Hole drilling data-based cloud and English rock type ore body construction method and system

Abstract

The invention belongs to the technical field of information, and discloses a cloud-English rock type ore body construction method and system based on drilling data, and the method comprises the steps: carrying out the boundary recognition processing according to the gradient change characteristics in an alteration intensity space distribution model, and if the alteration intensity gradient change rate of adjacent space points exceeds a preset boundary threshold value, carrying out the boundary recognition processing; if yes, determining that the position is an ore body boundary point, forming an ore body boundary surface by connecting all boundary points, and judging geometric morphology characteristics of the ore body; a three-dimensional reconstruction algorithm is adopted to carry out internal structure analysis on a space area inside the boundary surface of the ore body, mineral combination difference degrees of different positions inside are calculated through a heterogeneity analysis method, internal structure layers are divided according to difference degree distribution characteristics, and occurrence characteristics and spatial distribution rules inside the ore body are determined. According to the method, full-process automation from data preprocessing to three-dimensional space modeling is achieved, the depicting precision of the ore body space distribution characteristics is remarkably improved, and efficient and accurate technical support is provided for geological exploration and resource evaluation.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a method and system for constructing greisen-type ore bodies based on borehole data. Background Technology

[0002] This research focuses on constructing three-dimensional models of greisen-type ore bodies using borehole data, which is crucial for mineral resource exploration and development. Accurate ore body models directly impact resource estimation, mining planning, and economic benefits, and are a core element of efficient geological exploration. However, existing methods often suffer from model distortion when dealing with complex geological environments due to their inability to accurately capture the spatial distribution characteristics of the ore body, especially when facing greisen-type ore bodies with complex alteration features and spatial heterogeneity.

[0003] Current methods primarily rely on single borehole data analysis, neglecting the dynamic changes of ore bodies in three-dimensional space. In particular, the comprehensive analysis of the spatial distribution patterns and mineral assemblage characteristics of greisen alteration zones is insufficient, resulting in low accuracy in describing ore body boundaries and internal structures. For example, existing techniques often generate models by directly interpolating from scattered borehole data, failing to reflect the gradual changes in greisen alteration intensity at different depths and directions. This limitation prevents the models from accurately reconstructing the actual morphology of ore bodies in complex strata, thus affecting the reliability of subsequent mining designs.

[0004] The core challenge lies in extracting and integrating multidimensional geological information from limited borehole data to accurately describe the spatial occurrence of greisen-type ore bodies. First, the mineral assemblage characteristics and alteration intensity distribution contained in borehole data exhibit high spatial heterogeneity, making it difficult to capture their variation patterns using simple statistical methods. This heterogeneity leads to significant errors in the model's determination of the ore body's strike and dip angle. For example, in a mine exploration, borehole data might show a section of greisen alteration zone that is intense in the shallows but rapidly weakens at depth. If its spatial distribution pattern cannot be accurately analyzed, the generated ore body model may incorrectly estimate resource distribution. Second, the azimuth and dip angle information from the boreholes is not effectively integrated into the three-dimensional spatial analysis, resulting in an inability to fully reflect the geometric morphology and occurrence characteristics of the ore body. This lack of information integration makes it difficult for the model to achieve accurate spatial orientation in complex geological environments. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method and system for constructing greisen-type ore bodies based on borehole data. It achieves full automation from data preprocessing to three-dimensional spatial modeling, significantly improves the accuracy of depicting the spatial distribution characteristics of ore bodies, and provides efficient and accurate technical support for geological exploration and resource assessment.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for constructing greisen-type ore bodies based on borehole data, the method comprising:

[0008] Mineral assemblage feature information and alteration intensity distribution data in borehole data are obtained and preprocessed. At the same time, the azimuth and tilt angle parameters of each borehole point are extracted to obtain a standardized dataset containing spatial coordinates, mineral composition content and alteration intensity values.

[0009] Cluster analysis algorithms were used to classify the mineral assemblage characteristics in the standardized dataset and determine the spatial distribution range of different alteration zones.

[0010] The spatial distribution range of the alteration zone is calculated in three dimensions using a spatial interpolation algorithm. A three-dimensional coordinate transformation matrix is ​​constructed by combining the azimuth and tilt angle information of the borehole, and the continuous distribution function of the alteration intensity in three-dimensional space is obtained, resulting in a spatial distribution model of the alteration intensity that reflects the depth variation law.

[0011] Boundary identification is performed based on the gradient variation characteristics in the spatial distribution model of alteration intensity. By connecting all boundary points to form the ore body boundary surface, the geometric morphology of the ore body is determined.

[0012] A three-dimensional reconstruction algorithm is used to analyze the internal structure of the spatial region inside the boundary of the ore body. The heterogeneity analysis method is used to calculate the difference in mineral assemblage at different locations inside the ore body. Based on the distribution characteristics of the difference, the internal structural layers are divided to determine the occurrence characteristics and spatial distribution patterns of the ore body.

[0013] The occurrence characteristics parameters of the ore body are obtained, including strike angle, dip angle and dip angle values. The boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed by the three-dimensional modeling algorithm to construct a complete three-dimensional spatial occurrence model of the greisen type ore body, and obtain an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

[0014] Preferably, methods for classifying mineral assemblage characteristics in a standardized dataset using cluster analysis algorithms to determine the spatial distribution range of different alteration zones include:

[0015] The alteration type of each borehole point is calculated based on the alteration intensity threshold and the mineral assemblage similarity. If the alteration intensity exceeds the preset threshold and the mineral assemblage similarity is greater than 0.8, it is determined to be the same alteration zone type, and the spatial distribution range of different alteration zones is determined.

[0016] Preferred methods for obtaining a spatial distribution model of alteration intensity that reflects the variation in depth include:

[0017] Set the alteration intensity data point as (x i ,yi ,z i ,z(x i ,y i ,z i The continuous distribution function f(x,y,z) of alteration intensity is obtained by Kriging interpolation.

[0018] Based on the borehole azimuth angle α and inclination angle β, the data points are transformed into a unified three-dimensional coordinate system using the coordinate transformation matrix R, resulting in the spatial distribution model of alteration intensity:

[0019]

[0020] Where, λ i The weighting coefficients are calculated using Kriging interpolation.

[0021] Preferably, the method for determining the geometric morphology of the ore body by performing boundary identification processing based on the gradient variation characteristics in the spatial distribution model of alteration intensity and forming the ore body boundary surface by connecting all boundary points includes:

[0022] By dividing the space into grids, the intensity values ​​of spatial points are obtained from the alteration intensity data, and the intensity difference between adjacent spatial points is calculated to obtain the gradient change.

[0023] If the gradient change rate of an adjacent spatial point exceeds a preset threshold, the point is marked as a boundary point of the ore body, and a set of boundary points is generated.

[0024] The Delaunay triangulation algorithm is used to construct a triangular mesh from the set of boundary points to obtain the ore body boundary surface;

[0025] By calculating the topological properties of the triangular mesh, the vertex, edge, and face information of the boundary surface is extracted to generate a geometric description;

[0026] Based on the geometric morphology description, calculate the volume and surface area enclosed by the boundary surfaces to obtain the geometric parameters of the ore body;

[0027] By analyzing geometric parameters and the distribution of alteration intensity at spatial points, the spatial morphology classification of ore bodies is determined;

[0028] The K-means clustering algorithm is used to classify and group the spatial morphology of the ore bodies to obtain the distribution pattern of the ore body morphology.

[0029] Preferably, methods for calculating the degree of difference in mineral assemblages at different locations within the ore body using heterogeneity analysis, dividing the internal structural layers based on the distribution characteristics of the degree of difference, and determining the occurrence characteristics and spatial distribution patterns within the ore body include:

[0030] DH=N g *(∑(a i -aL ) 2 xM i 2 ) / (a L 2 xM L 2 );

[0031] Where, N g It is the number of groups, a i and a L These are the group and batch levels, M. i and M L These refer to the quality of the group and the batch. The batch is a special field that represents the entire cavedore pillar, i.e., the situation of assessing the inhomogeneity within a single cavedore point, or it represents a horizontal slice of the cavedore, i.e., the situation of determining the inhomogeneity during mine production.

[0032] Preferably, the method for constructing a complete three-dimensional spatial occurrence model of a greisen-type ore body by comprehensively modeling the boundary surface, internal structure, and occurrence characteristics using a three-dimensional modeling algorithm includes:

[0033] By automatically adding ore body trend lines and allowing manual editing, the ore body shape is controlled using intermediate encrypted outline lines;

[0034] Automatically add branch points and use hole-bound triangulation to automatically construct branches;

[0035] Quality control is introduced to reconstruct the initial model and optimize the mesh quality.

[0036] Preferably, methods for reconstructing the initial model and optimizing mesh quality by introducing quality control include:

[0037] Introduce evaluation indicators for the quality of triangular meshes: element size and degree of beautification;

[0038] Using mesh optimization technology, the surface morphology and resolution of the ore body are controlled. The function for the beautification degree of triangles is:

[0039]

[0040] Where r(T) is the radius of the incircle of triangle T, and R(T) is the radius of the circumcircle of triangle T. For the overall beautification degree of the mesh T(S), the global beautification function B(T(S)) is:

[0041]

[0042] Where |T(S)| is the number of triangles in the triangular mesh S.

[0043] The present invention also provides a greisen-type orebody construction system based on borehole data. The system is used to implement the aforementioned method and includes: a data preprocessing module, a cluster analysis module, a spatial interpolation module, a boundary recognition module, a three-dimensional reconstruction module, and a three-dimensional modeling module.

[0044] The data preprocessing module is used to acquire mineral assemblage feature information and alteration intensity distribution data in borehole data and preprocess them, while extracting the azimuth and tilt angle parameters of each borehole point to obtain a standardized dataset containing spatial coordinates, mineral composition content and alteration intensity values.

[0045] The clustering analysis module is used to classify the mineral assemblage characteristics in the standardized dataset using a clustering analysis algorithm, and to determine the spatial distribution range of different alteration zones.

[0046] The spatial interpolation module is used to perform three-dimensional spatial interpolation calculation on the spatial distribution range of the alteration zone through a spatial interpolation algorithm. It constructs a three-dimensional coordinate transformation matrix by combining the azimuth and tilt angle information of the borehole, obtains the continuous distribution function of the alteration intensity in three-dimensional space, and obtains a spatial distribution model of the alteration intensity that reflects the depth variation law.

[0047] The boundary identification module is used to perform boundary identification processing based on the gradient change characteristics in the spatial distribution model of alteration intensity, and to determine the geometric morphological characteristics of the ore body by connecting all boundary points to form the ore body boundary surface.

[0048] The three-dimensional reconstruction module is used to perform internal structure analysis on the spatial region inside the boundary surface of the ore body using a three-dimensional reconstruction algorithm, calculate the difference in mineral assemblage at different locations inside the ore body using a heterogeneity analysis method, divide the internal structure layers according to the distribution characteristics of the difference, and determine the occurrence characteristics and spatial distribution patterns inside the ore body.

[0049] The three-dimensional modeling module is used to obtain the occurrence characteristic parameters of the ore body, including strike angle, dip angle and dip angle values. Through the three-dimensional modeling algorithm, the boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed to construct a complete three-dimensional spatial occurrence model of the greisen-type ore body, and obtain an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] This invention discloses a three-dimensional spatial occurrence modeling method for greisen-type ore bodies based on borehole data. Addressing the challenge of accurately depicting the spatial distribution characteristics of ore bodies in geological exploration, this method standardizes raw borehole data, extracting features such as spatial coordinates, mineral composition, and alteration intensity. Combining cluster analysis and spatial interpolation algorithms, a three-dimensional model reflecting the continuous distribution of alteration intensity is constructed. Ore body boundaries are identified based on gradient variation features, and a three-dimensional reconstruction algorithm is used to analyze internal structural heterogeneity and classify structural layers. Finally, a three-dimensional modeling algorithm integrates boundary surfaces, internal structure, and occurrence characteristic parameters to generate an accurate oriented model of the greisen-type ore body. This invention automates the entire process from data preprocessing to three-dimensional spatial modeling, significantly improving the accuracy of depicting the spatial distribution characteristics of ore bodies and providing efficient and accurate technical support for geological exploration and resource assessment. Attached Figure Description

[0052] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of a method for constructing a greisen-type orebody based on borehole data according to an embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of a greisen-type orebody construction system based on borehole data, according to an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] like Figure 1 As shown, this invention provides a method for constructing greisen-type ore bodies based on borehole data, the method comprising:

[0059] Mineral assemblage feature information and alteration intensity distribution data in borehole data are obtained and preprocessed. At the same time, the azimuth and tilt angle parameters of each borehole point are extracted to obtain a standardized dataset containing spatial coordinates, mineral composition content and alteration intensity values.

[0060] Cluster analysis algorithms were used to classify the mineral assemblage characteristics in the standardized dataset and determine the spatial distribution range of different alteration zones.

[0061] The spatial distribution range of the alteration zone is calculated in three dimensions using a spatial interpolation algorithm. A three-dimensional coordinate transformation matrix is ​​constructed by combining the azimuth and tilt angle information of the borehole, and the continuous distribution function of the alteration intensity in three-dimensional space is obtained, resulting in a spatial distribution model of the alteration intensity that reflects the depth variation law.

[0062] Boundary identification is performed based on the gradient variation characteristics in the spatial distribution model of alteration intensity. By connecting all boundary points to form the ore body boundary surface, the geometric morphology of the ore body is determined.

[0063] A three-dimensional reconstruction algorithm is used to analyze the internal structure of the spatial region inside the boundary of the ore body. The heterogeneity analysis method is used to calculate the difference in mineral assemblage at different locations inside the ore body. Based on the distribution characteristics of the difference, the internal structural layers are divided to determine the occurrence characteristics and spatial distribution patterns of the ore body.

[0064] The occurrence characteristics parameters of the ore body are obtained, including strike angle, dip angle and dip angle values. The boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed by the three-dimensional modeling algorithm to construct a complete three-dimensional spatial occurrence model of the greisen type ore body, and obtain an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

[0065] In this embodiment, mineral assemblage feature information and alteration intensity distribution data are obtained from the borehole data. The original borehole data is standardized through a data preprocessing module, and the azimuth and tilt angle parameters of each borehole point are extracted to obtain a standardized dataset containing spatial coordinates, mineral composition content, and alteration intensity values, including:

[0066] The raw borehole data, including mineral composition, alteration intensity, and spatial coordinates, is obtained. By parsing the data format, the records of each borehole point are extracted to obtain a structured initial dataset.

[0067] A data preprocessing module was used to clean the structured initial dataset, removing missing and outlier values. The mineral composition content and alteration intensity values ​​were processed using the z-score normalization method to obtain a standardized numerical dataset.

[0068] Spatial coordinate information of each borehole point is extracted from the standardized numerical dataset, azimuth angle parameters and tilt angle parameters are calculated, and a set of spatial geometric parameters is obtained through three-dimensional vector transformation.

[0069] Based on the set of spatial geometric parameters and combined with the standardized numerical dataset, a comprehensive dataset containing spatial coordinates, mineral composition content, alteration intensity value, azimuth angle parameter and tilt angle parameter is constructed.

[0070] If the correlation coefficient between the mineral composition content and alteration intensity value in the comprehensive dataset is greater than the preset threshold, then the mineral assemblage features are dimensionality reduced by principal component analysis to obtain the dimensionality-reduced mineral feature vector.

[0071] Based on the dimensionality-reduced mineral feature vectors and combined with the set of spatial geometric parameters, the k-means clustering algorithm is used to classify the distribution of borehole points and obtain the clustering results of mineral assemblage features.

[0072] By analyzing the spatial correlation between borehole point distribution and mineral assemblage characteristics through clustering results, a standardized dataset containing spatial coordinates, mineral composition content, alteration intensity values, and cluster labels is generated.

[0073] In this embodiment, the method for classifying the mineral assemblage characteristics in a standardized dataset using a clustering analysis algorithm to determine the spatial distribution range of different alteration zones includes:

[0074] The alteration type of each borehole point is calculated based on the alteration intensity threshold and the mineral assemblage similarity. If the alteration intensity exceeds the preset threshold and the mineral assemblage similarity is greater than 0.8, it is determined to be the same alteration zone type, and the spatial distribution range of different alteration zones is determined.

[0075] In this embodiment, a three-dimensional spatial interpolation calculation is performed on the spatial distribution range of the alteration zone using a spatial interpolation algorithm. A three-dimensional coordinate transformation matrix is ​​constructed by combining the borehole's azimuth and tilt angle information to obtain a continuous distribution function of the alteration intensity in three-dimensional space. The method for obtaining a spatial distribution model of the alteration intensity reflecting the depth variation law includes:

[0076] Spatial interpolation algorithms are used to estimate values ​​at unknown locations from known discrete data points. Common spatial interpolation methods include Kriging, Inverse Distance Weighted (IDW), and Nearest Neighbor Interpolation.

[0077] Kriging interpolation is a statistical interpolation method that estimates spatial correlation using a semivariance function.

[0078] Semivariance function:

[0079]

[0080] Where, z(x) i ) is the position x i The observation value at point h is the distance, and N(h) is the number of point pairs at a distance of h.

[0081] Kriging estimates:

[0082]

[0083] Where, λ i These are the weighting coefficients, obtained by solving the following system of equations:

[0084]

[0085] Inverse distance weighting (IDW) is a simple interpolation method where the weights are proportional to the inverse of the distance. IDW formula:

[0086]

[0087] Where, d(x0,x i () are points x0 and x i The distance between them, where p is a power parameter, usually taking the value 2.

[0088] A three-dimensional coordinate transformation matrix is ​​constructed by combining the borehole's azimuth and inclination angles. Assuming the borehole's azimuth angle is α and its inclination angle is β, the coordinate transformation matrix is:

[0089]

[0090] The alteration intensity value obtained by spatial interpolation algorithm can be represented as a continuous function f(x,y,z).

[0091]

[0092] in, The alteration intensity value is calculated using an interpolation algorithm.

[0093] By interpolation and coordinate transformation, a continuous distribution model of alteration intensity in three-dimensional space can be obtained. Assuming f(x,y,z) is a continuous distribution function of alteration intensity, the model can be constructed through the following steps:

[0094] 1. Data interpolation: Use Kriging interpolation or inverse distance weighting to interpolate the known alteration intensity data to obtain f(x,y,z).

[0095] 2. Coordinate transformation: Based on the azimuth and inclination angle of the borehole, the data is transformed into a unified three-dimensional coordinate system using the coordinate transformation matrix R.

[0096] 3. Model Construction: Combining interpolation results and coordinate transformation, a spatial distribution model of alteration intensity is constructed.

[0097] Specifically, the alteration intensity data point is set as (x i ,y i ,z i ,z(x i ,y i ,z i The continuous distribution function f(x,y,z) of alteration intensity is obtained by Kriging interpolation.

[0098] Based on the borehole azimuth angle α and inclination angle β, the data points are transformed into a unified three-dimensional coordinate system using the coordinate transformation matrix R, resulting in the spatial distribution model of alteration intensity:

[0099]

[0100] Where, λ i The weighting coefficients are calculated using Kriging interpolation.

[0101] In this embodiment, the method for determining the geometric morphology of the ore body by performing boundary identification processing based on the gradient variation characteristics in the spatial distribution model of alteration intensity and forming the ore body boundary surface by connecting all boundary points includes:

[0102] By dividing the space into grids, the intensity values ​​of spatial points are obtained from the alteration intensity data, and the intensity difference between adjacent spatial points is calculated to obtain the gradient change.

[0103] If the gradient change rate of an adjacent spatial point exceeds a preset threshold, the point is marked as a boundary point of the ore body, and a set of boundary points is generated.

[0104] The Delaunay triangulation algorithm is used to construct a triangular mesh from the set of boundary points to obtain the ore body boundary surface;

[0105] By calculating the topological properties of the triangular mesh, the vertex, edge, and face information of the boundary surface is extracted to generate a geometric description;

[0106] Based on the geometric morphology description, calculate the volume and surface area enclosed by the boundary surfaces to obtain the geometric parameters of the ore body;

[0107] By analyzing geometric parameters and the distribution of alteration intensity at spatial points, the spatial morphology classification of ore bodies is determined;

[0108] The K-means clustering algorithm is used to classify and group the spatial morphology of the ore bodies to obtain the distribution pattern of the ore body morphology.

[0109] In this embodiment, the method for calculating the degree of difference in mineral assemblages at different locations within the ore body using heterogeneity analysis, dividing the internal structural layers based on the distribution characteristics of the degree of difference, and determining the occurrence characteristics and spatial distribution patterns within the ore body includes:

[0110] DH=N g *(∑(a i -a L ) 2 xM i 2 ) / (a L 2 xM L 2 );

[0111] Where, N g It is the number of groups, a i and a L These are the group and batch levels, M. i and M L These refer to the quality of the group and the batch. The batch is a special field that represents the entire cavedore pillar, i.e., the situation of assessing the inhomogeneity within a single cavedore point, or it represents a horizontal slice of the cavedore, i.e., the situation of determining the inhomogeneity during mine production.

[0112] In this embodiment, the method for constructing a complete three-dimensional spatial occurrence model of a greisen-type ore body by comprehensively modeling the boundary surface, internal structure, and occurrence features using a three-dimensional modeling algorithm includes:

[0113] By automatically adding ore body trend lines and allowing manual editing, the ore body shape is controlled using intermediate encrypted outline lines;

[0114] Automatically add branch points and use hole-bound triangulation to automatically construct branches;

[0115] Quality control is introduced to reconstruct the initial model and optimize the mesh quality.

[0116] Specifically, methods for reconstructing the initial model and optimizing mesh quality by introducing quality control include:

[0117] Introduce evaluation indicators for the quality of triangular meshes: element size and degree of beautification;

[0118] Using mesh optimization technology, the surface morphology and resolution of the ore body model are controlled. The size of a triangle is measured by its area and the radius of its circumcircle; the quality of a triangle is determined by the ratio of its inscribed radius to its circumcircle radius. The function for the aesthetic appeal of a triangle is as follows:

[0119]

[0120] Where r(T) is the radius of the incircle of triangle T, and R(T) is the radius of the circumcircle of triangle T. when When b(T) is equilateral, triangle T is the best-shaped triangle; the closer b(T) is to 0, the worse the quality of the triangle, and the flatter or more elongated its shape. To evaluate the overall beautification degree of the mesh T(S), a global beautification function B(T(S)) is defined:

[0121]

[0122] Here, |T(S)| represents the number of triangles in the triangular mesh S. Reducing the resolution of a triangular mesh involves removing nodes containing redundant information. Mesh quality control methods can be divided into two types: one is to keep the mesh point positions unchanged and modify the mesh topology to improve mesh quality; the other is to keep the mesh topology unchanged and move the mesh point positions to improve mesh quality. Simultaneously, edge and point disintegration techniques, as well as subdivision and refinement techniques, are used to optimize mesh quality.

[0123] Example 2

[0124] like Figure 2 As shown, the present invention also provides a greisen-type orebody construction system based on borehole data. The system is used to implement the method described in Embodiment 1. The system includes: a data preprocessing module, a cluster analysis module, a spatial interpolation module, a boundary recognition module, a 3D reconstruction module, and a 3D modeling module.

[0125] The data preprocessing module is used to acquire mineral assemblage feature information and alteration intensity distribution data from borehole data and preprocess them. At the same time, it extracts the azimuth and tilt angle parameters of each borehole point to obtain a standardized dataset containing spatial coordinates, mineral composition content, and alteration intensity values.

[0126] The clustering analysis module is used to classify the mineral assemblage characteristics in a standardized dataset using clustering analysis algorithms, and to determine the spatial distribution range of different alteration zones.

[0127] The spatial interpolation module is used to perform three-dimensional spatial interpolation calculations on the spatial distribution range of the alteration zone using a spatial interpolation algorithm. It constructs a three-dimensional coordinate transformation matrix by combining the borehole azimuth and tilt angle information, obtains the continuous distribution function of alteration intensity in three-dimensional space, and obtains a spatial distribution model of alteration intensity that reflects the depth variation law.

[0128] The boundary identification module is used to perform boundary identification processing based on the gradient change characteristics in the spatial distribution model of alteration intensity. By connecting all boundary points to form the ore body boundary surface, the geometric morphology characteristics of the ore body are determined.

[0129] The 3D reconstruction module is used to perform internal structure analysis on the spatial region inside the boundary surface of the ore body using a 3D reconstruction algorithm. It calculates the difference in mineral assemblage at different locations inside the ore body through heterogeneity analysis, divides the internal structure layers according to the distribution characteristics of the difference, and determines the occurrence characteristics and spatial distribution patterns inside the ore body.

[0130] The 3D modeling module is used to obtain the occurrence characteristic parameters of the ore body, including strike angle, dip angle and dip angle values. Through 3D modeling algorithms, the boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed to construct a complete 3D spatial occurrence model of the greisen-type ore body, resulting in an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

[0131] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for constructing greisen-type ore bodies based on borehole data, characterized in that, The method includes: Mineral assemblage feature information and alteration intensity distribution data in borehole data are obtained and preprocessed. At the same time, the azimuth and tilt angle parameters of each borehole point are extracted to obtain a standardized dataset containing spatial coordinates, mineral composition content and alteration intensity values. Cluster analysis algorithms were used to classify the mineral assemblage characteristics in the standardized dataset and determine the spatial distribution range of different alteration zones. The spatial distribution range of the alteration zone is calculated in three dimensions using a spatial interpolation algorithm. A three-dimensional coordinate transformation matrix is ​​constructed by combining the azimuth and tilt angle information of the borehole, and the continuous distribution function of the alteration intensity in three-dimensional space is obtained, resulting in a spatial distribution model of the alteration intensity that reflects the depth variation law. Boundary identification is performed based on the gradient variation characteristics in the spatial distribution model of alteration intensity. By connecting all boundary points to form the ore body boundary surface, the geometric morphology of the ore body is determined. A three-dimensional reconstruction algorithm is used to analyze the internal structure of the spatial region inside the boundary of the ore body. The heterogeneity analysis method is used to calculate the difference in mineral assemblage at different locations inside the ore body. Based on the distribution characteristics of the difference, the internal structural layers are divided to determine the occurrence characteristics and spatial distribution patterns of the ore body. The occurrence characteristics parameters of the ore body are obtained, including strike angle, dip angle and dip angle values. The boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed by the three-dimensional modeling algorithm to construct a complete three-dimensional spatial occurrence model of the greisen type ore body, and obtain an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

2. The method according to claim 1, characterized in that, Methods for classifying mineral assemblage features in standardized datasets using cluster analysis algorithms to determine the spatial distribution range of different alteration zones include: The alteration type of each borehole point is calculated based on the alteration intensity threshold and the mineral assemblage similarity. If the alteration intensity exceeds the preset threshold and the mineral assemblage similarity is greater than 0.8, it is determined to be the same alteration zone type, and the spatial distribution range of different alteration zones is determined.

3. The method according to claim 1, characterized in that, Methods for obtaining a spatial distribution model of alteration intensity that reflects the variation in depth include: Set the alteration intensity data point as (x i ,y i ,z i ,z(x i ,y i ,z i The continuous distribution function f(x,y,z) of alteration intensity is obtained by Kriging interpolation. Based on the borehole azimuth angle α and inclination angle β, the data points are transformed into a unified three-dimensional coordinate system using the coordinate transformation matrix R, resulting in the spatial distribution model of alteration intensity: Where, λ i The weighting coefficients are calculated using Kriging interpolation.

4. The method according to claim 1, characterized in that, Methods for determining the geometric morphology of ore bodies include: performing boundary identification based on gradient variation characteristics in the spatial distribution model of alteration intensity; forming ore body boundary surfaces by connecting all boundary points; and using methods to assess the geometric morphology of ore bodies. By dividing the space into grids, the intensity values ​​of spatial points are obtained from the alteration intensity data, and the intensity difference between adjacent spatial points is calculated to obtain the gradient change. If the gradient change rate of an adjacent spatial point exceeds a preset threshold, the point is marked as a boundary point of the ore body, and a set of boundary points is generated. The Delaunay triangulation algorithm is used to construct a triangular mesh from the set of boundary points to obtain the ore body boundary surface; By calculating the topological properties of the triangular mesh, the vertex, edge, and face information of the boundary surface is extracted to generate a geometric description; Based on the geometric morphology description, calculate the volume and surface area enclosed by the boundary surfaces to obtain the geometric parameters of the ore body; By analyzing geometric parameters and the distribution of alteration intensity at spatial points, the spatial morphology classification of ore bodies is determined; The K-means clustering algorithm is used to classify and group the spatial morphology of the ore bodies to obtain the distribution pattern of the ore body morphology.

5. The method according to claim 1, characterized in that, Methods for calculating the degree of difference in mineral assemblages at different locations within an ore body using heterogeneity analysis, and then classifying the internal structural layers based on the distribution characteristics of this degree of difference to determine the occurrence characteristics and spatial distribution patterns within the ore body include: DH=N g *(∑(a i -a L ) 2 xM i 2 ) / (a L 2 xM L 2 ); Where, N g It is the number of groups, a i and a L These are the group and batch levels, M. i and M L These refer to the quality of the group and the batch. The batch is a special field that represents the entire cavedore pillar, i.e., the situation of assessing the inhomogeneity within a single cavedore point, or it represents a horizontal slice of the cavedore, i.e., the situation of determining the inhomogeneity during mine production.

6. The method according to claim 1, characterized in that, Methods for constructing a complete three-dimensional spatial occurrence model of greisen-type ore bodies by comprehensively modeling boundary surfaces, internal structures, and occurrence features using three-dimensional modeling algorithms include: By automatically adding ore body trend lines and allowing manual editing, the ore body shape is controlled using intermediate encrypted outline lines; Automatically add branch points and use hole-bound triangulation to automatically construct branches; Quality control is introduced to reconstruct the initial model and optimize the mesh quality.

7. The method according to claim 6, characterized in that, Methods for incorporating quality control to reconstruct the initial model and optimize mesh quality include: Introduce evaluation indicators for the quality of triangular meshes: element size and degree of beautification; Using mesh optimization technology, the surface morphology and resolution of the ore body are controlled. The function for the beautification degree of triangles is: Where r(T) is the radius of the incircle of triangle T, and R(T) is the radius of the circumcircle of triangle T. For the overall beautification degree of the mesh T(S), the global beautification function B(T(S)) is: Where |T(S)| is the number of triangles in the triangular mesh S.

8. A system for constructing greisen-type ore bodies based on borehole data, the system being used to implement the method described in any one of claims 1-7, characterized in that, The system includes: a data preprocessing module, a cluster analysis module, a spatial interpolation module, a boundary recognition module, a 3D reconstruction module, and a 3D modeling module; The data preprocessing module is used to acquire mineral assemblage feature information and alteration intensity distribution data in borehole data and preprocess them, while extracting the azimuth and tilt angle parameters of each borehole point to obtain a standardized dataset containing spatial coordinates, mineral composition content and alteration intensity values. The clustering analysis module is used to classify the mineral assemblage characteristics in the standardized dataset using a clustering analysis algorithm, and to determine the spatial distribution range of different alteration zones. The spatial interpolation module is used to perform three-dimensional spatial interpolation calculation on the spatial distribution range of the alteration zone through a spatial interpolation algorithm. It constructs a three-dimensional coordinate transformation matrix by combining the azimuth and tilt angle information of the borehole, obtains the continuous distribution function of the alteration intensity in three-dimensional space, and obtains a spatial distribution model of the alteration intensity that reflects the depth variation law. The boundary identification module is used to perform boundary identification processing based on the gradient change characteristics in the spatial distribution model of alteration intensity, and to determine the geometric morphological characteristics of the ore body by connecting all boundary points to form the ore body boundary surface. The three-dimensional reconstruction module is used to perform internal structure analysis on the spatial region inside the boundary surface of the ore body using a three-dimensional reconstruction algorithm, calculate the difference in mineral assemblage at different locations inside the ore body using a heterogeneity analysis method, divide the internal structure layers according to the distribution characteristics of the difference, and determine the occurrence characteristics and spatial distribution patterns inside the ore body. The three-dimensional modeling module is used to obtain the occurrence characteristic parameters of the ore body, including strike angle, dip angle and dip angle values. Through the three-dimensional modeling algorithm, the boundary surface, internal structure and occurrence characteristics are comprehensively modeled and processed to construct a complete three-dimensional spatial occurrence model of the greisen-type ore body, and obtain an oriented model that can accurately reflect the spatial distribution characteristics of the ore body.

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