A refined 3D modeling method for thin vein ore bodies

Abstract

The present invention relates to the technical field of three-dimensional geological modeling, and in particular to a method for fine-grained three-dimensional modeling of thin-vein ore bodies. The present invention generates a sequence of ore body contour lines and outer boundary lines of the ore body by vectorizing geological maps such as given exploration line profiles, encrypts its line segment nodes through natural cubic spline function interpolation, and then fits corresponding biharmonic spline functions respectively. Based on the typical characteristics of thin-vein ore bodies, the scan lines in the sequence are adaptively and dynamically interpolated. The method achieves the generation of surfaces that meet specific smoothness conditions, which helps to ensure that the morphological changes of the model are more reasonable, while avoiding interlacing phenomena, effectively identifying detailed features of areas such as faults and boundaries, improving the accuracy of the model and the authenticity of the geological expression, thereby providing a more reliable fine-grained three-dimensional modeling method for thin-vein ore bodies for geological exploration and resource assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional geological modeling, and in particular to a method for fine three-dimensional modeling of thin-vein ore bodies. Background Art

[0002] A 3D geological model of an ore body is a closed surface, consisting of a top and bottom interface in three-dimensional space. For thin-vein ore bodies, modeling is primarily based on closed ore body contours and the outer boundary lines drawn by geologists using vector technology. These closed contours are split into two open contours along their endpoints, defining the upper and lower boundaries of the ore body. These upper and lower boundaries are combined with the outer boundary to form the data for the top and bottom interfaces of the ore body.

[0003] Traditional modeling techniques first increase the data density of the top and bottom interfaces, using methods such as discrete smooth interpolation (DSI) within the constraints of the orebody's outer boundary. Delaunay triangulation is then used to generate the top and bottom interfaces of the model. However, for thin-vein orebodies, this interpolation and densification process can locally result in interlaced data between the top and bottom interfaces, causing the top and bottom interfaces of the model to intersect and fail to accurately reflect the 3D morphology of the thin-vein orebody. Therefore, traditional modeling techniques have limitations in constructing 3D models of thin-vein orebodies.

[0004] The 3D modeling method based on incremental orebody simulation first uses traditional modeling techniques to establish the model's bottom interface. This function is then used to fit an elevation increment function by calculating the elevation difference between the orebody's lower boundary node and the corresponding upper boundary node. This function is used to determine the elevation increment of the model's bottom interface, which is then raised accordingly to form a new top interface. This method effectively avoids the problem of interlaced top and bottom interfaces during the modeling of thin-vein orebodies. However, this method assumes a strict vertical correspondence between the lower and upper boundary data. In reality, orebody contour nodes are often distributed along an inclined direction, not completely perpendicular to the Z axis. This skewed distribution can lead to errors in the calculation of elevation differences, affecting model accuracy and limiting the method's applicability.

[0005] Delaunay triangulation is an algorithm for 3D surface visualization that constructs a triangular mesh by connecting given node data. The algorithm aims to generate triangles that are as regular as possible, as regular triangles help generate smoother 3D model surfaces.

[0006] However, in actual modeling, the node data for ore body contours is typically arranged in a directional pattern in space. Existing modeling methods often fail to fully consider the importance of directional consistency between the new data and the original data when infilling data. When the orientation of a contour node differs significantly from that of its adjacent infill nodes, the resulting triangulated facets become more morphologically distinct from regular triangles, resulting in a decrease in the quality of the triangulated mesh in that area.

[0007] Therefore, it is necessary to provide a method for fine-grained three-dimensional modeling of thin-vein ore bodies that can ensure the geometric accuracy of the model and improve the geological expression ability and visual quality of the model, thereby providing a more reliable method for geological exploration and resource assessment. Summary of the Invention

[0008] In order to overcome the problems of the prior art, the present invention proposes a method for fine-grained three-dimensional modeling of thin-vein ore bodies that can ensure the geometric accuracy of the model and improve the geological expression ability and visual quality of the model, thereby providing a more reliable method for geological exploration and resource assessment.

[0009] A method for fine-grained three-dimensional modeling of a thin-vein ore body comprises the following steps:

[0010] Step 1: Based on the given exploration information, vectorize and generate the ore body contour line sequence and the ore body external boundary line and perform node encryption to generate the model top interface data and bottom interface data;

[0011] Step 2: Based on the top interface data and the bottom interface data of the model, the corresponding biharmonic spline functions are fitted respectively;

[0012] Step 3: Project the top interface data and the bottom interface data of the model onto the horizontal plane respectively. Then, under the range constraint of the outer boundary line of the ore body, generate a boundary-limited scan line sequence by the scan line method. Finally, perform adaptive dynamic interpolation on the scan lines in the sequence to generate two-dimensional directional interpolation encryption nodes within the top and bottom interface ranges.

[0013] Step 4: Determine the connection relationship between each node in the top and bottom interface data of the model;

[0014] Step 5: Merge the top and bottom interface data of the model and splice the corresponding triangular mesh node connection matrix to form a seamless ore body three-dimensional model.

[0015] Furthermore, the step one also includes: splitting the ore body contour line sequence into an ore body upper boundary line sequence and an ore body lower boundary line sequence, and encrypting the line segment nodes of the ore body upper boundary line sequence, the ore body lower boundary line sequence and the ore body outer boundary line in sequence through natural cubic spline function interpolation.

[0016] Furthermore, the step 1 further includes: combining the node-encrypted upper boundary line sequence of the ore body with the outer boundary line of the ore body into model top interface data, and combining the node-encrypted lower boundary line sequence of the ore body with the outer boundary line of the ore body into model bottom interface data.

[0017] Furthermore, the step 2 further includes:

[0018] Based on the model top interface data and the model bottom interface data, the surface fitting function is called respectively. The corresponding biharmonic spline function is calculated and the three-dimensional regular grid surface is reconstructed.

[0019] Furthermore, the step three also includes: the adaptive dynamic interpolation determines the scan line encryption interpolation density based on the ore body physical property parameters and data quality information.

[0020] Furthermore, the data confidence is calculated based on the multivariate linear regression model;

[0021] The multiple linear regression model is expressed as follows: in, 、 、 、 is the regression coefficient, is the sampling accuracy, is the representativeness of the sampled data, is the spatial coverage, To reduce sampling repeatability errors, the system is divided into N regions according to the distance from the outer boundary of the ore body, and the regression coefficient is determined for each region.

[0022] Furthermore, the scan line encryption interpolation density is calculated based on the parallel deep random forest according to the physical property parameters of the ore body and the data confidence, wherein the multi-granularity scanning stage fills the feature set after dimensionality reduction and randomly samples the feature subsequence after the sliding window scan.

[0023] Furthermore, step three also includes obtaining the elevation value of the edge encryption node, i.e., the first and last nodes of the boundary-limited scan line, by assigning a value to the length ratio of the edge encryption node to the segment boundary along the long axis of the horizontal projection plane. The calculation formula is as follows: .

[0024] Furthermore, the step 4 also includes assigning the elevation values ​​of the top and bottom interface data of the model to 0, and using the two-dimensional boundary to define the Delaunay triangulation function Generate corresponding triangular connection matrices respectively; based on the top and bottom interface data of the model and the triangular connection matrix, visualize the top and bottom interfaces of the model through triangular meshes.

[0025] Furthermore, the step five further includes:

[0026] The data sets and triangular connection matrices of the top and bottom interfaces of the model are directly spliced ​​together to generate a merged data set and a merged triangular connection matrix. Then, a fine three-dimensional model of the thin vein ore body with seamless splicing of the top and bottom interfaces of the model is generated through triangular mesh visualization.

[0027] The present invention has the following advantages: 1. By introducing the biharmonic spline function, the present invention can generate a surface that meets specific smoothness conditions, which helps to ensure that the morphological changes of the model are more reasonable, while avoiding interlacing phenomena, and improving the accuracy of the model and the authenticity of the geological expression.

[0028] 2. The present invention uses a scanning line algorithm to propose a method for generating an adaptive dynamic interpolation data set along a direction consistent with the given ore body data. This method performs dynamic interpolation based on the typical characteristics of thin-vein ore bodies. On the one hand, it can improve the quality of the local triangulation network of the model, thereby making the surface of the ore body three-dimensional model smoother, enhancing the model's visual effect and analysis accuracy, and on the other hand, it can effectively identify the detailed characteristics of areas such as faults and boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the directional interpolation process of the scan line method of the present invention.

[0030] Figure 2 This is a schematic diagram of the unencrypted ore body outline and the outer boundary line of the ore body in the present invention.

[0031] Figure 3 This is a schematic diagram of the encrypted ore body contour line and the outer boundary line of the ore body in the present invention.

[0032] Figure 4 This is the XY horizontal projection diagram of the biharmonic spline surface corresponding to the top and bottom interfaces of the model of the present invention.

[0033] Figure 5 Schematic diagram of the relationship between the scanning line direction and the scanning travel direction of the present invention.

[0034] Figure 6 Schematic diagram of the relationship between the original scanning line and the interpolated scanning line of the present invention.

[0035] Figure 7 Schematic diagram of directional encryption of the boundary-limited scan line sequence of the present invention.

[0036] Figure 8 This is a schematic diagram of the three-dimensional directional node encryption of the top and bottom interfaces of the present invention.

[0037] Figure 9 This is a schematic diagram of the three-dimensional visualization effect of the bottom interface of the model of the present invention.

[0038] Figure 10 This is a schematic diagram of the effect of the fine three-dimensional model of the thin vein ore body of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below with reference to specific examples. These examples are intended only to illustrate the present invention and are not intended to limit the scope of the present invention.

[0040] A method for fine-grained three-dimensional modeling of a thin-vein ore body, comprising:

[0041] Step 1: First, based on the given geological map such as the exploration line profile, manually vectorize it to generate a sequence of ore body contour lines and ore body outer boundary lines. Second, split the ore body contour line sequence into a sequence of ore body upper boundary lines and a sequence of ore body lower boundary lines.

[0042] On this basis, the upper and lower ore body boundary sequences, as well as the outer ore body boundary sequences, are interpolated using natural cubic spline functions to encrypt their line segment nodes. The encrypted upper ore body boundary sequence and the outer ore body boundary sequence are combined to form the model top interface data, while the encrypted lower ore body boundary sequence and the outer ore body boundary sequence are combined to form the model bottom interface data.

[0043] Step 2: Based on the top interface data and the bottom interface data of the model, the corresponding biharmonic spline functions are fitted respectively.

[0044] Step 3: Project the top and bottom interface data onto the horizontal plane respectively, and then generate a boundary-limited scan line sequence through the scan line method under the range constraint of the outer boundary line of the ore body. Finally, perform adaptive dynamic interpolation on the scan lines in the sequence to generate two-dimensional directional interpolation encryption nodes within the top and bottom interface range.

[0045] like Figure 1 As shown, for the above-mentioned two-dimensional directional interpolation encryption nodes, the biharmonic spline function of the model top interface and the biharmonic spline function of the model bottom interface fitted in step 2 are used to assign elevation values ​​to them respectively to generate the three-dimensional directional encryption point sets of the model top interface and bottom interface.

[0046] Step 4: First, clean the top and bottom interface data of the model, delete the redundant node data of the outer boundary line of the ore body, and use the optimized data to regenerate the outer boundary line of the ore body.

[0047] Secondly, the elevation values ​​of the model top interface data and the model bottom interface data are assigned to 0 and projected onto the two-dimensional XY plane. Then, the outer boundary line of the ore body is used as the boundary constraint condition, and the corresponding triangular mesh node connection matrix is ​​generated by the Delaunay triangulation criterion under boundary constraints to determine the connection relationship between each node in the model top interface and bottom interface data.

[0048] Therefore, according to the triangular mesh node connection matrix corresponding to the top interface and bottom interface data of the model, the top interface and the bottom interface of the ore body model can be visualized using the triangular mesh.

[0049] Step 5: The top and bottom interfaces of the ore body share the new outer boundary line. Therefore, the top and bottom interface data of the model are merged and the corresponding triangular mesh node connection matrix is ​​spliced ​​to form a seamless 3D ore body model. This model can accurately represent the ore body's morphological changes and has a high-quality surface.

[0050] Detailed process of step 1:

[0051] A1. Import geological maps such as exploration line profiles into modeling software such as Micromine and perform coordinate correction. Then, manually draw the profile ore body contour lines to generate a vectorized ore body contour line sequence.

[0052] Based on the relative orientation of the nodes of the ore body contour line and the experience of geologists, the position of the outer boundary point of the ore body strike is inferred, and then the endpoints on both sides of the ore body contour line and the strike boundary points are connected in sequence to generate a vectorized outer boundary line of the ore body.

[0053] A2. For the ore body contour line sequence, split it along the endpoints on both sides of each internal contour line to divide it into the ore body upper boundary line sequence and the ore body lower boundary line sequence.

[0054] A3. Import the segment node coordinate data of the ore body upper boundary line sequence, lower boundary line sequence and outer boundary line into MatLab.

[0055] For each upper boundary line, lower boundary line and outer boundary line of the ore body, the natural cubic spline function is fitted using the coordinates of the line segment nodes. , thereby generating the corresponding spline curve Then draw the function according to the curve Get the corresponding spline curve Control Points As an encryption node.

[0056] A4. The encrypted upper boundary line sequence of the ore body and the outer boundary line of the ore body constitute the top interface data of the model; the encrypted lower boundary line sequence of the ore body and the outer boundary line of the ore body constitute the bottom interface data of the model.

[0057] like Figure 2 and Figure 3 As shown, we can see the comparison of the effects before and after the densification of the upper boundary line sequence, the lower boundary line sequence and the outer boundary line of the ore body. The densified ore body contour line and the outer boundary line of the ore body are smoother, reducing geometric errors and visual defects.

[0058] Detailed process of step 2:

[0059] A1. Based on the top and bottom interface data of the model, call the built-in surface fitting function of MATLAB respectively Compute the corresponding biharmonic spline function.

[0060] A2. Reconstruct a three-dimensional regular mesh surface based on the biharmonic spline functions corresponding to the top and bottom interfaces, and project it onto the horizontal plane.

[0061] Depend on Figure 4 It can be seen that the top and bottom interfaces use the outer boundary line of the ore body as the common boundary. The boundaries between the two are clear, and there is no interpenetration phenomenon inside. A surface that meets specific smoothness conditions can be generated, which helps to ensure that the morphological changes of the model are more reasonable, while avoiding interpenetration phenomena, and improving the accuracy of the model and the authenticity of the geological expression.

[0062] Detailed process of step three:

[0063] A1. Project the model top interface data onto the horizontal plane. The horizontal projection line segment sequence of the upper boundary line of the ore body is a set of parallel straight line segments. The line segment direction vector is taken as the scan line direction vector v. It is known that the projection result of the upper boundary line sequence of the ore body is a series of parallel straight line segments. Therefore, the scan line direction vector is calculated by the following formula: v : Where, Horizontal coordinates of the first node of the upper boundary of the ore body; Coordinates of the tail node of the upper boundary of the ore body.

[0064] A2. Let the scan line sequence move in a direction perpendicular to the scan line, that is, the scan line moving direction vector It is calculated by the following formula :

[0065] In the above formula, we define , that is, the scan line sequence along The axis moves in the positive direction, and the calculation is , thereby obtaining the scanning line moving direction vector Initial value of , and then normalize it according to the following formula to obtain the unit vectorized scan line direction vector . like Figure 5 As shown, the relationship between the scan line direction vector and the scan line moving direction vector can be seen.

[0066] A3. Based on the horizontal projection line sequence of the upper boundary of the ore body, obtain the boundary-defining scan line sequence according to the following steps:

[0067] ① Expand the first and last nodes of each line segment in the horizontal projection line segment sequence of the upper boundary of the ore body along the scanning line direction, and connect the first and last nodes of each line segment after expansion to form the in-situ scanning line sequence. The calculation formula for the expanded coordinates of the first and last nodes of the horizontal projection line segment of the upper boundary of the ore body is as follows: Where, ② Assume that the coordinates of the first and last boundary points of any in-situ scan line are , the coordinates of the first node of the adjacent in-situ scanning line on the side of the scanning line moving direction are , the distance between the two is calculated by the following formula . Where, Determinant The absolute value of . ③ The distance between adjacent in-situ scan lines is known , set the scan line travel distance to , calculate the number of scan lines generated by the current in-situ scan line along the scanning direction according to the following formula . Then calculate the number of scan lines according to the above formula , recalculate the actual distance of the scan line : Starting from the current in-situ scan line, move along the scan line in the direction of the spacing generate Directed scan lines. The coordinates of the first and last nodes of the scan line can be calculated using the following formula: Where, The coordinates of the tail node of the i-th scan line; The coordinates of the tail node of the i-th scan line.

[0068] Repeat this step between each adjacent in-situ scan line to generate a scan line sequence. Figure 6 As shown, the relationship between the original scan line and the interpolated scan line sequence is relatively Figure 4 , rotate 20° counterclockwise.

[0069] ④ Sort the node coordinates of the projected line segments of the outer boundary of the ore body by X-axis and Y-axis respectively to obtain Coordinates and Secondly, the projected line segments of the outer boundary of the ore body are split into pairs of two nodes to generate a sequence of segmented outer boundary lines.

[0070] For each scan line in the scan line sequence, by traversing and searching the segmented external line sequence, the cross product of the line vector connecting the first and last nodes of each segmented external line and the line vector connecting its first and last nodes is calculated. The cross product calculation formula is as follows:

[0071] The cross product calculation formula is as follows: Where, The first and last nodes of the segment boundary; The first and last nodes of the scan line.

[0072] On this basis, the boundary-limited scan line is obtained according to the following principle: when the cross product of the directional scan line sequence and two or more segmented external lines is not 0, there are two or more intersections between the directional scan line and the external boundary line of the ore body. If the middle point of the intersection line satisfies and When , the line connecting the two intersection points is the boundary-limited scan line. The formula for calculating the intersection coordinates is as follows: Based on the above principles, the boundary-limited scan line sequence is obtained according to the cross product results of the line vectors connecting the first and last nodes of each scan line and the segmented outer boundary line.

[0073] A4、 Figure 7 As shown in the figure, based on the boundary-defined scan line sequence, the line segment nodes of each scan line are encrypted to generate a set of directional encrypted points on the projection interface. For the edge encrypted nodes, that is, the first and last nodes of the boundary-defined scan line, the elevation value is obtained by assigning the length ratio between them and the segment boundary along the long axis of the horizontal projection plane to ensure the consistency of the top and bottom interface boundaries of the model. The calculation formula is as follows: Then, use the top interface biharmonic spline function and the bottom interface biharmonic spline function to assign elevation values ​​to the internal directional encryption point set, and you can get the following: Figure 8 The three-dimensional directional encrypted point sets of the top and bottom interfaces of the model are shown.

[0074] Optionally, the present invention dynamically determines the scan line encryption interpolation density based on key ore body data to achieve dynamic interpolation of geological characteristics. Since the typical characteristics of thin-vein ore bodies are small thickness, unstable extension, and large grade fluctuations, in the existing method for fine three-dimensional modeling of thin-vein ore bodies, conventional interpolation methods usually adopt a uniform interpolation method, which cannot effectively identify the detailed characteristics of areas such as faults and boundaries, and cannot adjust the modeling parameters based on the reliability of sampling. Based on the problems existing in the above-mentioned prior art, the present invention dynamically determines the scan line encryption interpolation density based on key ore body data, and specifically includes the following steps:

[0075] a. Obtain ore body physical attribute parameters and data quality information;

[0076] The physical property parameters of the ore body include the ore body grade distribution gradient, the distance from the outer boundary line of the ore body, the ore body thickness change rate, and the structural curvature;

[0077] The ore body grade distribution gradient characterization refers to the spatial variation intensity of the useful element content in the ore. Its core feature is the significant mutation of grade within a unit distance, which is mathematically expressed as the gradient maximum area of ​​the grade function.

[0078] The data quality information includes sampling accuracy, such as drilling density, representativeness of sampling data, such as core sampling rate, spatial coverage, and sampling repeatability error.

[0079] b. Determine data confidence based on data quality information.

[0080] Optionally, calculate data confidence based on a multiple linear regression model;

[0081] The multiple linear regression model is expressed as follows: in, 、 、 、 is the regression coefficient, is the sampling accuracy, is the representativeness of the sampled data, is the spatial coverage, To achieve refined modeling of thin-vein ore bodies, the thin-vein ore bodies are divided into N regions according to their characteristics and their distance from the outer boundary of the ore body, and the regression coefficients are determined for each region.

[0082] Optionally, estimate regression coefficients based on the least squares method.

[0083] c. The scan line encryption interpolation density is obtained based on the parallel deep random forest calculation according to the physical property parameters of the ore body and the data confidence.

[0084] Includes feature dimensionality reduction, multi-granularity scanning, cascade forest construction, and load balancing stages;

[0085] Optionally, to ensure smoothness and balance of the data, the multi-granularity scanning stage of the present invention fills the feature set after dimensionality reduction and randomly samples the feature subsequence after the sliding window scan.

[0086] Compared with traditional deep neural network structures, parallel deep random forests have the following advantages:

[0087] In terms of algorithm performance, it is superior to traditional deep neural networks. It is easy to train and requires significantly fewer parameters than deep neural networks. The results of the algorithm are not very sensitive to the parameter settings, the hyperparameter settings are extremely robust, and it does not require the use of massive data for training. Even on small data samples, it can have good performance and relatively accurate experimental results. The algorithm is relatively efficient and scalable. It is suitable for parallel deployment and can achieve efficient parallel training. It does not require large-scale manual parameter setting and adjustment like the prediction method of neural networks.

[0088] Detailed process of step 4:

[0089] A1. For the top and bottom interface data of the model after directional encryption, delete the redundant line segment nodes in the outer boundary line of the ore body, that is, the isolated nodes without adjacent nodes along the scanning line direction.

[0090] A2. Reconnect the ore body outer boundary line segment nodes and edge encryption nodes in sequence within the model top interface data to obtain a new ore body outer boundary line.

[0091] A3. Assign the elevation values ​​of the top and bottom interfaces of the model to 0 again, and then use the two-dimensional boundary to define the Delaunay triangulation function in MatLab. Generate the corresponding triangular connection matrix respectively. Figure 9 As shown, the top interface and bottom interface of the model are then visualized directly through the triangular mesh according to the data of the top interface and bottom interface of the model and the triangle connection matrix.

[0092] Detailed process of step five:

[0093] like Figure 10 As shown, the data sets and triangular connection matrices of the top and bottom interfaces of the model are directly spliced ​​together to generate a merged data set and a merged triangular connection matrix. Then, a fine three-dimensional model of a thin-vein ore body with seamless splicing of the top and bottom interfaces of the model can be generated through triangular mesh visualization. The present invention can capture and accurately express the morphological changes of the ore body, and can effectively present both complex geometric shapes and subtle geological features. Moreover, the application is not limited to a specific type of ore body. Whether it is a thick ore body or a thin-vein ore body, effective three-dimensional modeling can be obtained through the method of the present invention, showing its wide applicability.

[0094] The above embodiments describe preferred embodiments of the present invention in detail, but the present invention is not limited thereto. Within the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, including combining the various technical features in any other appropriate manner. These simple modifications and combinations should also be regarded as the contents disclosed by the present invention and fall within the scope of protection of the present invention.

Claims

1. A method for fine-grained three-dimensional modeling of thin-vein ore bodies, characterized in that: The steps include: Step 1: Based on the given exploration information, vectorize and generate the ore body contour line sequence and the ore body external boundary line and perform node encryption to generate the model top interface data and bottom interface data; Step 2: Based on the top interface data and the bottom interface data of the model, the corresponding biharmonic spline functions are fitted respectively; Step 3: Project the top interface data and the bottom interface data of the model onto the horizontal plane respectively. Then, under the range constraint of the outer boundary line of the ore body, generate a boundary-limited scan line sequence by the scan line method. Finally, perform adaptive dynamic interpolation on the scan lines in the sequence to generate two-dimensional directional interpolation encryption nodes within the top and bottom interface ranges. Step 4: Determine the connection relationship between each node in the top and bottom interface data of the model; Step 5: Merge the top and bottom interface data of the model and splice the corresponding triangular mesh node connection matrix to form a seamless ore body three-dimensional model.

2. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 1, characterized in that: The step 1 further includes: splitting the ore body contour line sequence into an ore body upper boundary line sequence and an ore body lower boundary line sequence, and encrypting the line segment nodes of the ore body upper boundary line sequence, the ore body lower boundary line sequence and the ore body outer boundary line in sequence through natural cubic spline function interpolation.

3. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 2, characterized in that: The step 1 further includes: combining the upper boundary line sequence of the ore body after node encryption and the outer boundary line of the ore body into model top interface data, and combining the lower boundary line sequence of the ore body after node encryption and the outer boundary line of the ore body into model bottom interface data.

4. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 1, characterized in that: The second step also includes: Based on the model top interface data and the model bottom interface data, the surface fitting function is called respectively. The corresponding biharmonic spline function is calculated and the three-dimensional regular grid surface is reconstructed.

5. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 1, characterized in that: The step three also includes: the adaptive dynamic interpolation determines the scan line encryption interpolation density based on the ore body physical property parameters and data quality information.

6. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 5, characterized in that: Calculate data confidence based on the multiple linear regression model; The multiple linear regression model is expressed as follows: in, 、 、 、 is the regression coefficient, is the sampling accuracy, is the representativeness of the sampled data, is the spatial coverage, To reduce sampling repeatability errors, the system is divided into N regions according to the distance from the outer boundary of the ore body, and the regression coefficient is determined for each region.

7. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 5, characterized in that: The scan line encryption interpolation density is calculated based on the parallel deep random forest according to the physical property parameters of the ore body and the data confidence, wherein the multi-granularity scanning stage fills the feature set after dimensionality reduction and randomly samples the feature subsequence after the sliding window scan.

8. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 7, characterized in that: Step 3 also includes obtaining the elevation value of the edge encryption node, that is, the first and last nodes of the boundary-limited scan line, by assigning the length ratio between the edge encryption node and the segment boundary along the long axis of the horizontal projection plane. The calculation formula is as follows: Where, (x1, y1) is the horizontal coordinate of the first node of the upper boundary of the ore body; (x2, y2) is the coordinate of the last node of the upper boundary of the ore body, mid It is the horizontal coordinate of the midpoint of the line connecting the intersection of the directional scanning line and the outer boundary line of the ore body.

9. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 1, characterized in that: The step 4 also includes assigning the elevation values ​​of the top and bottom interface data of the model to 0, and using the two-dimensional boundary to define the Delaunay triangulation function Generate corresponding triangular connection matrices respectively; based on the model top interface and bottom interface data and the triangular connection matrix, visualize the model top interface and model bottom interface through triangular mesh.

10. The method for fine three-dimensional modeling of a thin-vein ore body according to claim 1, characterized in that: The step five further includes: The data sets and triangular connection matrices of the top and bottom interfaces of the model are directly spliced ​​together to generate a merged data set and a merged triangular connection matrix. Then, a fine three-dimensional model of the thin vein ore body with seamless splicing of the top and bottom interfaces of the model is generated through triangular mesh visualization.

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