Three-dimensional geological modeling method, system and device and storage medium

Abstract

The invention discloses a three-dimensional geological modeling method, system and device and a storage medium, and the method comprises the steps: obtaining first point cloud data and second point cloud data of a to-be-modeled region, splicing the first point cloud data and the second point cloud data through a spatial interpolation method, and obtaining third point cloud data; performing sparse region detection on the first point cloud data and the second point cloud data through a spatial density analysis method to obtain fourth point cloud data; based on the third point cloud data and the fourth point cloud data, performing prediction through a preset graph convolutional neural network to obtain a predicted geological boundary label; and based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is carried out through a Delaunay triangulation method to obtain a three-dimensional geological model of the to-be-modeled area, and the accuracy of three-dimensional geological model construction is improved.

Description

Technical Field

[0001] The present application relates to the technical field related to three-dimensional geological modeling, and in particular to a three-dimensional geological modeling method, system, equipment and storage medium. Background Art

[0002] As a core technology in digital geology, 3D geological modeling bridges geological theory and engineering practice, playing an irreplaceable role in mineral resource exploration, geological disaster warning, and underground space development. 3D geological modeling not only visually displays complex spatial features such as underground strata, faults, and ore bodies, but also assists in exploration decision-making, optimizes resource development plans, and reduces exploration risks, thereby achieving scientific management and sustainable utilization of underground resources. Precisely because of its significant scientific value and engineering significance, improving the accuracy and reliability of 3D geological models has become a critical issue currently under investigation in the field of Earth science.

[0003] Currently, 3D geological modeling methods can be divided into two major systems: explicit modeling and implicit modeling. Regardless of the explicit or implicit modeling method, the reliability of the model is essentially limited by the quality and completeness of the input data. However, current modeling methods rely on borehole data and make insufficient use of geological data from other sources, which affects the accuracy of the 3D geological model. Summary of the Invention

[0004] This application aims to at least solve the technical problems existing in the prior art. To this end, this application proposes a three-dimensional geological modeling method, system, device and storage medium that can fully utilize the rich geological data in three-dimensional space and improve the accuracy of three-dimensional geological model construction.

[0005] In a first aspect of the present application, a three-dimensional geological modeling method is provided, comprising the following steps: Acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; splicing the first point cloud data and the second point cloud data by a spatial interpolation method to obtain third point cloud data; performing sparse area detection on the first point cloud data and the second point cloud data by a spatial density analysis method to obtain fourth point cloud data; Based on the third point cloud data and the fourth point cloud data, prediction is performed through a preset graph convolutional neural network to obtain a predicted geological boundary label; Based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed using a Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled.

[0006] The control method according to the embodiment of the present application has at least the following beneficial effects: This method obtains first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data of the geological boundary label representing the area to be modeled, and the second point cloud data is point cloud data of the geophysical information representing the area to be modeled; the first point cloud data and the second point cloud data are spliced by a spatial interpolation method to obtain third point cloud data; sparse area detection is performed on the first point cloud data and the second point cloud data by a spatial density analysis method to obtain fourth point cloud data; this application makes full use of different types of geological information by combining point cloud data of geological boundary labels and point cloud data of geophysical information, improves the integrity and information richness of the geological model, and based on the third point cloud data and the fourth point cloud data, predicts through a preset graph convolutional neural network to obtain a predicted geological boundary label; based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed through the Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled, thereby improving the accuracy of the construction of the three-dimensional geological model.

[0007] According to some embodiments of the present application, obtaining first point cloud data and second point cloud data of the area to be modeled includes: Acquire borehole data, geological maps, and physical point data representing geophysical information of the area to be modeled, wherein the borehole data includes borehole mouth coordinates, inclination point distance, inclination point vertex angle, inclination point azimuth, and a first geological boundary label; The coordinates of the first geological demarcation point are calculated based on the drilling data using the following formula: ; in, is the east coordinate of the borehole mouth, is the north coordinate of the borehole mouth, is the elevation of the borehole mouth, For the Inclination point to the The distance between the inclinometer points, For the The top angle of the inclinometer point, For the The azimuth of the inclinometer point, For the The east coordinate of the first geological boundary point, For the The north coordinate of the first geological demarcation point, For the The elevation of the first geological dividing point; Based on the geological map, extracting the coordinates of the second geological boundary point and its corresponding second geological boundary label using mapping software and preset intervals; Taking the first geological boundary point coordinates, the first geological boundary label, the second geological boundary point coordinates and the second geological boundary label as the first point cloud data; Obtaining corresponding three-dimensional coordinates and corresponding geophysical attribute values of the physical point data; The corresponding three-dimensional coordinates and the corresponding geophysical attribute values are used as the second point cloud data.

[0008] According to some embodiments of the present application, extracting the coordinates of the second geological boundary point and its corresponding second geological boundary label based on the geological map by using mapping software and preset intervals includes: Based on the geological map, extracting a plurality of second geological boundary points using mapping software and preset intervals; Determining a second geological boundary label, a preset coordinate value of the second geological boundary point, a starting point coordinate of the geological section, and a strike angle corresponding to each second geological boundary point; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset east coordinate, the corresponding north coordinate is calculated by the following formula: ; in, For the The north coordinate of the second geological boundary point, is the north coordinate of the starting point of the geological section, is the east coordinate of the starting point of the geological section, For the The preset east coordinate of the second geological demarcation point, is the strike angle; Using the second geological boundary point preset coordinate value and the corresponding north coordinate corresponding to each second geological boundary point as the second geological boundary point coordinates; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset north coordinate, the corresponding east coordinate is calculated by the following formula: ; in, For the The corresponding east coordinate of the second geological boundary point, For the The preset north coordinate of the second geological demarcation point; The second geological boundary point preset coordinate value corresponding to each second geological boundary point and the corresponding east coordinate are used as the second geological boundary point coordinates.

[0009] According to some embodiments of the present application, the step of splicing the first point cloud data and the second point cloud data using a spatial interpolation method to obtain third point cloud data includes: Calculating the distance between any point in the first point cloud data and any point in the second point cloud data; The first geophysical attribute value of the first point cloud data is calculated using the following formula: ; in, is the first point cloud data point to the first point in the second point cloud data The weight of the point, is the first point cloud data point to the first point in the second point cloud data The distance between points, is the total number of points in the second point cloud data, is the first point cloud data The first geophysical attribute value of the point, The first point in the second point cloud data The corresponding geophysical attribute values of the points, is the power of the preset distance; The first point cloud data and the first geophysical attribute value are used as the third point cloud data.

[0010] According to some embodiments of the present application, performing sparse area detection on the first point cloud data and the second point cloud data using a spatial density analysis method to obtain fourth point cloud data includes: Calculate the average Euclidean distance of all points in the first point cloud data; Dividing the area to be modeled into grids based on the average Euclidean distance to obtain a divided grid; Determine the total number of grids and the number of corresponding points in each grid after division; Calculating a global average density based on the total number of points in the first point cloud data and the total number of grids; determining a sparse grid based on the global average density and the number of corresponding points; generating a point cloud to be completed in the sparse grid based on the preset interval; The point cloud to be completed and the second point cloud data are spliced together using a spatial interpolation method to obtain the fourth point cloud data.

[0011] According to some embodiments of the present application, the step of performing prediction based on the third point cloud data and the fourth point cloud data by using a preset graph convolutional neural network to obtain a predicted geological boundary label and a corresponding predicted probability includes: Constructing an initial graph convolutional neural network, inputting the third point cloud data into the initial graph convolutional neural network for model training to obtain the preset graph convolutional neural network; The fourth point cloud data is input into the preset graph convolutional neural network for prediction to obtain a predicted geological boundary label.

[0012] According to some embodiments of the present application, the modeling is performed using a Delaunay triangulation method based on the third point cloud data, the fourth point cloud data, and the predicted geological boundary label to obtain a three-dimensional geological model of the area to be modeled, including: Inputting the fourth point cloud data into the preset graph convolutional neural network for prediction to obtain a corresponding prediction probability of the predicted geological boundary label; Filtering the fourth point cloud data based on the corresponding predicted probability and a preset filtering value to obtain filtered point cloud data; The filtered point cloud data and the corresponding predicted geological boundary label are used as fifth point cloud data; Connecting points of the same type of geological boundary labels in the third point cloud data and the fifth point cloud data using a Delaunay triangulation method to obtain an initial surface triangulated network model; Obtaining an encoded geological model file according to the encoding of the initial surface triangulated network model; Modeling is performed based on the encoded geological model file to obtain a three-dimensional geological model of the area to be modeled.

[0013] In a second aspect of the present application, a three-dimensional geological modeling system is provided. The three-dimensional geological modeling system includes: a data acquisition module, configured to acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; a splicing module, configured to splice the first point cloud data and the second point cloud data using a spatial interpolation method to obtain third point cloud data; a sparse area detection module, configured to perform sparse area detection on the first point cloud data and the second point cloud data by using a spatial density analysis method to obtain fourth point cloud data; A prediction module, configured to perform prediction based on the third point cloud data and the fourth point cloud data by using a preset graph convolutional neural network to obtain a predicted geological boundary label; A modeling module is used to perform modeling based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label by using a Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled.

[0014] This system obtains first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data of the geological boundary label of the area to be modeled, and the second point cloud data is point cloud data of the geophysical information of the area to be modeled; the first point cloud data and the second point cloud data are spliced by the spatial interpolation method to obtain third point cloud data; the first point cloud data and the second point cloud data are sparsely detected by the spatial density analysis method to obtain fourth point cloud data; this application makes full use of different types of geological information by combining the point cloud data of the geological boundary label and the point cloud data of the geophysical information, improves the integrity and information richness of the geological model, and based on the third point cloud data and the fourth point cloud data, predicts by a preset graph convolutional neural network to obtain a predicted geological boundary label; based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed by the Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled, thereby improving the accuracy of the construction of the three-dimensional geological model.

[0015] A third aspect of the present application provides a three-dimensional geological modeling electronic device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform the above-mentioned three-dimensional geological modeling method.

[0016] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned three-dimensional geological modeling method.

[0017] It should be noted that the beneficial effects between the second to fourth aspects of the present application and the prior art are the same as the beneficial effects between the above-mentioned three-dimensional geological modeling system and the prior art, and will not be described in detail here.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1This is a flow chart of a three-dimensional geological modeling method according to an embodiment of the present application; Figure 2 is a schematic structural diagram of an embodiment of a three-dimensional geological modeling system provided by the present application; Figure 3 It is a structural diagram of an embodiment of the electronic device provided by this application. DETAILED DESCRIPTION

[0020] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0021] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0022] In the description of this application, it should be understood that descriptions involving orientation, such as the orientation or positional relationship indicated by up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0023] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technical personnel in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0024] As a core technology in digital geology, 3D geological modeling bridges geological theory and engineering practice, playing an irreplaceable role in mineral resource exploration, geological disaster warning, and underground space development. 3D geological modeling not only visually displays complex spatial features such as underground strata, faults, and ore bodies, but also assists in exploration decision-making, optimizes resource development plans, and reduces exploration risks, thereby achieving scientific management and sustainable utilization of underground resources. Precisely because of its significant scientific value and engineering significance, improving the accuracy and reliability of 3D geological models has become a critical issue currently under investigation in the field of Earth science.

[0025] Currently, 3D geological modeling methods can be divided into two major systems: explicit modeling and implicit modeling. Regardless of the explicit or implicit modeling method, the reliability of the model is essentially limited by the quality and completeness of the input data. However, current modeling methods rely on borehole data and make insufficient use of geological data from other sources, which affects the accuracy of the 3D geological model.

[0026] In order to solve the above technical defects, the embodiments of the present application provide a three-dimensional geological modeling method, system, device and storage medium.

[0027] See Figure 1 , is a flow chart of a three-dimensional geological modeling method provided in an embodiment of the present application, the method is applied to an electronic device, which may be a server, etc. Figure 1 As shown, the three-dimensional geological modeling method includes: Step S101: Acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; Step S102: splicing the first point cloud data and the second point cloud data by a spatial interpolation method to obtain third point cloud data; Step S103: Perform sparse area detection on the first point cloud data and the second point cloud data using a spatial density analysis method to obtain fourth point cloud data; Step S104: Based on the third point cloud data and the fourth point cloud data, prediction is performed using a preset graph convolutional neural network to obtain a predicted geological boundary label; Step S105 : Based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, a Delaunay triangulation method is used to perform modeling to obtain a three-dimensional geological model of the area to be modeled.

[0028] This method obtains first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data of the geological boundary label representing the area to be modeled, and the second point cloud data is point cloud data of the geophysical information representing the area to be modeled; the first point cloud data and the second point cloud data are spliced by a spatial interpolation method to obtain third point cloud data; sparse area detection is performed on the first point cloud data and the second point cloud data by a spatial density analysis method to obtain fourth point cloud data; this application makes full use of different types of geological information by combining point cloud data of geological boundary labels and point cloud data of geophysical information, improves the integrity and information richness of the geological model, and based on the third point cloud data and the fourth point cloud data, predicts through a preset graph convolutional neural network to obtain a predicted geological boundary label; based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed through the Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled, thereby improving the accuracy of the construction of the three-dimensional geological model.

[0029] In some embodiments, obtaining first point cloud data and second point cloud data of the area to be modeled includes: Step S201: Acquire borehole data, geological maps, and physical point data representing geophysical information of the area to be modeled, wherein the borehole data includes borehole mouth coordinates, inclination point distance, inclination point vertex angle, inclination point azimuth, and a first geological boundary label; Step S202: Calculate the coordinates of the first geological demarcation point based on the drilling data using the following formula: ; in, is the east coordinate of the borehole mouth, is the north coordinate of the borehole mouth, is the elevation of the borehole mouth, For the Inclination point to the The distance between the inclinometer points, For the The top angle of the inclinometer point, For the The azimuth of the inclinometer point, For the The east coordinate of the first geological boundary point, For the The north coordinate of the first geological demarcation point, For the The elevation of the first geological dividing point; Step S203: Based on the geological map, extract the coordinates of the second geological boundary point and its corresponding second geological boundary label using mapping software and preset intervals; Step S204: taking the first geological boundary point coordinates, the first geological boundary label, the second geological boundary point coordinates, and the second geological boundary label as first point cloud data; Step S205: Obtain corresponding three-dimensional coordinates and corresponding geophysical attribute values of the physical point data; Step S206: Use the corresponding three-dimensional coordinates and the corresponding geophysical attribute values as second point cloud data.

[0030] This application constructs a multi-source point cloud through drilling data, geological boundary points in geological maps and geophysical exploration points, breaking through the limitations of traditional methods that mainly rely on drilling data, making full use of different types of geological information, and improving the integrity and information richness of the geological model.

[0031] In some embodiments, based on the geological map, extracting the coordinates of the second geological boundary point and its corresponding second geological boundary label using mapping software and preset intervals includes: Step S301: extracting multiple second geological boundary points based on the geological map using mapping software and preset intervals; Step S302: Determine the second geological boundary label, the preset coordinate value of the second geological boundary point, the starting point coordinates of the geological section, and the strike angle corresponding to each second geological boundary point; Step S303: When the preset coordinate values of the second geological boundary point are the preset elevation and the preset east coordinate, the corresponding north coordinate is calculated using the following formula: ; in, For the The north coordinate of the second geological boundary point, is the north coordinate of the starting point of the geological section, is the east coordinate of the starting point of the geological section, For the The preset east coordinate of the second geological demarcation point, is the strike angle; Step S304: using the preset coordinate value of the second geological boundary point and the corresponding north coordinate corresponding to each second geological boundary point as the coordinates of the second geological boundary point; Step S305: When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset north coordinate, the corresponding east coordinate is calculated using the following formula: ; in, For the The corresponding east coordinate of the second geological boundary point, For the The preset north coordinate of the second geological demarcation point; Step S306: Use the preset coordinate value of the second geological boundary point and the corresponding east coordinate corresponding to each second geological boundary point as the coordinates of the second geological boundary point.

[0032] This application provides a data basis for subsequent modeling by calculating the coordinates of the second geological dividing point, thereby improving the accuracy of model construction.

[0033] In some embodiments, the first point cloud data and the second point cloud data are spliced together using a spatial interpolation method to obtain third point cloud data, including: Step S401: Calculate the distance between any point in the first point cloud data and any point in the second point cloud data; Step S402: Calculate the first geophysical attribute value of the first point cloud data using the following formula: ; in, is the first point cloud data point to the first point in the second point cloud data The weight of the point, is the first point cloud data point to the first point in the second point cloud data The distance between points, is the total number of points in the second point cloud data, is the first point cloud data The first geophysical attribute value of the point, The first point in the second point cloud data The corresponding geophysical attribute values of the points, is the power of the preset distance; Step S403: Use the first point cloud data and the first geophysical attribute value as third point cloud data.

[0034] This application calculates the second point cloud data and the first geophysical attribute value and uses them as the third point cloud data to provide a data basis for subsequent modeling and improve the accuracy of model construction.

[0035] In some embodiments, performing sparse area detection on the first point cloud data and the second point cloud data using a spatial density analysis method to obtain fourth point cloud data includes: Step S501: Calculate the average Euclidean distance of all points in the first point cloud data; Step S502: Divide the area to be modeled into grids based on the average Euclidean distance to obtain a divided grid; Step S503: determining the total number of grids after division and the number of corresponding points in each grid; Step S504: Calculate the global average density based on the total number of points and the total number of grids in the first point cloud data; Step S505: determining a sparse grid based on the global average density and the number of corresponding points; Step S506: generating a point cloud to be completed in a sparse grid based on a preset interval; Step S507: splicing the point cloud to be completed and the second point cloud data using a spatial interpolation method to obtain fourth point cloud data.

[0036] This application uses a spatial density analysis method to generate a complete point cloud representing geological boundary information in data-sparse areas, which makes up for the defect that traditional interpolation methods are difficult to fit complex geological interface shapes in data-sparse areas.

[0037] In some embodiments, based on the third point cloud data and the fourth point cloud data, a preset graph convolutional neural network is used to perform prediction to obtain a predicted geological boundary label and a corresponding prediction probability, including: Step S601: construct an initial graph convolutional neural network, input the third point cloud data into the initial graph convolutional neural network for model training, and obtain a preset graph convolutional neural network; Step S602: Input the fourth point cloud data into a preset graph convolutional neural network for prediction to obtain a predicted geological boundary label.

[0038] This application uses graph convolutional neural networks to learn the spatial distribution patterns of geological interfaces from multi-source point clouds, realizes automated spatial relationship learning and completion, reduces the workload and subjective interference of modeling, and improves the scientificity and efficiency of modeling.

[0039] In some embodiments, a three-dimensional geological model of the area to be modeled is obtained by performing modeling using a Delaunay triangulation method based on the third point cloud data, the fourth point cloud data, and the predicted geological boundary label, including: Step S701: input the fourth point cloud data into a preset graph convolutional neural network for prediction to obtain the corresponding prediction probability of the predicted geological boundary label; Step S702: Filter the fourth point cloud data based on the corresponding predicted probability and the preset filtering value to obtain filtered point cloud data; Step S703: Use the filtered point cloud data and the corresponding predicted geological boundary label as the fifth point cloud data; Step S704: connecting the points with the same type of geological boundary labels in the third point cloud data and the fifth point cloud data using the Delaunay triangulation method to obtain an initial surface triangulated network model; Step S705: Encode the initial surface triangulated mesh model to obtain an encoded geological model file; Step S706: Modeling is performed based on the encoded geological model file to obtain a three-dimensional geological model of the area to be modeled.

[0040] Specifically, to facilitate understanding by those skilled in the art, a set of best embodiments are provided below: 1. Data Acquisition Obtain first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing the geological boundary label of the area to be modeled, and the second point cloud data is point cloud data representing the geophysical information of the area to be modeled, specifically: Obtaining borehole data, geological maps, and physical point data representing geophysical information of the area to be modeled, wherein the borehole data includes borehole mouth coordinates, inclination point distance, inclination point top angle, inclination point azimuth, and a first geological boundary label; The coordinates of the first geological demarcation point are calculated based on the drilling data using the following formula: ; in, is the east coordinate of the borehole mouth, is the north coordinate of the borehole mouth, is the elevation of the borehole mouth, For the Inclination point to the The distance between the inclinometer points, For the The top angle of the inclinometer point, For the The azimuth of the inclinometer point, For the The east coordinate of the first geological boundary point, For the The north coordinate of the first geological demarcation point, For the The elevation of the first geological dividing point; Based on the geological map, the coordinates of the second geological boundary point and its corresponding second geological boundary label are extracted using drawing software and preset intervals, specifically: Based on the geological map, a plurality of second geological boundary points are extracted by using mapping software and preset intervals; Determine the second geological boundary label, the preset coordinate value of the second geological boundary point, the starting point coordinates of the geological section, and the strike angle corresponding to each second geological boundary point; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset east coordinate, the corresponding north coordinate is calculated using the following formula: ; in, For the The north coordinate of the second geological boundary point, is the north coordinate of the starting point of the geological section, is the east coordinate of the starting point of the geological section, For the The preset east coordinate of the second geological demarcation point, is the strike angle; The preset coordinate value of the second geological boundary point and the corresponding north coordinate corresponding to each second geological boundary point are used as the coordinates of the second geological boundary point; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset north coordinate, the corresponding east coordinate is calculated using the following formula: ; in, For the The corresponding east coordinate of the second geological boundary point, For the The preset north coordinate of the second geological demarcation point; The second geological boundary point preset coordinate value and the corresponding east coordinate corresponding to each second geological boundary point are used as the second geological boundary point coordinates.

[0041] The first geological boundary point coordinates, the first geological boundary label, the second geological boundary point coordinates and the second geological boundary label are used as first point cloud data; Obtain the corresponding three-dimensional coordinates and corresponding geophysical attribute values of the physical point data; The corresponding three-dimensional coordinates and the corresponding geophysical attribute values are used as the second point cloud data.

[0042] 2. Data splicing: The first point cloud data and the second point cloud data are spliced together by a spatial interpolation method to obtain the third point cloud data, specifically: Calculating the distance between any point in the first point cloud data and any point in the second point cloud data; The first geophysical attribute value of the first point cloud data is calculated using the following formula: ; in, is the first point cloud data point to the first point in the second point cloud data The weight of the point, is the first point cloud data point to the first point in the second point cloud data The distance between points, is the total number of points in the second point cloud data, is the first point cloud data The first geophysical attribute value of the point, The first point in the second point cloud data The corresponding geophysical attribute values of the points, is the power of the preset distance; The first point cloud data and the first geophysical attribute value are used as third point cloud data.

[0043] 3. Sparse area detection: The sparse area detection is performed on the first point cloud data and the second point cloud data by the spatial density analysis method to obtain the fourth point cloud data, specifically: Calculate the average Euclidean distance of all points in the first point cloud data; The area to be modeled is divided into grids based on the average Euclidean distance to obtain a divided grid; Determine the total number of grids and the number of corresponding points in each grid after division; Calculate the global average density based on the total number of points and the total number of grids in the first point cloud data; Determine the sparse grid based on the global average density and the number of corresponding points; Generate a point cloud to be completed in a sparse grid based on a preset interval; The point cloud to be completed and the second point cloud data are spliced together through the spatial interpolation method to obtain the fourth point cloud data.

[0044] 4. Forecast: Based on the third point cloud data and the fourth point cloud data, prediction is performed through the preset graph convolutional neural network to obtain the predicted geological boundary label, specifically: Construct an initial graph convolutional neural network, input the third point cloud data into the initial graph convolutional neural network for model training, and obtain a preset graph convolutional neural network; The fourth point cloud data is input into the preset graph convolutional neural network for prediction to obtain the predicted geological boundary label.

[0045] 5. Modeling Based on the third point cloud data, the fourth point cloud data and the predicted geological boundary labels, the Delaunay triangulation method is used to model the area to be modeled, and the three-dimensional geological model is obtained. Specifically: Input the fourth point cloud data into the preset graph convolutional neural network for prediction, and obtain the corresponding prediction probability of the predicted geological boundary label; Filtering the fourth point cloud data based on the corresponding predicted probability and the preset filtering value to obtain filtered point cloud data; The filtered point cloud data and the corresponding predicted geological boundary labels are used as the fifth point cloud data; Connect the points with the same geological boundary label in the third point cloud data and the fifth point cloud data using the Delaunay triangulation method to obtain an initial surface triangulated network model; According to the encoding of the initial surface triangulated network model, an encoded geological model file is obtained; Modeling is performed based on the encoded geological model file to obtain a three-dimensional geological model of the area to be modeled.

[0046] In addition, refer to Figure 2 One embodiment of the present application provides a three-dimensional geological modeling system, including a data acquisition module 1100, a splicing module 1200, a sparse region detection module 1300, a prediction module 1400, and a modeling module 1500, wherein: The data acquisition module 1100 is used to acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; The splicing module 1200 is used to splice the first point cloud data and the second point cloud data by a spatial interpolation method to obtain third point cloud data; The sparse region detection module 1300 is configured to perform sparse region detection on the first point cloud data and the second point cloud data by using a spatial density analysis method to obtain fourth point cloud data; The prediction module 1400 is used to perform prediction based on the third point cloud data and the fourth point cloud data through a preset graph convolutional neural network to obtain a predicted geological boundary label; The modeling module 1500 is used to perform modeling by using the Delaunay triangulation method based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label to obtain a three-dimensional geological model of the area to be modeled.

[0047] This system obtains first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data of the geological boundary label of the area to be modeled, and the second point cloud data is point cloud data of the geophysical information of the area to be modeled; the first point cloud data and the second point cloud data are spliced by the spatial interpolation method to obtain third point cloud data; the first point cloud data and the second point cloud data are sparsely detected by the spatial density analysis method to obtain fourth point cloud data; this application makes full use of different types of geological information by combining the point cloud data of the geological boundary label and the point cloud data of the geophysical information, improves the integrity and information richness of the geological model, and based on the third point cloud data and the fourth point cloud data, predicts by a preset graph convolutional neural network to obtain a predicted geological boundary label; based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed by the Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled, thereby improving the accuracy of the construction of the three-dimensional geological model.

[0048] It should be noted that this system embodiment and the above-mentioned method embodiment are based on the same inventive concept, so the relevant content of the above-mentioned method embodiment is also applicable to this system embodiment and will not be repeated here.

[0049] Figure 3 A schematic diagram of the rule mining hardware structure provided by an embodiment of the present application is shown.

[0050] The three-dimensional geological modeling device may include a processor 301 and a memory 302 storing computer program instructions.

[0051] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0052] Memory 302 may include a large-capacity memory for data or instructions. By way of example and not limitation, memory 302 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, memory 302 is a non-volatile solid-state memory.

[0053] In some embodiments, the memory 302 may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0054] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any one of the three-dimensional geological modeling methods in the above embodiments.

[0055] In one example, the 3D geological modeling device may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301 , the memory 302 , and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0056] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0057] Bus 310 includes hardware, software, or both, and couples the components of the 3D geological modeling apparatus to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Area Network (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0058] The 3D geological modeling device can execute the 3D geological modeling method in the embodiment of the present application based on the 3D design model, thereby realizing the combination of Figure 1 and Figure 2 Described are a three-dimensional geological modeling method and system.

[0059] In addition, in conjunction with the three-dimensional geological modeling method in the above embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the three-dimensional geological modeling methods in the above embodiments is implemented.

[0060] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0061] The functional blocks shown in the above block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memory, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments can be downloaded via a computer network such as the Internet or an intranet.

[0062] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0063] Aspects of the present disclosure have been described above with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that execution of these instructions by the processor of the computer or other programmable data processing device enables the implementation of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, as well as combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0064] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A three-dimensional geological modeling method, characterized in that: The three-dimensional geological modeling method comprises: Acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; splicing the first point cloud data and the second point cloud data by a spatial interpolation method to obtain third point cloud data; performing sparse area detection on the first point cloud data and the second point cloud data by a spatial density analysis method to obtain fourth point cloud data; Based on the third point cloud data and the fourth point cloud data, prediction is performed through a preset graph convolutional neural network to obtain a predicted geological boundary label; Based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label, modeling is performed using a Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled.

2. The three-dimensional geological modeling method according to claim 1, characterized in that: The obtaining of first point cloud data and second point cloud data of the area to be modeled includes: Acquire borehole data, a geological map, and physical point data representing geophysical information of the area to be modeled, wherein the borehole data includes borehole mouth coordinates, inclination point distance, inclination point vertex angle, inclination point azimuth, and a first geological boundary label; The coordinates of the first geological demarcation point are calculated based on the drilling data using the following formula: ； in, is the east coordinate of the borehole mouth, is the north coordinate of the borehole mouth, is the elevation of the borehole mouth, For the Inclination point to the The distance between the inclinometer points, For the The top angle of the inclinometer point, For the The azimuth of the inclinometer point, For the The east coordinate of the first geological boundary point, For the The north coordinate of the first geological demarcation point, For the The elevation of the first geological dividing point; Based on the geological map, extracting the coordinates of the second geological boundary point and its corresponding second geological boundary label using mapping software and preset intervals; Taking the first geological boundary point coordinates, the first geological boundary label, the second geological boundary point coordinates and the second geological boundary label as the first point cloud data; Obtaining corresponding three-dimensional coordinates and corresponding geophysical attribute values of the physical point data; The corresponding three-dimensional coordinates and the corresponding geophysical attribute values are used as the second point cloud data.

3. The three-dimensional geological modeling method according to claim 2, characterized in that: The method of extracting the coordinates of the second geological boundary point and the corresponding second geological boundary label based on the geological map by using drawing software and preset intervals includes: Based on the geological map, extracting a plurality of second geological boundary points using mapping software and preset intervals; Determining a second geological boundary label, a preset coordinate value of the second geological boundary point, a starting point coordinate of the geological section, and a strike angle corresponding to each second geological boundary point; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset east coordinate, the corresponding north coordinate is calculated by the following formula: ； in, For the The north coordinate of the second geological boundary point, is the north coordinate of the starting point of the geological section, is the east coordinate of the starting point of the geological section, For the The preset east coordinate of the second geological demarcation point, is the strike angle; Using the second geological boundary point preset coordinate value and the corresponding north coordinate corresponding to each second geological boundary point as the second geological boundary point coordinates; When the preset coordinate values of the second geological demarcation point are the preset elevation and the preset north coordinate, the corresponding east coordinate is calculated by the following formula: ； in, For the The corresponding east coordinate of the second geological boundary point, For the The preset north coordinate of the second geological demarcation point; The second geological boundary point preset coordinate value corresponding to each second geological boundary point and the corresponding east coordinate are used as the second geological boundary point coordinates.

4. The three-dimensional geological modeling method according to claim 2, characterized in that: The step of splicing the first point cloud data and the second point cloud data by a spatial interpolation method to obtain third point cloud data includes: Calculating the distance between any point in the first point cloud data and any point in the second point cloud data; The first geophysical attribute value of the first point cloud data is calculated using the following formula: ； in, is the first point cloud data point to the first point in the second point cloud data The weight of the point, is the first point cloud data point to the first point in the second point cloud data The distance between points, is the total number of points in the second point cloud data, is the first point cloud data The first geophysical attribute value of the point, The first point in the second point cloud data The corresponding geophysical attribute values of the points, is the power of the preset distance; The first point cloud data and the first geophysical attribute value are used as the third point cloud data.

5. The three-dimensional geological modeling method according to claim 4, characterized in that: The performing sparse area detection on the first point cloud data and the second point cloud data by a spatial density analysis method to obtain fourth point cloud data includes: Calculate the average Euclidean distance of all points in the first point cloud data; Dividing the area to be modeled into grids based on the average Euclidean distance to obtain a divided grid; Determine the total number of grids and the number of corresponding points in each grid after division; Calculating a global average density based on the total number of points in the first point cloud data and the total number of grids; determining a sparse grid based on the global average density and the number of corresponding points; generating a point cloud to be completed in the sparse grid based on the preset interval; The point cloud to be completed and the second point cloud data are spliced together using a spatial interpolation method to obtain the fourth point cloud data.

6. The three-dimensional geological modeling method according to claim 1, characterized in that: The method of performing prediction based on the third point cloud data and the fourth point cloud data by using a preset graph convolutional neural network to obtain a predicted geological boundary label and a corresponding prediction probability includes: Constructing an initial graph convolutional neural network, inputting the third point cloud data into the initial graph convolutional neural network for model training to obtain the preset graph convolutional neural network; The fourth point cloud data is input into the preset graph convolutional neural network for prediction to obtain a predicted geological boundary label.

7. The three-dimensional geological modeling method according to claim 6, characterized in that: The method of modeling based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label by using a Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled includes: Inputting the fourth point cloud data into the preset graph convolutional neural network for prediction to obtain a corresponding prediction probability of the predicted geological boundary label; Filtering the fourth point cloud data based on the corresponding predicted probability and a preset filtering value to obtain filtered point cloud data; The filtered point cloud data and the corresponding predicted geological boundary label are used as fifth point cloud data; Connecting points of the same type of geological boundary labels in the third point cloud data and the fifth point cloud data using a Delaunay triangulation method to obtain an initial surface triangulated network model; Obtaining an encoded geological model file according to the encoding of the initial surface triangulated network model; Modeling is performed based on the encoded geological model file to obtain a three-dimensional geological model of the area to be modeled.

8. A three-dimensional geological modeling system, characterized in that: The three-dimensional geological modeling system includes: a data acquisition module, configured to acquire first point cloud data and second point cloud data of the area to be modeled, wherein the first point cloud data is point cloud data representing geological boundary labels of the area to be modeled, and the second point cloud data is point cloud data representing geophysical information of the area to be modeled; a splicing module, configured to splice the first point cloud data and the second point cloud data using a spatial interpolation method to obtain third point cloud data; a sparse area detection module, configured to perform sparse area detection on the first point cloud data and the second point cloud data by using a spatial density analysis method to obtain fourth point cloud data; A prediction module, configured to perform prediction based on the third point cloud data and the fourth point cloud data by using a preset graph convolutional neural network to obtain a predicted geological boundary label; A modeling module is used to perform modeling based on the third point cloud data, the fourth point cloud data and the predicted geological boundary label by using a Delaunay triangulation method to obtain a three-dimensional geological model of the area to be modeled.

9. A three-dimensional geological modeling device, characterized in that: The invention comprises at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to perform a three-dimensional geological modeling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a three-dimensional geological modeling method according to any one of claims 1 to 7.

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