Mine geological property modeling method and system

Abstract

The application provides a mine geological attribute modeling method and system. The method comprises the following steps: generating a triangular meshed stratum interface surface stored in a half-edge data structure according to geological drilling data and seismic data; constructing a three-dimensional geological layer block sequence defined by a triangular mesh surface model by using a pair of stratum interface surfaces and a boundary of a region of interest; performing discretization processing on the three-dimensional geological layer blocks in the sequence, adaptively adopting a cuboid unit or a triangular prism unit for splitting according to a block thickness and an adjacent relationship, and generating a discrete unit set; and performing attribute interpolation on each unit according to drilling point attribute data, and generating the discrete unit set with geological attribute values. The method can realize full-process automation and standardization of mine geological attribute modeling, improve modeling efficiency, and improve the accuracy and continuity of the constructed model.

Description

Technical Field

[0001] This application relates to the field of digital and intelligent mining technology, and in particular to a method and system for modeling geological attributes in mines. Background Technology

[0002] Currently, mine geological attribute modeling, as a core technology for building "transparent mines" and realizing safe and intelligent mining, is widely used in fields such as mineral resource assessment, safe production, and intelligent mining.

[0003] Current mine geological attribute modeling schemes in related technologies often employ fragmented software modules or only discretize single thick blocks, failing to form an integrated and intelligent end-to-end system. This can lead to cumbersome and inefficient modeling processes. When dealing with ultra-thin geological layers or multi-layered geological sequences, it can easily result in accuracy defects such as discrete units penetrating strata surfaces and discontinuities between layers, thereby affecting the reliability of "transparent mine" construction and the scientific nature of mine safety production decisions. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a method for modeling geological properties in mines.

[0006] The second objective of this application is to propose a modeling system for geological properties in mines.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a computer-readable storage medium.

[0009] To achieve the above objectives, the first aspect of this application is to propose a method for modeling geological properties in mines, comprising the following steps:

[0010] Based on geological borehole data and seismic data, a triangular meshed stratigraphic interface surface is generated and stored in a half-side data structure. Using the paired stratigraphic interface surfaces and the boundaries of the region of interest, a sequence of three-dimensional geological blocks defined by a triangular mesh surface model is constructed; The three-dimensional geological layer blocks in the sequence are discretized. Based on the thickness and adjacency of each three-dimensional geological layer block, cuboid or triangular prism elements are adaptively used to divide the block, generating a set of discrete elements. Based on the borehole point attribute data, attribute interpolation is performed on each unit in the discrete unit set to generate a discrete unit set with geological attribute values.

[0011] To achieve the above objectives, the second aspect of this application also proposes a mine geological attribute modeling system for performing the mine geological attribute modeling method of the first aspect, the system comprising: The formation interface surface generation subsystem is used to generate triangular meshed formation interface surfaces stored in a half-edge data structure based on geological borehole data and seismic data. A stratigraphic block construction subsystem is used to construct a sequence of three-dimensional geological block shapes defined by a triangular mesh surface model using pairs of the stratigraphic interface surfaces and the boundaries of the region of interest. The stratigraphic block decomposition system is used to discretize the three-dimensional geological block in the sequence. Based on the thickness and adjacency of each three-dimensional geological block, it adaptively uses cuboid or triangular prism elements to decompose the block and generate a set of discrete elements. The stratigraphic block attribute interpolation subsystem is used to perform attribute interpolation on each unit in the discrete unit set based on borehole point attribute data, and generate a discrete unit set with geological attribute values. The attribute modeling 3D visualization subsystem is used to perform 3D visualization rendering of the discrete unit set with geological attribute values ​​and provide users with a variety of interactive operations. The attribute modeling output subsystem is used to export the results of mine geological attribute modeling and to link with external systems.

[0012] To achieve the above objectives, a third aspect of this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the mine geological property modeling method as described in any one of the first aspects above.

[0013] To achieve the above objectives, the fourth aspect of this application also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the mining geological attribute modeling method described in any one of the first aspects above.

[0014] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application realizes the automation and standardization of the entire process of mine geological attribute modeling, achieving one-click or pipelined processing from raw data to the final model, greatly reducing the threshold for use and improving work efficiency. Furthermore, it can intelligently handle complex geological conditions. Through technologies such as intelligent thin-layer judgment and automatic identification and merging of adjacent layers, it can automatically adapt to complex geological conditions such as thin layers and multi-layer sequences, ensuring the accuracy and reliability of complex geological environment attribute modeling. Therefore, this application can realize the automation and standardization of the entire process of mine geological attribute modeling, effectively solving the problems of low modeling accuracy and model discontinuity under complex geological conditions, improving modeling efficiency, and enhancing the accuracy and continuity of the constructed model.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for modeling geological properties in a mine, as proposed in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of a mine geological attribute modeling system proposed in an embodiment of this application. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0018] The following description, with reference to the accompanying drawings, illustrates a method and system for modeling geological attributes in a mine, as proposed in an embodiment of this application.

[0019] Example 1 Figure 1 This is a flowchart of a method for modeling geological properties in a mine, as proposed in an embodiment of this application. Figure 1 As shown, the method includes the following steps: Step S101: Based on geological borehole data and seismic data, generate a triangular meshed stratigraphic interface surface stored in a half-side data structure.

[0020] Specifically, this step is the foundation for building a three-dimensional geological attribute model. It aims to transform discrete, unstructured raw geological data into a continuous surface representation with a clear geometric shape and topological relationship, providing precise boundary constraints for subsequent geological block construction.

[0021] Specifically, the spatial point set on the stratigraphic interface is first extracted from the geological borehole data, and the stratigraphic and fault information is obtained from the seismic data. These data together constitute the original input for surface generation.

[0022] Subsequently, spatial interpolation was used to process the elevation data of each stratigraphic interface in order to fit a continuous surface that reflects the spatial undulation trend of the interface.

[0023] After interpolation, the continuous surface is triangulated within the specified region of interest, discretizing it into a surface model composed of a series of triangular patches. To efficiently manage and utilize this triangular mesh data, this step innovatively employs a half-edge data structure for storage. This data structure not only records the geometric information of each vertex and patch but also explicitly stores the topological adjacency relationships between patches.

[0024] As one implementation method, geostatistical methods such as Kriging, discrete smoothing interpolation, or radial basis function method can be used for spatial interpolation, and the interpolated surfaces can be triangularized into meshes. Finally, these triangular mesh surfaces are stored and managed in a half-side data structure.

[0025] Therefore, by adopting the above-mentioned technical means, this step can efficiently and accurately transform the original discrete data into triangular mesh surfaces with clear topological relationships, providing high-precision geometric boundaries and efficient adjacency query capabilities for the subsequent construction of geological blocks, thus laying a reliable foundation for the entire attribute modeling process.

[0026] Step S102: Using paired stratigraphic interface surfaces and region of interest boundaries, construct a sequence of three-dimensional geological blocks defined by a triangular mesh surface model.

[0027] Specifically, when constructing a 3D geological block using paired stratigraphic interface surfaces, the process first involves acquiring triangularly meshed stratigraphic interface surfaces generated in the previous processing step and stored in a half-edge data structure. These surfaces represent the top and bottom interfaces of the target geological layer, respectively. Simultaneously, the user- or system-preset boundary of the region of interest is acquired; this boundary defines the spatial scope of the modeling. By spatially pruning the top and bottom interface surfaces, only the portion of the surface located within the region of interest is retained.

[0028] Subsequently, the boundary of the region of interest is stretched vertically to form a series of lateral boundary surfaces. These lateral boundary surfaces are used to connect the clipped top and bottom interface surfaces, thus enclosing a closed three-dimensional space. In order to represent this closed space as a surface model that can be discretized later, the clipped top, bottom, and lateral boundary surfaces are triangulated to generate their respective triangular meshes.

[0029] Finally, these triangular meshes are merged to form a complete three-dimensional geological block surface model, which is defined by a series of triangular facets and accurately describes the spatial geometry of the geological block.

[0030] As one possible implementation, a complete geological block surface model can be constructed by stretching the polygon of the region of interest vertically to form a column, using the boundary surface of the column to connect the cut top and bottom surfaces, and then merging the top, bottom, and boundary surfaces after triangulation.

[0031] Therefore, this step, by utilizing paired stratigraphic interface surfaces and the boundary of the region of interest, achieves the accurate construction of the geometric shape of the geological block, providing an accurate and closed solid model foundation for subsequent discretization processing, thereby ensuring the geometric consistency and reliability of the entire attribute modeling process.

[0032] Step S103: Discretize the three-dimensional geological layer blocks in the sequence. Based on the thickness and adjacency relationship of each three-dimensional geological layer block, adaptively use cuboid elements or triangular prism elements to divide it into discrete element sets.

[0033] Specifically, the three-dimensional geological layer blocks are discretized to transform the continuous geological volume model into a set of discrete units consisting of a finite number of geometric elements, thus providing a spatial basis for subsequent attribute interpolation calculations. This process is based on a comprehensive analysis of the block's geometry and its spatial relationships, adaptively selecting the partitioning strategy and unit type.

[0034] Specifically, the system first identifies the thickness features of the input 3D geological layer block to determine whether it belongs to a thin layer and assesses its spatial adjacency with adjacent blocks. Based on the thickness identification results, the system employs appropriate subdivision algorithms for blocks of different thicknesses: for conventional blocks with larger thicknesses, a subdivision method based on spatial recursive decomposition is used to generate a discrete element set composed of cuboid elements, and local densification is performed in the block boundary region to ensure geometric fitting accuracy; for thin layers with smaller thicknesses, a subdivision method based on surface projection and normal extension is used to generate a discrete element set composed of triangular prism elements, thereby avoiding geometric penetration or element distortion caused by mismatch between element size and layer thickness.

[0035] Meanwhile, based on the results of the adjacency determination, the system merges multiple spatially adjacent blocks into a composite block before further subdivision, in order to eliminate potential gaps at the interlayer interface and reduce the total number of discrete units.

[0036] As one possible implementation, the system can first determine the thin layer of a single block. If it is determined to be a thin layer, a triangular prism partitioning algorithm based on the projection of the top surface and the extension of the normal direction is used; otherwise, an adaptive cuboid partitioning algorithm based on an octree is used. For multiple blocks, the system first determines the adjacency between blocks based on the half-side data structure, merges adjacent blocks, and then performs unified partitioning, thereby achieving efficient and high-precision discretization processing of complex geological layer sequences.

[0037] Therefore, through the above adaptive discretization process, this step can flexibly select the subdivision strategy and unit type according to the actual thickness and spatial adjacency of the geological block, which significantly improves the adaptability to complex geological conditions such as thin layers and multi-layers. While ensuring the geometric accuracy of the discrete unit, it effectively controls the number of units and computational overhead, thus providing a high-quality, seamless discretization model foundation for subsequent attribute interpolation.

[0038] Step S104: Based on the borehole point attribute data, perform attribute interpolation on each unit in the discrete unit set to generate a discrete unit set with geological attribute values.

[0039] Specifically, after discretizing the geological block, the generated set of discrete units needs to be assigned attribute values ​​that reflect the physical or chemical properties of the actual geological body, thereby constructing a three-dimensional attribute model with engineering application value. This step, based on known borehole point attribute data, performs attribute interpolation calculations on each unit in the set of discrete units. Its core lies in using spatial statistical or deterministic interpolation methods to estimate the attribute values ​​of the target unit's center point or representative point based on the attribute values ​​of surrounding known sample points.

[0040] Specifically, the system first acquires a set of discrete units generated by the aforementioned segmentation steps. Each unit has a defined spatial location and geometric shape. Simultaneously, it acquires sample point data with measured attribute values ​​distributed along the drilling path. Then, for each discrete unit, based on its spatial coordinates and the spatial relationship between it and the known sample points, the system calls a pre-defined interpolation algorithm model. By calculating parameters such as the spatial distance, direction, and variogram between the sample points and the target point, the system determines the contribution weight of each sample point to the target point's attribute value, and then performs a weighted summation to obtain the attribute estimate for that unit. This process can be executed independently for a single attribute field or processed in parallel for multiple attribute fields. Ultimately, one or more attribute values ​​are assigned to each discrete unit, forming a complete set of attribute-assigned discrete units.

[0041] As one possible implementation, the system can employ geostatistical methods such as the inverse distance weighting method or the kriging method, using the center point of the discrete unit as the interpolation target, and calculate based on the grade, porosity, or density of the surrounding borehole sample points, thereby assigning corresponding attribute estimates to each unit.

[0042] Therefore, this step, by combining the discretized geometric model with measured attribute data, achieves a complete mapping from geometric shape to attribute information. This enables the constructed three-dimensional model to not only possess spatial geometric accuracy but also to carry quantitative information reflecting the distribution patterns of attributes within the geological body, providing a reliable data foundation for subsequent engineering applications such as resource estimation, mining design, and numerical simulation.

[0043] Example 2 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S101, "generating a triangular meshed stratigraphic interface surface stored in a half-side data structure based on geological borehole data and seismic data".

[0044] In this embodiment, step S101 specifically includes the following processing procedures. First, the system reads geological borehole data from the mining area. This data includes borehole number, spatial coordinates, elevation, and stratigraphic lithology information at each borehole point. From this data, spatial point sets on various stratigraphic interfaces are extracted, such as the coordinates of discrete points on the roof and floor of the coal seam. Simultaneously, based on the stratigraphic and fault data obtained from seismic data interpretation, a dataset for each fault plane is established, providing basic input for subsequent surface generation.

[0045] Subsequently, the system employs geostatistical methods to spatially interpolate the elevation data of each stratigraphic interface to generate a three-dimensional spatial surface reflecting the spatial undulation trend of that interface. As an example, this geostatistical method can utilize one of the following: kriging, discrete smoothing interpolation, or radial basis function method. Kriging provides unbiased optimal estimation by considering spatial autocorrelation; discrete smoothing interpolation is suitable for handling irregularly distributed data points; and the radial basis function method can effectively fit complex surface morphologies.

[0046] After interpolation, the system performs triangulation on the generated continuous surface within a specified region, representing it as a set of triangular facets, thereby discretizing the continuous spatial surface into a computable three-dimensional mesh model.

[0047] Finally, the system uses a half-edge data structure to store and manage these triangular mesh surfaces. This data structure not only records the spatial coordinates of each vertex and the vertex index of each facet, but also stores the topological adjacency relationships between facests, such as the facet to which each half-edge belongs and its adjacent half-edge information, thereby providing efficient topological query support for subsequent operations such as stratigraphic adjacency determination and block construction.

[0048] Therefore, through the above steps, the system outputs a series of triangular meshed stratigraphic interface surfaces stored in a half-edge data structure, which serve as input for subsequent stratigraphic block construction. This implementation method enhances the system's adaptability to different data distribution characteristics by introducing various geostatistical interpolation methods, while also improving the efficiency and accuracy of topology processing by utilizing the half-edge data structure, thus laying a solid foundation for the automation and high-precision realization of mine geological attribute modeling.

[0049] Example 3 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S102, "constructing a three-dimensional geological block sequence defined by a triangular mesh surface model using paired stratigraphic interface surfaces and region of interest boundaries".

[0050] In this embodiment, step S102 is implemented in the following way. First, the system receives a triangular meshed stratigraphic interface surface stored in a half-edge data structure from the stratigraphic interface surface generation subsystem. This surface includes surfaces representing the top and bottom surfaces of a certain geological layer, and simultaneously obtains the boundary of a user- or system-preset region of interest polygon. The system uses the region of interest polygon to spatially trim the top and bottom surfaces respectively, retaining only the surface portion of the region inside the polygon, thereby obtaining the trimmed local surfaces of the top and bottom surfaces.

[0051] Next, the system stretches the polygon of the region of interest vertically to form a vertical cylinder. The side of the cylinder is the boundary surface, which is used to connect the edges of the trimmed top and bottom local surfaces, thus forming a closed volume space together with the top and bottom surfaces.

[0052] Subsequently, the system performs independent triangulation processing on the trimmed top surface, bottom surface, and boundary surface, generating corresponding top, bottom, and boundary triangular meshes. In one possible implementation, the triangulation processing can employ the Delaunay triangulation algorithm to ensure that the generated triangular patches have good shape quality.

[0053] Finally, the system merges the generated top, bottom, and boundary triangular meshes. This involves sharing vertices and edges on the boundaries to stitch these triangular meshes together into a complete, seamless closed triangular mesh surface model. This model represents the three-dimensional geological block of the geological layer. The data structure of this block surface model still uses a half-edge data structure to record vertices, edges, faces, and their topological adjacency relationships, providing efficient geometric query support for subsequent block subdivision processing.

[0054] Therefore, this embodiment ensures the spatial closure and geometric integrity of the constructed geological block surface model by stretching the polygon of the region of interest to form a cylindrical boundary surface and accurately splicing it with the trimmed top and bottom triangular meshes. This provides accurate and unambiguous geometric boundaries for subsequent block discretization processing, thereby avoiding subdivision errors caused by insufficient model closure.

[0055] Example 4 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S103 above, which involves "discretizing the three-dimensional geological layer blocks in the sequence, and adaptively using cuboid units or triangular prism units to divide the blocks according to their thickness and adjacency relationship, thereby generating a set of discrete units".

[0056] In this embodiment, when discretizing the three-dimensional geological layer blocks in step S103, the system first performs a preliminary analysis on the input blocks to determine whether they are a single block or a collection of multiple blocks, and routes them to the corresponding processing flow based on the determination result. Specifically, the system receives the three-dimensional geological layer block sequence defined by the triangular mesh surface model output from step S102, identifies the number of independent blocks it contains by parsing the structural information of the block data, and routes it to a single block processing flow if it contains only one block, and to a multiple block processing flow if it contains multiple blocks.

[0057] For a single geological block, the system applies virtual drilling technology to determine whether the current block is a thin layer. Specifically, several virtual drilling points are selected inside the block or on its boundary, and virtual drilling is performed along the vertical direction or the block's normal direction. The vertical distance between the top and bottom surfaces of the block or the distance along the normal direction along the drilling path is obtained. This distance is compared with a preset thin layer thickness threshold. If it is less than the threshold, it is determined to be a thin layer; otherwise, it is determined to be a thick layer. If it is determined to be a thin layer, a thin layer segmentation algorithm is invoked; if it is determined to be a thick layer, a thick layer segmentation algorithm is invoked.

[0058] For the thick-layer partitioning algorithm, the system first calculates the minimum bounding box of the block, using this bounding box as the initial root node, and recursively performs octree partitioning. During each partitioning process, an intersection algorithm between the cuboid and the block surface is used to accurately determine the positional relationship between each sub-unit and the block. This positional relationship includes three states: internal, external, and intersecting. If the sub-unit is located inside the block, it is marked as an internal unit, and the recursive partitioning continues until the preset minimum unit size is reached, and all descendant units are marked as internal units. If the sub-unit is located outside the block, it is discarded directly. If the sub-unit intersects with the block surface, the recursive partitioning continues until the preset minimum unit size is reached. Through the above processing, a discrete cuboid unit set that is dense near the boundary and uniform inside is finally generated.

[0059] For the thin-layer partitioning algorithm, the system first projects the top surface of the thin-layer block onto a two-dimensional plane. Based on the distribution of the normal vectors of the top surface, a quadtree partitioning is performed, dividing the top surface into a set of spatial quadrilaterals. Then, by applying virtual drilling technology along the opposite direction of the normal direction of each vertex of each spatial quadrilateral, the corresponding points of each vertex are obtained on the bottom surface, and a set of spatial quadrilaterals constituting the bottom surface is generated based on these points. Finally, each spatial quadrilateral on the top and bottom surfaces is represented by two triangles, thus discretizing the thin-layer block into a set of triangular prism elements.

[0060] When modeling multiple strata, the system determines whether any two blocks are spatially adjacent based on the half-side data structure generated in step S101. The criteria are: whether the two blocks share the same geological interface, or whether the average distance between their adjacent surfaces is less than a preset tolerance ε. If either of these conditions is met, the blocks are considered adjacent. The system merges multiple blocks determined to be adjacent into a composite block. The merging operation eliminates the common interfaces between them, forming a larger modeling unit. For the composite block formed after merging, or multiple non-adjacent independent blocks selected by the user, the system calls the single block subdivision function mentioned above to discretize them separately, and finally outputs a set of discrete units.

[0061] Therefore, this embodiment significantly improves the accuracy and efficiency of discretization under complex geological conditions by intelligently determining the block type and adaptively calling the corresponding subdivision algorithm. At the same time, by merging adjacent blocks, it eliminates interlayer gaps from the source, ensuring the continuity and quality of the model.

[0062] Example 5 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S104, "according to the borehole point attribute data, perform attribute interpolation on each unit in the discrete unit set to generate a discrete unit set with geological attribute values".

[0063] In this embodiment, step S104 specifically implements the attribute assignment process for the discrete element set. The input to this step comes from the discrete element set generated in step S103. Each element in this set can be a cuboid element or a triangular prism element. The input also includes the original borehole point attribute data, such as geological attribute values ​​like grade, porosity, and density. For each discrete element in the discrete element set, the system first calculates its geometric center point, which serves as the spatial location reference for subsequent attribute interpolation. Specifically, for each discrete element, the system extracts the spatial coordinates of all its vertices and determines the center point coordinates of the element by calculating the arithmetic mean of the coordinates of each vertex.

[0064] Subsequently, the system invokes a geological attribute interpolation algorithm to calculate the attribute estimate of the center point based on the known attribute data of the borehole points surrounding it. In one possible implementation, the geological attribute interpolation algorithm used is the inverse distance weighting method. This method uses the reciprocal of the spatial distance as a weight to perform a weighted average of the attribute values ​​of the surrounding known borehole points, assigning higher weights to borehole points closer to the center point, thereby calculating the attribute estimate of the center point.

[0065] In another possible implementation, the geological attribute interpolation algorithm used is the Kriging method, which uses a variogram model to describe the spatial correlation of attribute values ​​and calculates the attribute estimate of the center point through unbiased optimal estimation.

[0066] Then, the system assigns the calculated attribute estimate to the discrete unit as its geological attribute value. By traversing all units in the discrete unit set, the system assigns a corresponding attribute value to each unit, and finally outputs a set of discrete units with geological attribute values, thus completing the 3D attribute modeling with attribute assignment.

[0067] Therefore, this embodiment calculates the center point for each discrete unit and calls the geological attribute interpolation algorithm to assign attribute values, thereby achieving accurate attribute filling of the discrete unit set, ensuring the spatial continuity and accuracy of the attribute model, and thus providing a reliable data foundation for subsequent 3D visualization and resource estimation.

[0068] Example 6 In this embodiment, after generating a set of discrete units with geological attribute values, the method further includes: determining the display color for each discrete unit according to a user-defined attribute value and color mapping table; drawing all the discrete units with displayed colors using computer graphics technology to generate a three-dimensional attribute color map of the block; and providing the user with interactive functions for rotating, scaling, translating, profile cutting, and attribute querying of the three-dimensional attribute color map.

[0069] Specifically, this embodiment is implemented as a visualization and interactive processing method for a three-dimensional geological attribute model. First, the system receives a set of discrete units with geological attribute values ​​output from step S104. Each discrete unit in the set contains its spatial coordinates, geometric dimensions, and geological attribute values ​​calculated by interpolation, such as grade, porosity, or density.

[0070] Subsequently, the system determines the corresponding display color for each discrete unit based on a user-predefined attribute value-color lookup table. Specifically, this lookup table defines the mapping relationship between attribute value ranges and RGB color values. The system iterates through all discrete units, reads the geological attribute values ​​of each unit, and searches for matching color values ​​in the lookup table, thereby assigning each unit a unique display color.

[0071] After completing the color mapping, the system uses computer graphics technology to draw all discrete units in the 3D scene. During the drawing process, the system constructs a 3D geometric model of each discrete unit based on its geometric type (cubic prism or triangular prism) and vertex coordinates, and performs shading and rendering in combination with the determined display colors, ultimately generating a complete 3D attribute shading map that intuitively shows the distribution pattern of geological attributes in space.

[0072] Building upon this foundation, the system integrates various interactive functions to support in-depth model analysis. Specifically, by listening to user input events, the system provides rotation, scaling, and translation operations, allowing users to observe the model from any angle and distance. Furthermore, the system offers a profile cutting function, allowing users to define cutting planes in any direction. The system calculates and displays the intersection of the cutting plane and the set of discrete elements in real time, revealing the details of the model's internal attribute distribution. Simultaneously, the system supports attribute query functionality. When a user selects a specific discrete element in the model by clicking or using a selection box, the system reads the element's stored spatial coordinates, dimensions, and attribute values, displaying them in the user interface. Through these processes, this embodiment transforms abstract attribute data into a visualized 3D model and provides rich interactive analysis tools.

[0073] Therefore, this embodiment uses color mapping and 3D rendering to intuitively present discrete attribute data as a 3D colored map. Combined with interactive functions such as rotation, scaling, translation, profile cutting, and attribute query, it significantly improves the user's understanding and analysis efficiency of the spatial distribution patterns of geological attributes, providing an intuitive and efficient decision support tool for mine resource assessment and mining design.

[0074] Example 7 In this embodiment, after providing users with interactive functions such as rotation, scaling, translation, section cutting, and attribute query of the 3D attribute shaded map in the previous embodiment, it further includes: exporting the spatial coordinates, dimensions, and attribute values ​​of discrete units into a standard format file; automatically generating a report containing modeling parameters, unit statistical information, and resource quantity estimation results based on the standard format file; and saving the current 3D view as an image or recording it as a demonstration video.

[0075] Specifically, this embodiment implements the standardized output and report generation function of attribute modeling results. The input for this step comes from the discrete unit set with geological attribute values ​​generated in step S104 above, and the three-dimensional visualization view generated in embodiment 6.

[0076] First, the system iterates through the set of discrete units, extracting the spatial coordinates, dimensions in three dimensions, and assigned geological attribute values ​​of each discrete unit, and then organizes these data into structured data records according to preset field formats.

[0077] Subsequently, the system calls the data export module to write these records into standard format files, such as CSV text files, VTK mesh data files, or Mesh finite element mesh files, thereby generating data files that can be directly read and used by other professional software. Simultaneously, the system automatically initiates the report generation process. This process first reads the parameter information recorded during the modeling process, including the type of interpolation algorithm used, search radius, and variation function model; then, it performs statistical analysis on the discrete element set, calculating the total number of elements, the distribution range of each attribute value, mean, variance, and other statistical information.

[0078] Finally, based on the volume and attribute values ​​of each unit, resource estimation is performed, such as calculating the average grade and total metal content of the ore body. The system integrates the above modeling parameters, unit statistics, and resource estimation results to automatically generate formatted report files, such as PDF or Excel files.

[0079] In addition, the system simultaneously performs visualization output operations, capturing the rendered image in the current 3D view window and saving it as a high-resolution image file, or recording the interactive operations such as rotation and scaling of the 3D model as a demonstration video file through continuous frame capture and encoding technology. Through the above processing, this embodiment exports the spatial coordinates, dimensions, and attribute values ​​of discrete units into standard format files, automatically generates reports containing modeling parameters, unit statistical information, and resource estimation results, and saves the current 3D view as an image or records it as a demonstration video, thus completing the complete conversion from attribute model to output results in multiple formats.

[0080] Therefore, this embodiment achieves multi-format and standardized output of attribute modeling results through automated data export, report generation and visualization output processes, which significantly improves the usability and shareability of modeling results and facilitates subsequent resource assessment, engineering design and results reporting.

[0081] Example 8 This embodiment will describe in detail the complete implementation of the method for modeling geological attributes in mines. This method decomposes the complex process of geological attribute modeling into multiple logically clear and functionally specialized processing stages through a modular and pipelined processing flow. Each stage is connected through a standard interface, and data is automatically transmitted. It also integrates intelligent judgment and processing algorithms for special geological conditions such as thin layers and multi-layer layers, thereby achieving efficient, high-precision, and integrated attribute modeling for various types of mine geological bodies.

[0082] In this embodiment, the first step is to generate a stratigraphic interface surface. The system reads geological borehole data from the mining area, which includes borehole number, coordinates, elevation, and stratigraphic lithology information for each borehole point. Simultaneously, it reads stratigraphic and fault data obtained through seismic data interpretation. The system extracts spatial point sets from each stratigraphic interface from the borehole data, such as points on the coal seam roof and floor, and establishes datasets for each fault plane based on the stratigraphic and fault data obtained from seismic data interpretation. Subsequently, geostatistical methods are used to spatially interpolate the elevation data of each stratigraphic interface, generating a three-dimensional spatial surface reflecting the spatial undulation trend of that interface. These geostatistical methods include Kriging, discrete smoothing interpolation, or radial basis function methods. The interpolated continuous surface is triangulated within a specified region, representing it as a set of triangular facets. To support subsequent advanced processing, this embodiment innovatively uses a half-edge data structure to store and manage these triangular mesh surfaces. This structure not only records vertex and facet information but also stores the topological adjacency relationships between facets, providing efficient support for subsequent functions such as stratigraphic adjacency determination. The output of this step is a series of triangularly meshed formation interface surfaces stored in a half-edge data structure.

[0083] Next, the stratigraphic block construction step is executed. The system utilizes paired stratigraphic interface surfaces from the previous step, such as the top and bottom surfaces of a coal seam, and the user- or system-defined region of interest (ROI) boundary, to construct a three-dimensional closed block representing the actual geological strata. Specifically, the top and bottom interface surfaces are trimmed using the ROI polygon, retaining only portions within the region; the ROI polygon is extruded vertically to form a cylinder, and the boundary surface of this cylinder is used to connect the trimmed top and bottom surfaces, thus forming a closed volume space; the top, bottom, and boundary surfaces of the trimmed block are triangulated, creating triangular meshes for the top, bottom, and boundary surfaces; these meshes are then merged to form a complete stratigraphic block surface model, which is still a set of triangular facets. The output of this step is an independent sequence of three-dimensional geological layer blocks, each block defined by its surface triangular mesh model.

[0084] Subsequently, the stratigraphic block segmentation step is executed. This is the core processing unit of this method, responsible for discretizing the geological block into a series of regular or irregular volumetric units, laying the foundation for attribute modeling. This step includes three functional modules: block selection and composition determination, single block segmentation, and multiple block segmentation. In the block selection and composition determination function, the user selects the stratigraphic blocks to be modeled according to the needs of mine production operations. The system performs a preliminary analysis of the input blocks, determining whether they are single blocks or a collection of multiple blocks, and routes the appropriate processing flow based on the determination result. When discretizing a single geological block, virtual drilling technology is first applied to determine whether the current block is a thin layer. If so, the thin-layer segmentation algorithm is called; otherwise, the thick-layer segmentation algorithm is called. For non-thin conventional blocks, an adaptive cuboid partitioning algorithm based on octrees is used for volume element partitioning. This function calculates the bounding box of the block, recursively performs octree partitioning, and uses a cuboid-block surface intersection algorithm to accurately determine the positional relationship between the sub-units and the block. This positional relationship includes internal, external, and intersection. Based on this positional relationship, it is determined whether to continue partitioning, and finally a set of discrete cuboid units that are encrypted near the boundary and uniform inside is generated. For the identified thin geological layer blocks, a partitioning algorithm for thin layers is applied. This function projects the top surface of the thin layer onto a two-dimensional plane and performs quadtree partitioning based on the distribution of the normal vectors of the top surface, dividing the top surface into a set of spatial quadrilaterals. By applying virtual drilling technology along the opposite direction of the normal direction of each quadrilateral vertex, the corresponding points of each vertex are obtained on the bottom surface, and a set of spatial quadrilaterals constituting the bottom surface is generated. Each spatial quadrilateral on the top and bottom surfaces is represented by two triangles, thereby discretizing the thin layer block into a set of triangular prism elements, avoiding the error of discrete elements penetrating the stratum surface caused by traditional cuboid element discretization. When multiple strata need to be modeled, the system determines whether any two blocks are spatially adjacent based on the aforementioned generated half-side data structure. The criteria for judgment are whether the two blocks share the same geological interface or whether the average distance between their adjacent surfaces is less than the preset tolerance ε. Multiple blocks that are determined to be adjacent are merged into a larger composite block. The merging operation eliminates the common interface between them and merges them into a larger modeling unit, which fundamentally avoids the interlayer gaps that may be generated during subsequent discretization and reduces the number of entities that need to be discretized. For the composite block formed after merging, or multiple non-adjacent independent blocks selected by the user, the single block partitioning function is called to discretize them separately.

[0085] Next, the stratigraphic block attribute interpolation step is performed. The system receives a set of discrete elements from the previous step, including cuboid or triangular prism elements, and the original borehole point attribute data, including grade, porosity, density, etc. For each discrete element, typically its center point, the system calls a geological attribute interpolation algorithm to calculate the attribute estimate for that point based on the surrounding known borehole attribute data, and assigns this value to the corresponding discrete element. The geological attribute interpolation algorithm includes the inverse distance weighting method or the kriging method. The output of this step is a set of discrete elements with geological attribute values, thus completing the attribute-assigned three-dimensional attribute model.

[0086] Next, the attribute modeling and 3D visualization step is performed. The system receives a set of discrete units with attribute values ​​from the previous step, determines the display color for each discrete unit according to the user-defined attribute value-color lookup table, draws all discrete units using computer graphics technology, and generates a 3D attribute color map of the block. It also provides interactive functions such as rotation, scaling, translation, profile cutting, and attribute querying, dynamically and interactively displaying the 3D geological attribute model in the system interface.

[0087] Finally, the attribute modeling output step is executed. The system receives the complete 3D attribute model from the attribute interpolation step and the visualization results from the visualization step. It can export information such as the spatial coordinates, dimensions, and attribute values ​​of discrete elements into standard format files, including CSV, VTK, and Mesh files; automatically generate reports containing modeling parameters, element statistics, and resource estimation results; save the current 3D view as an image or record it as a demonstration video; and support linkage with the 3D visualization module. For example, clicking on an element record in a table will highlight the corresponding element in the 3D view.

[0088] Therefore, this embodiment integrates the originally scattered and manual modeling process into a coherent method through the above-mentioned fully automated and standardized processing steps, realizing integrated processing from raw data to the final model. The innovative functions embedded in the system, such as thin-layer intelligent judgment and automatic identification and merging of adjacent layers, can automatically adapt to complex geological conditions such as thin layers and multi-layer sequences. The adjacent block merging function eliminates gaps between multi-layer models from the source. The adaptive octree partitioning algorithm and the dedicated thin-layer partitioning algorithm ensure the accuracy of the discretization of various blocks, thereby guaranteeing the accuracy and reliability of complex geological environment attribute modeling.

[0089] To implement the above embodiments, this application also proposes a mine geological attribute modeling system, such as... Figure 2 As shown, the system includes: The formation interface surface generation subsystem 100 is used to generate triangular meshed formation interface surfaces stored in a half-side data structure based on geological borehole data and seismic data.

[0090] The stratigraphic block construction subsystem 200 is used to construct a sequence of three-dimensional geological block shapes defined by a triangular mesh surface model using paired stratigraphic interface surfaces and region of interest boundaries.

[0091] The stratigraphic block decomposition system 300 is used to discretize three-dimensional geological block in a sequence. Based on the thickness and adjacency of each three-dimensional geological block, it adaptively uses cuboid or triangular prism elements to generate a set of discrete elements.

[0092] The stratigraphic block attribute interpolation subsystem 400 is used to perform attribute interpolation on each unit in the discrete unit set based on borehole point attribute data, generating a discrete unit set with geological attribute values.

[0093] The Attribute Modeling 3D Visualization Subsystem 500 is used to perform 3D visualization rendering of discrete unit sets with geological attribute values ​​and provides users with a variety of interactive operations.

[0094] The attribute modeling output subsystem 600 is used to export the results of mine geological attribute modeling and to link with external systems.

[0095] Specifically, the stratigraphic interface surface generation subsystem 100 is responsible for calculating and generating three-dimensional spatial surfaces representing inter-layer interfaces based on raw, discrete geological borehole data and seismic data. The input to this subsystem is: geological borehole data of the mining area, including borehole number, coordinates, elevation, and stratigraphic lithology information for each borehole point, as well as stratigraphic and fault data obtained through seismic data interpretation. The output of this subsystem is: a series of triangularly meshed stratigraphic interface surfaces stored in a half-edge data structure.

[0096] The stratigraphic block construction subsystem 200 utilizes two stratigraphic interface surfaces (upper and lower) to construct three-dimensional closed blocks representing actual geological layers. The input to this subsystem is a pair of stratigraphic interface surfaces from subsystem 100 (such as the top and bottom surfaces of a coal seam), and the user-defined or system-defined boundary of the region of interest. The output of this subsystem is an independent sequence of three-dimensional geological layer blocks, each block defined by a triangular mesh model of its surface.

[0097] The stratigraphic block decomposition system 300 is responsible for discretizing geological blocks into a series of regular or irregular volume units, laying the foundation for attribute modeling. This subsystem includes three functional modules: 1) block selection and composition determination function; 2) single block decomposition function; 3) multiple block decomposition function.

[0098] The stratigraphic block attribute interpolation subsystem 400 assigns geological attribute values ​​to the discretized volume elements. The input to this subsystem is a set of discrete elements (cubic or triangular prisms) from subsystem 300, along with the original borehole point attribute data (such as grade, porosity, and density). The output of this subsystem is a set of discrete elements with assigned geological attribute values, thus completing the 3D attribute modeling with attribute assignment.

[0099] The attribute modeling 3D visualization subsystem 500 is responsible for rendering the attribute model in 3D, providing users with an intuitive and interactive viewing experience. The input to this subsystem is a set of discrete units with attribute values ​​from subsystem 400. The output of this subsystem is a dynamically and interactively displayed 3D geological attribute model in the system interface.

[0100] The Attribute Modeling Output Subsystem 600 is responsible for exporting the final modeling results in various formats for use by other software or for generating reports.

[0101] It should be noted that the aforementioned descriptions of the embodiments of the mine geological attribute modeling method also apply to the system of this application, and will not be repeated here.

[0102] In summary, the mine geological attribute modeling system of this application can realize the full-process automation and standardization of mine geological attribute modeling, improve modeling efficiency, and enhance the accuracy and continuity of the constructed model.

[0103] To implement the above embodiments, this application also proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the mining geological property modeling method described in any one of the first aspects of the embodiments above.

[0104] To implement the above embodiments, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the mine geological attribute modeling method as described in any one of the first aspects of the embodiments above.

[0105] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0106] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0107] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0110] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for modeling geological properties in mines, characterized in that, Includes the following steps: Based on geological borehole data and seismic data, a triangular meshed stratigraphic interface surface is generated and stored in a half-side data structure. Using the paired stratigraphic interface surfaces and the boundaries of the region of interest, a sequence of three-dimensional geological blocks defined by a triangular mesh surface model is constructed; The three-dimensional geological layer blocks in the sequence are discretized. Based on the thickness and adjacency of each three-dimensional geological layer block, cuboid or triangular prism elements are adaptively used to divide the block, generating a set of discrete elements. Based on the borehole point attribute data, attribute interpolation is performed on each unit in the discrete unit set to generate a discrete unit set with geological attribute values.

2. The method according to claim 1, characterized in that, After generating the set of discrete units with geological attribute values, the method further includes: The display color is determined for each discrete unit based on the attribute values ​​set by the user and the color lookup table. Using computer graphics techniques, all discrete units with displayed colors are drawn to generate a 3D attribute shading map of the block; Provide users with interactive functions such as rotation, scaling, translation, section cutting, and attribute querying of the three-dimensional attribute shaded map.

3. The method according to claim 2, characterized in that, After providing users with interactive functions for rotating, scaling, translating, sectioning, and querying attributes of the 3D attribute shaded map, the system further includes: Export the spatial coordinates, dimensions, and attribute values ​​of discrete elements to a standard format file; The system automatically generates a report containing modeling parameters, unit statistics, and resource estimation results based on the standard format file, and saves the current 3D view as an image or records it as a demonstration video.

4. The method according to claim 1, characterized in that, The generation of the triangular meshed stratigraphic interface surface stored in a half-edge data structure includes: Read the geological borehole data, extract the spatial point sets on the stratigraphic interfaces of each region, and establish datasets for each fault plane based on the stratigraphic and fault data obtained by interpreting the seismic data. Geostatistical algorithms are used to spatially interpolate the elevation data of each stratigraphic interface to generate a three-dimensional spatial surface that reflects the spatial undulation trend of each interface. The continuous surface generated by spatial interpolation is triangulated within a specified region to represent the continuous surface as a set of triangular patches. The triangular mesh surface is stored using a half-edge data structure, which records vertex, patch information and topological adjacency relationships between patches.

5. The method according to claim 1, characterized in that, The construction of the three-dimensional geological layer block sequence defined by the triangular mesh surface model includes: The paired formation interface surfaces are clipped using the region of interest, retaining a portion of the formation interface surface within the region of interest; The region of interest is stretched vertically to form a column, and the cut pairs of stratigraphic interface surfaces are connected through the boundary surface of the column to form a closed volume space. The top, bottom, and boundary surfaces of the volume space are triangulated to create triangular meshes for the top, bottom, and boundary surfaces respectively. The top triangular mesh, the bottom triangular mesh, and the boundary triangular mesh are merged to form a complete surface model of the stratigraphic block.

6. The method according to claim 1, characterized in that, The generation of the discrete unit set includes: The input target block is preliminarily analyzed to determine whether it is a single block or a collection of multiple blocks, and the process is routed to the corresponding processing flow based on the determination result. If the target block is a single block, virtual drilling technology is used to determine whether the current block is a thin layer. If it is, the thin layer subdivision algorithm is called; otherwise, the thick layer subdivision algorithm is called. When the target block is a collection of multiple blocks, the system determines whether any two blocks are spatially adjacent based on the half-side data structure. Multiple blocks that are determined to be adjacent are merged into a composite block. The composite block or multiple independent blocks that are not adjacent selected by the user are discretized using a single block partitioning algorithm.

7. The method according to claim 6, characterized in that, The invocation of the thick layer subdivision algorithm includes: Calculate the bounding box of the block, perform octree recursive partitioning on the bounding box, and perform intersection operation between the sub-cuboids split out in each recursive process and the surface of the block to determine the positional relationship between the sub-cuboids and the block. Based on the positional relationship, determine whether to continue subdividing until the size of the sub-cubic prism meets the preset requirements, generating a set of discrete cuboid units that are encrypted near the boundary and uniform inside.

8. The method according to claim 6, characterized in that, The invocation of the thin-layer subdivision algorithm includes: The thin top surface is projected onto a two-dimensional plane, and quadtree partitioning is performed based on the distribution of the normal vectors of the top surface to divide the top surface into a set of top surface space quadrilaterals. Along the opposite direction of the normal to each quadrilateral vertex, virtual drilling technology is applied to obtain the corresponding points of each vertex on the bottom surface, generating a set of quadrilaterals in the bottom space. Each spatial quadrilateral in the set of spatial quadrilaterals on the top surface and the set of spatial quadrilaterals on the bottom surface is represented by two triangles to discretize the thin-layer block into a set of triangular prism units.

9. The method according to claim 1, characterized in that, The step of performing attribute interpolation on each unit in the discrete unit set to generate a discrete unit set with geological attribute values ​​further includes: For the center point of each discrete unit, a geological attribute interpolation algorithm is invoked to calculate the attribute estimate of the center point based on the surrounding known borehole attribute data, and the attribute estimate is assigned to the corresponding discrete unit.

10. A mine geological attribute modeling system, characterized in that, The mine geological attribute modeling system, used for executing the mine geological attribute modeling method as described in any one of claims 1 to 9, comprises: The formation interface surface generation subsystem is used to generate triangular meshed formation interface surfaces stored in a half-edge data structure based on geological borehole data and seismic data. A stratigraphic block construction subsystem is used to construct a sequence of three-dimensional geological block structures defined by a triangular mesh surface model using pairs of the stratigraphic interface surfaces and the boundaries of the region of interest. The stratigraphic block decomposition system is used to discretize the three-dimensional geological block in the sequence. Based on the thickness and adjacency of each three-dimensional geological block, it adaptively uses cuboid or triangular prism elements to decompose the block and generate a set of discrete elements. The stratigraphic block attribute interpolation subsystem is used to perform attribute interpolation on each unit in the discrete unit set based on borehole point attribute data, and generate a discrete unit set with geological attribute values. The attribute modeling 3D visualization subsystem is used to perform 3D visualization rendering of the discrete unit set with geological attribute values ​​and provide users with a variety of interactive operations. The attribute modeling output subsystem is used to export the results of mine geological attribute modeling and to link with external systems.

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