate and reliable core data support for fault deformation and stress inversion, three-dimensional potential seismic source research, and earthquake geological hazard assessment. It also provides an efficient and practical new technology approach for the study of active fault geometry and the prevention and control of earthquake geological hazard risks.

[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1This is a flowchart of a method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence, according to the present invention. Figure 2 The flowchart for extracting the minimum complete subdirectory and determining its validity is as follows; Figure 3 A diagram illustrating the automatic identification algorithm for fault-based minor earthquake clusters; Figure 4 This is a schematic diagram of the adaptive threshold hierarchical clustering results based on the small earthquake catalog; Figure 5 A schematic diagram of a cluster of small earthquakes identified based on artificial intelligence algorithms; Figure 6 Flowchart for constructing a 3D model of an active fault. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0017] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0018] The following is combined Figures 1-6 The embodiments of the present invention will be described in detail below.

[0019] Example An automatic 3D model construction method for active faults based on spatial intelligence firstly performs cluster analysis on the small earthquake cluster catalog using adaptive threshold hierarchical clustering. Secondly, based on the overall trend of small earthquake clusters identified by hierarchical clustering, an improved RANSAC algorithm is designed. This algorithm filters out earthquake events with distances less than the fault plane by setting a distance threshold, and then searches and merges all small earthquake events with corresponding cluster labels in the clustering results based on the cluster labels of these earthquake events, thereby automatically identifying the small earthquake clusters of each fault. Next, a 3D fault plane is fitted. For each fault's small earthquake cluster, high-density 3D automatic slicing is used to obtain the earthquake events of each profile. Moving least squares is used to fit the fault interpretation line, and finally, spatial interpolation is used to construct the 3D fault plane. RANSAC (Random Sample Consensus) is a robust model fitting method that estimates mathematical model parameters iteratively from an observation dataset containing "inliers" (data points that conform to a certain mathematical model) and "outliers" (outliers that do not fit the model).

[0020] The method of this invention forms a completely new data-driven workflow, which is not only applicable to the 3D modeling of a single active fault, but also can efficiently handle multiple intersecting fault systems. It can significantly reduce the influence of human subjectivity and improve the objectivity, consistency and repeatability of modeling. It provides an efficient and reliable new technical approach for the study of active fault geometry, current deformation and stress inversion, 3D potential seismic source analysis and earthquake geological hazard risk assessment. The specific steps are as follows: S1. Collect data and organize and analyze the collected data; S101. Collect fault surface traces, focal mechanism solutions, and earthquake precise location data in the epicenter area. ,in, , This represents the total number of earthquakes. S102. Based on precise earthquake location data, analyze the minimum complete magnitude of the original earthquake catalog using the maximum curvature method, and extract the minimum complete sub-catalog. The expression is as follows: ; S103. Based on the spatial nearest neighbor index method, analyze whether the minimum complete subdirectory has the clustering property of a fault three-dimensional structure.

[0021] S2. Based on the data analyzed in S1, identify fault-related small earthquake clusters using adaptive threshold hierarchical clustering combined with the improved RANSAC algorithm. S201. Based on the minimum complete sub-directory obtained in S1, the overall trend of all small seismic clusters is identified by adaptive threshold hierarchical clustering, and the label of each small seismic cluster is obtained. S202. Based on the overall trend results of all small earthquake clusters obtained in S201, set the corresponding RANSAC iteration number N and the threshold for the distance between small earthquakes and faults. , RANSAC interior points and the remaining number of minor earthquakes ; S203. Construct a KD-Tree by randomly selecting three non-collinear points from the preprocessed minimal complete subdirectory S. Define the initial plane as follows: ; S204. Calculate the initial plane normal vector of the fault. and intercept And determine the plane equation; S205. Use KD-Tree to find candidate points near the sampling points and calculate the distance from the candidate points to the plane. Filter out those with a distance less than the threshold The point is expressed as follows: ; like Then point Marked as a local interior point; S206. Repeat S201-S205 above and iterate N times. Select the plane with the most interior points as the initial fault plane and output the corresponding set of interior points. S207. Based on the cluster labels of the in-points of the RANSAC model, search and merge all small earthquake events with corresponding cluster labels in the adaptive threshold hierarchical clustering results, and automatically identify the small earthquake clusters of each fault; combine the fault surface traces and focal mechanism information to determine the number of faults and identify the small earthquake clusters of each fault.

[0022] S3. Based on the small earthquake clusters obtained from S2, local weighted regression is used in combination with custom slice step size and overlap to perform three-dimensional high-density automatic slicing and to fit the fault interpretation line based on the moving least squares method. S301. Based on the small earthquake clusters obtained from S2, high-density three-dimensional automatic slicing is performed using local weighted regression combined with a custom slice step size and overlap. S301. Based on the fault surface traces and focal mechanism solutions in the study area, the activity nature of each fault is determined, and the fault point data of each fault surface trace are obtained. By projecting the small seismic points onto the corresponding cross-sections, the projection point sets of each cross-section are obtained. The expression is as follows: ; In the formula, , For the first The number of projection points of each section; S302. Robust fitting of fault interpretation lines based on moving least squares method; S302. For the projection point set of each two-dimensional profile, the fault interpretation line is fitted using the moving least squares method. For the first The set of projection points of each cross section An approximate function for fault interpretation lines is defined using first-order polynomial basis functions. The expression is as follows: ; In the formula, A vector of basis functions of a first-order polynomial; For coefficient vectors; Using Gaussian weights as local weights, construct a weighted residual sum of squares objective function. The expression is as follows: ; In the formula, It is a Gaussian weight function; For window width; Differentiating the target weighted residual sum of squares standard function and setting the derivative to 0 yields the coefficient vector. The expression is as follows: ; in: ; ; ; ; ; In the formula, The normal matrix; The load matrix; These are basis functions for a first-order polynomial; It is a diagonal weight matrix; The ordinate vector of the sample points; This is the symbol for a diagonal matrix; The closed-form solution of the coefficient vector Substituting the approximation function, we obtain the fault interpretation line of the k-th profile. Complete the interpretation line fitting for all profiles; S4. Based on the fault interpretation lines obtained in S3, and combined with surface rupture data points, construct a three-dimensional fine model of the active fault. S401. Construct the initial three-dimensional structure of the fault; Based on the fault points and fault interpretation lines obtained from S3, a three-dimensional initial structure of the fault is constructed using spatial interpolation. Calculate the distance from the cluster of minor earthquakes to the initial structure of the fault for each fault. ; Based on the Gaussian kernel function and using the kernel density estimation method, its spatial distribution characteristics are analyzed. The expression is as follows: ; In the formula, Let be the Gaussian kernel function, where It is an exponential function; Based on the characteristics of kernel density distribution, the parameters of the initial plane of the fault and the fitting parameters of the fault interpretation line are adjusted to make the density distribution of faults with small displacement approximately symmetrical.

[0023] S402. Based on fault interpretation lines and combined with surface rupture data points, construct a three-dimensional fine model of active faults.

[0024] For fault zones containing branch faults, S3 is performed on each fault separately; by combining seismic data and surface rupture data points, the cascade relationship of each fault is comprehensively analyzed and determined; the strike, dip, and dip angle of each fault are calculated to construct a three-dimensional fine model of active faults.

[0025] S5. Optimize the active fault 3D model constructed in S4 and output the fault 3D model file.

[0026] The output fault 3D model file includes the 3D model of each fault (i.e., the fault plane) and the attitude parameter table.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence, characterized in that, Includes the following steps: S1. Collect data and organize and analyze the collected data; S2. Based on the data analyzed in S1, identify fault-related small earthquake clusters using adaptive threshold hierarchical clustering combined with the improved RANSAC algorithm. S3. Based on the small earthquake clusters obtained from S2, local weighted regression is used in combination with a custom slice step size and overlap to perform three-dimensional high-density automatic slicing and to fit the fault interpretation line based on the moving least squares method. S4. Based on the fault interpretation lines obtained in S3, and combined with surface rupture data points, construct a three-dimensional fine model of the active fault. S5. Optimize the active fault 3D model constructed in S4 and output the fault 3D model file.

2. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 1, characterized in that, The specific content of S1 is as follows: S101. Collect fault surface traces, focal mechanism solutions, and earthquake precise location data in the epicenter area. ,in, , This represents the total number of earthquakes. S102. Based on precise earthquake location data, analyze the minimum complete magnitude of the original earthquake catalog using the maximum curvature method, and extract the minimum complete sub-catalog. The expression is as follows: ； S103. Based on the spatial nearest neighbor index method, analyze whether the minimum complete subdirectory has the clustering property of a fault three-dimensional structure.

3. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 2, characterized in that, The specific details of S2 are as follows: S201. Based on the minimum complete sub-directory obtained in S1, the overall trend of all small seismic clusters is identified by adaptive threshold hierarchical clustering, and the label of each small seismic cluster is obtained. S202. Based on the overall trend results of all small earthquake clusters obtained in S201, set the corresponding RANSAC iteration number N and the threshold for the distance between small earthquakes and faults. , RANSAC interior points and the remaining number of minor earthquakes ; S203. Construct a KD-Tree by randomly selecting three non-collinear points from the preprocessed minimal complete subdirectory S. Define the initial plane as follows: ； S204. Calculate the initial plane normal vector of the fault. and intercept And determine the plane equation; S205. Use KD-Tree to find candidate points near the sampling points and calculate the distance from the candidate points to the plane. Filter out those with a distance less than the threshold The point is expressed as follows: ； like Then point Marked as a local interior point; S206. Repeat S201-S205 above and iterate N times. Select the plane with the most interior points as the initial fault plane and output the corresponding set of interior points. S207. Based on the cluster labels of the in-points of the RANSAC model, search and merge all small earthquake events with corresponding cluster labels in the adaptive threshold hierarchical clustering results, and automatically identify the small earthquake clusters of each fault; combine the fault surface traces and focal mechanism information to determine the number of faults and identify the small earthquake clusters of each fault.

4. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 3, characterized in that, S3 specifically includes: S301. Based on the small earthquake clusters obtained from S2, high-density three-dimensional automatic slicing is performed using local weighted regression combined with a custom slice step size and overlap. S302. Robust fitting of fault interpretation lines based on moving least squares method.

5. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 4, characterized in that, The specific details of S301 are as follows: S301. Based on the fault surface traces and focal mechanism solutions in the study area, the activity nature of each fault is determined, and the fault point data of each fault surface trace are obtained. By projecting the small seismic points onto the corresponding cross-sections, the projection point sets of each cross-section are obtained. The expression is as follows: ； In the formula, , For the first The number of projection points of each section.

6. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 5, characterized in that, The specific details of S302 are as follows: S302. For the projection point set of each two-dimensional profile, the fault interpretation line is fitted using the moving least squares method. For the first The set of projection points of each cross section An approximate function for fault interpretation lines is defined using first-order polynomial basis functions. The expression is as follows: ； In the formula, A vector of basis functions of a first-order polynomial; For coefficient vectors; Using Gaussian weights as local weights, construct a weighted residual sum of squares objective function. The expression is as follows: ； In the formula, It is a Gaussian weight function; For window width; Differentiating the target weighted residual sum of squares standard function and setting the derivative to 0 yields the coefficient vector. The expression is as follows: ； in: ； ； ； ； ； In the formula, The normal matrix; The load matrix; These are basis functions for a first-order polynomial; It is a diagonal weight matrix; The ordinate vector of the sample points; This is the symbol for a diagonal matrix; The closed-form solution of the coefficient vector Substituting the approximation function, we obtain the fault interpretation line of the k-th profile. Complete the interpretation line fitting for all profiles.

7. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 6, characterized in that, S4 specifically includes: S401. Construct the initial three-dimensional structure of the fault; S402. Based on fault interpretation lines and combined with surface rupture data points, construct a three-dimensional fine model of active faults.

8. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 7, characterized in that, The specific details of S401 are as follows: Based on the fault points and fault interpretation lines obtained from S3, a three-dimensional initial structure of the fault is constructed using spatial interpolation. Calculate the distance from the cluster of minor earthquakes to the initial structure of the fault for each fault. ; Based on the Gaussian kernel function and using the kernel density estimation method, its spatial distribution characteristics are analyzed. The expression is as follows: ； In the formula, Let be the Gaussian kernel function, where It is an exponential function; Based on the characteristics of kernel density distribution, the parameters of the initial plane of the fault and the fitting parameters of the fault interpretation line are adjusted to make the density distribution of faults with small displacement approximately symmetrical.

9. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 8, characterized in that, The specific details of S402 are as follows: For fault zones containing branch faults, S3 is performed on each fault separately; by combining seismic data and surface rupture data points, the cascade relationship of each fault is comprehensively analyzed and determined; the strike, dip, and dip angle of each fault are calculated to construct a three-dimensional fine model of active faults.

10. The method for automatically constructing a three-dimensional model of an active fault based on spatial intelligence according to claim 9, characterized in that, The specific details of S5 are as follows: The output fault 3D model file includes the 3D model of each fault and a table of attitude parameters.

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