Geological type identification method

Abstract

The invention discloses a geological type identification method, which comprises the following steps: S1, loading and processing data: loading point data and OBJ-format grid data, converting the point data into a Point object, and converting the OBJ data into a grid; s2, constructing an intelligent octree: constructing an intelligent octree index according to the grid data of the fault surface, and adaptively refining the space by the intelligent octree to provide a spatial index for subsequent distance calculation; s3, distance calculation: for each point in the point cloud, using an intelligent octree to quickly position a region most likely to contain a nearest fault surface point, and in the region, calculating the shortest distance from the point to the fault surface; s4, result storage: storing the shortest distance from each point to the fault surface in attributes of the point cloud data; and S5, visualization and analysis: visualizing the point cloud data and the calculated distance attributes, and evaluating fault distance distribution through a visualization result so as to identify different geological types.

Description

Technical Field

[0001] The present invention relates to the technical field of geological identification, and particularly relates to a method for identifying geological types. Background Art

[0002] A fault is a tectonic phenomenon in the earth's crust where rock layers or rock masses undergo large displacements along a fracture surface, which is an important manifestation of crustal movement. The existence and characteristics of faults are of great significance for geological structure analysis, oil and gas resource exploration, etc. The fault distance attribute, namely the fault throw, is a key parameter in fault research. It reflects the relative displacement amount of rock layers or rock masses on both sides of the fault along the fault surface, and plays an important role in understanding the nature of the fault, its activity history, and the impact of the fault on oil and gas resources.

[0003] When calculating the fault distance attribute in the prior art, it is usually necessary to traverse all the points on the fault surface, resulting in low calculation efficiency. When dealing with large-scale data sets, it cannot run efficiently due to excessive memory consumption. Therefore, a method for identifying geological types is proposed. Summary of the Invention

[0004] The purpose of the present invention is to solve the disadvantages existing in the prior art, that is, when calculating the fault distance attribute in the prior art, it is usually necessary to traverse all the points on the fault surface, resulting in low calculation efficiency. When dealing with large-scale data sets, it cannot run efficiently due to excessive memory consumption, and a method for identifying geological types is proposed.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions: A method for identifying geological types includes the following steps: S1: Loading and processing data: Loading point data and mesh data in OBJ format, converting the point data into a Points object, and converting the OBJ data into a mesh; S2: Constructing an intelligent octree: Constructing an intelligent octree index according to the mesh data on the fault surface. The intelligent octree adaptively refines the space to provide a spatial index for subsequent distance calculations; S3: Distance calculation: For each point in the point cloud, use the intelligent octree to quickly locate the area most likely to contain the nearest point on the fault surface. In this area, calculate the shortest distance from the point to the fault surface, including the distance calculations from the point to the surface, from the point to the edge, and from the point to the vertex; S4: Result storage: Store the shortest distance from each point to the fault surface in the attributes of the point cloud data; S5: Visualization and analysis: Visualize the point cloud data and the calculated distance attribute, evaluate the fault distance distribution through the visualization results, and combine geological knowledge and experience to analyze and interpret the visualization results, so as to identify different geological types; S6: Export data: Export the points with the shortest distance attribute to a file in CSV or other format.

[0006] The above further includes: Further, in S1, the loading and processing of data provide an accurate and uniformly formatted data basis for subsequent geological type identification. By loading point data and mesh data in OBJ format, the original data obtained from geological exploration and surveying is acquired. The original data contains the spatial position and morphological information of geological bodies and is an important basis for identifying geological types.

[0007] Further, the specific steps of the loading and processing of data are as follows: Data loading: Read point data and mesh data in OBJ format from the data sources of geological exploration and surveying, verify the integrity and accuracy of the data, and ensure that the data is not lost or incorrect; Data conversion: Use professional data processing software or libraries (such as PCL, Open3D, etc.) to convert the point data into a Points object and convert the mesh data in OBJ format into a mesh data structure recognizable by the computer; Data preprocessing: Perform preprocessing operations such as denoising and smoothing on the converted data to improve the data quality. By performing coordinate transformation and scaling on the data, the consistency of the data is ensured.

[0008] Further, in S2, the specific steps for constructing an intelligent octree are as follows: Input mesh data: Input the mesh data of the fault surface. The mesh data includes information such as the coordinates of vertices and the definition of faces; Initialize the octree: Initialize an empty octree structure according to the input mesh data. The empty octree structure will be used to store the spatial index of the mesh data; Recursive partitioning: Starting from the root node, recursively partition the octree. In each step of the partitioning, determine whether the spatial region represented by the current node contains mesh data. If it does, according to the complexity and distribution of the data, further divide the node into eight child nodes. In regions with complex or rapidly changing geometries, the space will be subdivided into smaller octree nodes; while in regions with simple geometries, the space partitioning will be relatively coarser; Store the index: During the partitioning process, store the index information of the mesh data in the corresponding octree nodes. The index information will be used for subsequent distance calculation and query operations; Optimization and adjustment: Perform optimization and adjustment on the constructed octree, including operations such as adjusting the depth of nodes and merging adjacent nodes, such as the minimum and maximum boundaries within each node, so as to quickly determine whether a point is within a specific region, in order to improve the query efficiency and accuracy of the octree.

[0009] Furthermore, in S3, the distance calculation includes two stages: rapid positioning and shortest distance calculation. The specific steps of the rapid positioning are as follows: For each point in the point cloud, the Smart Octrees quickly locates the region most likely to contain the nearest fault surface points through its adaptive spatial indexing structure; Starting from the root node of the octree, traverse down level by level until the smallest node containing the target point is found; Smart Octrees utilizes the hierarchy of spatial indexing to quickly exclude those regions that are far from the target point, thereby reducing the search scope.

[0010] Furthermore, the specific steps of the shortest distance calculation are as follows; Once the search region containing the nearest fault surface points is located, which is usually a small cube or a group of adjacent cubes, calculate the distances for all the surface points within this region to determine the search region: Within the search region, calculate the Euclidean distances between the points in the point cloud and the vertices, edges, and faces in the fault surface mesh for all the surface points in the search region Calculate the distance to the point and find the minimum distance , and the minimum distance The calculation formula is: ; where represents the search region containing the nearest fault surface points located by the Smart Octrees, is the surface point within the search region, and are the three-dimensional coordinates of points P and Q respectively.

[0011] Furthermore, in S4, store the calculated shortest distance as attribute information into each point of the point cloud data, and each point contains the shortest distance information to the fault surface.

[0012] Furthermore, in S5, the specific steps of the visualization and analysis are as follows: Import data: Import the point cloud data and distance attributes into the visualization software; Select the visualization method: 3D rendering: Use 3D rendering technology to display the point cloud data and distance attributes in the form of a 3D model, which helps to intuitively understand the 3D shape of the geological structure; Contour map: Draw a contour map according to the values of the distance attributes, and the contour map shows the distribution and change trend of geological types (such as faults, folds, etc.); Cross-section view: Draw a cross-section view in a specific direction to show the shape and characteristics of the geological structure on the cross-section. This cross-section view helps to understand the internal structure of the geological structure; Adjust visualization parameters: Adjust various parameters such as color, transparency, and lighting to enhance the visualization effect; Observe the visualization results: Observe the 3D rendering, contour map, and cross-section view, and pay attention to the shape, distribution, and change trend of the geological structure; Identify geological types: Based on geological knowledge and experience, identify different geological types such as faults, folds, and igneous rocks. Use formulas or tools in professional software to measure and determine the position, shape, and scale of these geological types. For example, the distance formula can be used to calculate the shortest distance from a point to the fault surface to determine the position and extent of the fault; Evaluate the complexity and stability of the geological structure: Based on the distribution and characteristics of the geological types, conduct quantitative evaluations to assess the complexity and stability of the geological structure, such as geomechanical analysis and seismic activity assessment.

[0013] The present invention has the following beneficial effects: 1. In the present invention, the intelligent octree quickly locates the area most likely to contain the nearest point, reducing the number of points for which the distance needs to be calculated, thereby significantly improving the calculation efficiency. At the same time, the intelligent octree automatically adjusts the fineness of the spatial division according to the complexity of the data, avoiding unnecessary memory usage and reducing memory consumption.

[0014] 2. In the present invention, the intelligent octree can adaptively refine the space to adapt to the complexity of the data, maintaining a high resolution in complex areas and a coarser division in simple areas, thereby improving performance while ensuring accuracy.

[0015] 3. In the present invention, storing the shortest distance from each point to the fault surface in the attributes of the point cloud data makes subsequent analysis and visualization easier and more intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a step diagram of a method for identifying geological types proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Please refer toFigure 1 As shown, the present invention is a method for identifying geological types, comprising the following steps: S1: Loading and processing data: Loading point data and mesh data in OBJ format, converting the point data into Points objects, and converting the OBJ data into a mesh; S2: Constructing an intelligent octree: Constructing an intelligent octree index based on the mesh data of the fault surface. The intelligent octree adaptively refines the space to provide a spatial index for subsequent distance calculations; S3: Distance calculation: For each point in the point cloud, use the intelligent octree to quickly locate the region most likely to contain the nearest fault surface points. Within this region, calculate the shortest distance from the point to the fault surface, including distance calculations from the point to the face, from the point to the edge, and from the point to the vertex; S4: Result storage: Store the shortest distance from each point to the fault surface in the attributes of the point cloud data; S5: Visualization and analysis: Visualize the point cloud data and the calculated distance attributes, evaluate the fault distance distribution through the visualization results, and combine geological knowledge and experience to analyze and interpret the visualization results, thereby identifying different geological types; S6: Exporting data: Export the points storing the shortest distance attributes to a CSV or other format file.

[0019] In one embodiment, for the above S1, in S1, the loading and processing of data provide an accurate and uniformly formatted data basis for subsequent geological type identification. By loading point data and mesh data in OBJ format, the original data obtained from geological exploration and measurement is acquired. The original data contains the spatial position and morphological information of the geological body and is an important basis for identifying geological types.

[0020] In one embodiment, for the above loading and processing of data, the specific steps of the loading and processing of data are as follows: Data loading: Read point data and mesh data in OBJ format from the data sources of geological exploration and measurement, verify the integrity and accuracy of the data, and ensure that the data is not lost or incorrect; Data conversion: Use professional data processing software or libraries (such as PCL, Open3D, etc.) to convert the point data into Points objects and convert the mesh data in OBJ format into a mesh data structure recognizable by the computer; Data preprocessing: Perform preprocessing operations such as denoising and smoothing on the converted data to improve the data quality, and ensure the consistency of the data through coordinate transformation and scaling of the data.

[0021] During the data conversion process, when converting point data into Points objects, it is necessary to calculate the three-dimensional coordinates (x, y, z) of the points and other possible attributes (such as color, normal, etc.). These calculations are usually based on the original data obtained from geological exploration and surveying.

[0022] When converting mesh data in OBJ format into a mesh data structure, it is necessary to parse geometric information such as vertices and faces in the OBJ file and construct the corresponding data structure. The format of the OBJ file is usually as follows:

[0023] v x y z # Vertex coordinates vn i j k # Normal vector vt u v # Texture coordinates f v1 / vt1 / vn1 v2 / vt2 / vn2 v3 / vt3 / vn3 # Face definition, composed of vertices, texture coordinates, and normal vectors When parsing this information, it is necessary to read and parse according to the syntax rules of the OBJ file and construct the corresponding mesh data structure.

[0024] Suppose we have a simple OBJ file with the following content: v 0 0 0 v 1 0 0 v 0 1 0 f 1 2 3 This OBJ file defines a simple triangular mesh. When loading and processing the data, we need to read these vertex coordinates and face definitions and construct a data structure containing this information. Then, we use this data structure for subsequent distance calculations, visualization, and analysis.

[0025] In one embodiment, for S2 above, in S2, the specific steps for constructing an intelligent octree are as follows: Input mesh data: Input the mesh data of the fault surface, and the mesh data includes information such as the coordinates of vertices and the definition of faces; Initialize the octree: According to the input mesh data, initialize an empty octree structure, and the empty octree structure will be used to store the spatial index of the mesh data; Recursive partitioning: Starting from the root node, recursively partition the octree. In each step of partitioning, determine whether the spatial region represented by the current node contains mesh data. If it contains, according to the complexity and distribution of the data, further divide the node into eight child nodes. In regions with complex or rapidly changing geometries, the space will be subdivided into smaller octree nodes; while in regions with simple geometries, the space partitioning will be relatively coarser; Storage Index: During the partitioning process, the index information of the grid data is stored in the corresponding octree nodes, and this index information will be used for subsequent distance calculation and query operations; Optimization and Adjustment: Optimize and adjust the constructed octree, including operations such as adjusting the depth of nodes and merging adjacent nodes, such as the minimum and maximum boundaries within each node, so as to quickly determine whether a point is within a specific region, in order to improve the query efficiency and accuracy of the octree; Suppose we have a simple two-dimensional grid data, which includes four vertices and a rectangular face. The coordinates of these vertices are (0,0), (1,0), (1,1), and (0,1) respectively, and the rectangular face is defined by these four vertices.

[0026] Initialize the Octree: First, we initialize an empty octree structure. This structure will contain a root node, representing the entire spatial region.

[0027] Recursive Partitioning: Then, according to the distribution of the grid data, we recursively partition the octree. In this example, since the grid data is a rectangular face, we can divide it into four sub-regions. Therefore, we divide the root node into four child nodes, and each child node represents a sub-region.

[0028] Storage Index: During the partitioning process, we store the index information of the grid data in the corresponding octree nodes. We store the index information of the rectangular face in a child node of the root node (assuming this child node represents the spatial region containing the rectangular face); For the root node (x0, y0, z0, x1, y1, z1): Child Node 1: (x0, y0, z0, (x0 + x1) / 2, (y0 + y1) / 2, (z0 + z1) / 2) / / Upper left corner Child Node 2: ((x0 + x1) / 2, y0, z0, x1, (y0 + y1) / 2, (z0 + z1) / 2) / / Upper right corner Child Node 3: (x0, (y0 + y1) / 2, z0, (x0 + x1) / 2, y1, (z0 + z1) / 2) / / Lower left corner Child Node 4: ((x0 + x1) / 2, (y0 + y1) / 2, z0, x1, y1, (z0 + z1) / 2) / / Lower right corner And another four child nodes in the z direction can be calculated similarly; Optimization and adjustment: In this example, since the mesh data has only one rectangular face, there is no need to perform excessive optimization and adjustment on the octree. If the mesh data is more complex or the data distribution is uneven, we may need to optimize and adjust the octree.

[0029] In one embodiment, for S3 above, in S3, the distance calculation includes two stages: rapid positioning and shortest distance calculation. The specific steps of the rapid positioning are as follows: For each point in the point cloud, the intelligent octree quickly locates the region most likely to contain the nearest fault surface points through its adaptive spatial indexing structure; Starting from the root node of the octree, traverse down level by level until the smallest node containing the target point is found; Smart Octrees utilizes the hierarchy of spatial indexing to quickly exclude regions that are far from the target point, thereby reducing the search scope.

[0030] In one embodiment, for the shortest distance calculation above, the specific steps of the shortest distance calculation are as follows; Once the search region containing the nearest fault surface points is located, which is usually a small cube or a group of adjacent cubes, calculate the distances to all the points on the surfaces within this region to determine the search region: Within the search region, calculate the Euclidean distances between the points in the point cloud and the vertices, edges, and faces in the fault surface mesh for all the points on the surfaces of the search region Calculate the distance to the point and find the minimum distance The minimum distance The calculation formula is: ; where represents the search region containing the nearest fault surface points located by the intelligent octree, is the point on the surface within the search region, and are the three-dimensional coordinates of points P and Q respectively.

[0031] In one embodiment, for S4 above, in S4, store the calculated shortest distance as attribute information in each point of the point cloud data, and each point contains the shortest distance information to the fault surface.

[0032] In one embodiment, for S5 above, in S5, the specific steps of the visualization and analysis are as follows: Import data: Import the point cloud data and distance attributes into the visualization software; Select visualization method: 3D Rendering: Using 3D rendering technology, the point cloud data and distance attributes are presented in the form of a 3D model, which helps to intuitively understand the 3D morphology of geological structures; Contour Map: According to the values of the distance attributes, a contour map is drawn, and the contour map shows the distribution and change trends of geological types (such as faults, folds, etc.); Cross-Sectional View: A cross-sectional view is drawn in a specific direction to show the morphology and characteristics of the geological structure on the cross-section, and the cross-sectional view helps to understand the internal structure of the geological structure; Adjust Visualization Parameters: Adjust various parameters such as color, transparency, and lighting to enhance the visualization effect; Observe Visualization Results: Observe the 3D rendering, contour map, and cross-sectional view, and pay attention to the morphology, distribution, and change trends of the geological structure; Identify Geological Types: Based on geological knowledge and experience, different geological types such as faults, folds, and igneous rocks are identified. Formulas or tools in professional software are used to measure and determine the location, morphology, and scale of these geological types. For example, the distance formula can be used to calculate the shortest distance from a point to the fault surface to determine the location and extent of the fault; Evaluate the Complexity and Stability of Geological Structures: Based on the distribution and characteristics of geological types, quantitative evaluations are carried out to evaluate the complexity and stability of geological structures, such as geomechanical analysis and seismic activity assessment; Suppose we have a dataset containing point cloud data and distance attributes, which records the geological structure of a certain area. Visualization and analysis are carried out according to the following steps: Select Visualization Software: Select ArcGIS as the visualization software.

[0033] Import Data: Import the point cloud data and distance attributes into ArcGIS.

[0034] Select Visualization Methods: Use the 3D rendering function of ArcGIS to display the point cloud data and distance attributes. At the same time, draw a contour map and a cross-sectional view to show the distribution and characteristics of the geological structure.

[0035] Adjust Visualization Parameters: Adjust parameters such as color and transparency to enhance the visualization effect.

[0036] Observe and Analyze: Carefully observe the 3D rendering results, contour map, and cross-sectional view. Identify geological types such as faults and folds, and use the distance formula to measure and determine their location, morphology, and scale.

[0037] Evaluate Geological Structures: Based on the distribution and characteristics of geological types, evaluate the complexity and stability of the geological structure in this area. Use methods such as geomechanical analysis for quantitative evaluation to provide suggestions for subsequent exploration and development.

[0038] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying geological types, characterized in that, It includes the following steps: S1: Loading and processing data: Load point data and mesh data in OBJ format, convert the point data into a Points object, and convert the OBJ data into a mesh; S2: Constructing an intelligent octree: Construct an intelligent octree index based on the mesh data of the fault surface. The intelligent octree adaptively refines the space to provide a spatial index for subsequent distance calculations; S3: Distance calculation: For each point in the point cloud, use the intelligent octree to quickly locate the area most likely to contain the nearest fault surface point. Within this area, calculate the shortest distance from the point to the fault surface, including distance calculations from the point to the face, from the point to the edge, and from the point to the vertex; S4: Result storage: Store the shortest distance from each point to the fault surface in the attributes of the point cloud data; S5: Visualization and analysis: Visualize the point cloud data and the calculated distance attributes, evaluate the fault distance distribution through the visualization results, and analyze and interpret the visualization results in combination with geological knowledge and experience to identify different geological types; S6: Exporting data: Export the points with the shortest distance attributes stored to a CSV or other format file.

2. The geological type identification method according to claim 1, characterized in that In S1, the loading and processing of data provides a data basis with a unified format for subsequent geological type identification. By loading point data and mesh data in OBJ format, the original data obtained from geological exploration and measurement is acquired. The original data includes the spatial position and morphological information of the geological body.

3. The geological type identification method according to claim 1, characterized in that In S1, the specific steps of the loading and processing of data are as follows: Data loading: Read point data and mesh data in OBJ format from the data sources of geological exploration and measurement, and verify the integrity and accuracy of the data; Data conversion: Convert the point data into a Points object and convert the mesh data in OBJ format into a mesh data structure; Data preprocessing: Perform processing operations on the converted data. By performing coordinate transformation and scaling on the data, the consistency of the data is ensured.

4. A method for identifying a geological type according to claim 1, characterized in that, In S2, the specific steps for constructing an intelligent octree are: Input mesh data: Input the mesh data of the fault surface; Initialize the octree: Initialize an empty octree structure according to the input mesh data. The empty octree structure will be used to store the spatial index of the mesh data; Recursive partitioning: Starting from the root node, recursively partition the octree. In each step of the partitioning, determine whether the spatial region represented by the current node contains mesh data. If it does, further divide the node into eight child nodes according to the complexity and distribution of the data; Store the index: During the partitioning process, store the index information of the mesh data in the corresponding octree nodes. The index information will be used for subsequent distance calculations and query operations; Optimization and adjustment: Optimize and adjust the constructed octree.

5. A method for identifying a geological type according to claim 1, characterized in that, In S3, the distance calculation includes two stages: quick positioning and shortest distance calculation. The specific steps of the quick positioning are: For each point in the point cloud, the intelligent octree quickly locates the area most likely to contain the nearest fault surface point through its adaptive spatial index structure; Starting from the root node of the octree, traverse downwards level by level until the smallest node containing the target point is found.

6. The geological type identification method according to claim 5, characterized in that, The specific steps for calculating the shortest distance; Once the search area containing the nearest fault surface point is located, calculate the distances for all the surface points within this area to determine the search area: Within the search area, calculate the Euclidean distances between the points in the point cloud and the vertices, edges, and faces in the fault surface mesh for all points on the surfaces of the search area Calculate the distance to the point and find the minimum distance , where the minimum distance is calculated using the formula: ； Among them, represents the search area containing the nearest fault surface points located by the intelligent octree, and is the surface point within the search area, and are the three-dimensional coordinates of points P and Q respectively.

7. A method for identifying a geological type according to claim 1, characterized in that, In S4, store the calculated shortest distance as attribute information in each point of the point cloud data, and each point contains the shortest distance information to the fault surface.

8. A method for identifying a geological type according to claim 1, characterized in that, In S5, the specific steps for visualization and analysis: Import data: Import the point cloud data and distance attributes into the visualization software; Select visualization methods: 3D rendering: Use 3D rendering technology to display the point cloud data and distance attributes in the form of a 3D model, which helps to intuitively understand the 3D morphology of the geological structure; Contour map: Draw a contour map based on the values of the distance attributes, and the contour map shows the distribution and variation trend of the geological types; Cross-section diagram: Draw a cross-section diagram in a specific direction to show the morphology and characteristics of the geological structure on the cross-section; Adjust visualization parameters: Adjust various parameters such as color, transparency, and lighting; Observe the visualization results: Observe the 3D rendering, contour map, and cross-section diagram, and pay attention to the morphology, distribution, and variation trend of the geological structure; Identify geological types: Identify different geological types based on geological knowledge and experience; Evaluate the complexity and stability of the geological structure: Conduct a quantitative evaluation based on the distribution and characteristics of the geological types to evaluate the complexity and stability of the geological structure.

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