Tin ore prospecting method based on three-dimensional geological modeling

Abstract

The invention provides a tin ore prospecting method based on three-dimensional geological modeling, and relates to the technical field of geological exploration and mineral resource exploration, and the tin ore prospecting method comprises the steps: building a unified geological information database through multi-source geological data collection and fusion by using a weighted adaptive algorithm and a spatial registration technology, and building a three-dimensional geological geometric model; the method comprises the following steps: calculating and screening tin mineralization indexes of grid units by utilizing a space statistics and hot spot analysis method, determining candidate areas of a potential mining area, extracting mining characteristic parameters, establishing a mineralization probability exponential function, analyzing the mineralization potential of each grid unit, forming mineralization probability distribution of a three-dimensional space, and selecting an area with the highest mineralization probability as a drilling target point. According to the method, through multi-source geological data fusion and three-dimensional geological modeling, scientific and accurate recognition of the tin ore metallogenic target area and optimal selection of the drilling target point are achieved.

Description

Technical Field

[0001] This invention relates to the field of geological exploration and mineral resource exploration technology, specifically a tin ore prospecting method based on three-dimensional geological modeling. Background Technology

[0002] Tin ore, as an important non-ferrous metal resource, is widely used in industries such as electronics, metallurgy, and new materials. With the gradual depletion of shallow high-quality deposits, the exploration and development of tin resources faces the dual challenges of in-depth resource development and refined prospecting. Traditional prospecting methods mainly rely on geological surveys, drilling, and comprehensive analysis of single data sources, which are difficult to fully reflect the complex underground geological structure and mineralization patterns. The efficiency and accuracy of finding deep or concealed ore bodies are significantly limited. With the development of geological information technology and 3D modeling technology, 3D geological modeling based on the fusion of multi-source geological data has become an important means of mineral resource exploration. Multi-source data includes drilling data, geophysical data, geochemical information, and remote sensing images. These data come from diverse sources, have complex spatial distributions, and suffer from problems such as heterogeneity of data types, difficulties in spatial registration, and information redundancy. How to effectively integrate these heterogeneous data and achieve a high-precision, dynamically updated 3D geological model under a unified spatial coordinate system, as well as accurately locate mining targets, is the key to current mineral prospecting technology.

[0003] In existing technologies, traditional mineral exploration methods mainly rely on geological modeling based on isosurface methods or traditional interpolation methods. These models have limited accuracy and adaptability to complex geological bodies, making it difficult to accurately depict underground geological interfaces and mineralization control factors. Furthermore, existing mineralization probability assessments are mostly based on two-dimensional or partially three-dimensional models, lacking systematic extraction of mineralization characteristic parameters and cross-parameter correlation analysis, making it difficult to reflect the comprehensive effect of multiple factors controlling mineralization. In addition, most current mineral exploration methods only roughly determine the location of rocks and minerals without further refining the target mining point, increasing exploration risks and costs.

[0004] Therefore, it is necessary to propose a tin ore prospecting method based on three-dimensional geological modeling to solve the aforementioned problem.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a tin ore prospecting method based on three-dimensional geological modeling to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for tin ore exploration based on three-dimensional geological modeling, comprising the following steps: Step 1: Collect multi-source geological data obtained from drilling, geophysical exploration, geochemical exploration and remote sensing. Based on a weighted adaptive heterogeneous data fusion algorithm, fuse the multi-source geological data, and use a spatial registration algorithm to unify the coordinate system. Perform data cleaning and format standardization to establish a geological information database. Step 2: Based on the established geological information database, the implicit surface modeling technique with geological constraints is used, combined with the radial basis function interpolation algorithm and the geological elements in the database to construct a three-dimensional geological geometric model. Step 3: Divide the three-dimensional geological model into grid cells, and statistically analyze the geological elements in the three-dimensional geological model based on spatial statistical analysis to calculate the tin mineralization index of each grid cell. Then, use hotspot analysis combined with the tin mineralization index of each grid cell to screen and identify each grid cell to form candidate areas of potential ore deposits. Step 4: Extract mineralization characteristic parameters within the determined candidate potential ore areas to establish a mineralization probability index function, analyze the mineralization probability index value of each spatial grid cell within the candidate potential ore areas, and form a distribution dataset of the mineralization probability index in three-dimensional space. Step 5: Set the mineralization probability threshold, screen out tin mineralization candidate areas from the potential ore-bearing areas, and select the tin mineralization candidate area with the highest mineralization probability index value as the drilling target for tin mining based on the spatial distribution of the mineralization probability index.

[0008] Furthermore, multi-source geological data are fused, and a spatial registration algorithm is used to unify the coordinate system to establish a geological information database. The method used is as follows: Collect various geological data, including drilling, geophysical exploration, geochemical exploration and remote sensing data, including numerical, vector and image data. Perform format conversion and check and proofreading on various types of data, and use the mean method to fill in missing values. A weighted adaptive heterogeneous data fusion algorithm is used to fuse preprocessed multi-source geological data. The weights of each data source are determined based on their error variance. The formula for calculating the weights of each data source is as follows: in, Indicates the first The weight of each data source, It is the first The error variance of a data source is used to represent the measurement uncertainty of the data from that data source. It is the first Confidence level of each data source For the index of the data source, This represents the total number of data sources. The fused geological data source at any point in the spatial coordinate system is: in, Indicates the spatial location after fusion. Comprehensive geological data values ​​at the location, Indicates the first The data source is located in the spatial position The corresponding geological data value; Based on the spatial registration algorithm, the fused data sources are unified to the same spatial coordinate system. A rigid transformation model is used, minimizing the corresponding point error and combining this model to unify the coordinates of all 3D points in the original point set of the data source to the same spatial coordinate system. The formula used is as follows: in, Represents the original coordinates. These are the transformed coordinates. This is an error function representing the total squared error between two sets of corresponding points after rotation and translation transformations. The independent variable of the function is the rotation matrix. With translation vector , Represents the first point in the set of origins. Coordinates of a point, Indicates the first target point in the target point set. Coordinates of a point, It is a rotation matrix and must satisfy orthogonality constraints. It is a translation vector. The index of the coordinates of the points in the original point set, and , It is the total number of coordinates of the original point set points; Outlier detection is performed on data points within the same coordinate system using the Z-score statistical method. Outliers are removed, and based on the fused, registered, and cleaned data, a database structure is designed, data is imported, and indexes are created to form a geological information database.

[0009] Furthermore, a three-dimensional geological geometric model was established, based on the following method: Extracting a set of discrete points representing geological interfaces from a geological database. These sampling points serve as the foundation for model construction. Based on implicit surface modeling techniques grounded in geological constraints, an implicit function is defined using radial basis functions to reflect the geometry of the geological interface. This function consists of two parts: a smooth interpolation component formed by a weighted summation of the sampling points using radial basis functions, and a low-order linear polynomial component. The expression for this implicit function is as follows: in, Represents any point in space The implicit function value at that location. Radial basis functions describe any point in space. With the sampling points The function value corresponding to the distance between them. For the first The radial basis function weight coefficients corresponding to each sampling point Represents the distance between any two points. It is used to express the overall linear trend of implicit functions, avoiding the interpolation model from deviating from the actual geological features over a large range. , , , These are polynomial coefficients. , , It is any point in space Spatial coordinate components at the location; To determine the weight parameters and polynomial coefficients of the implicit function, two conditions must be met: First, the function value at each sampling point must match the preset value. To distinguish between the internal and external regions of the geological interface, auxiliary sampling points are generated near the interface, and the implicit function is assigned positive and negative values ​​for different spatial locations. Second, to ensure the uniqueness of the interpolation solution, the weight coefficients and polynomial coefficients in the implicit function must satisfy the polynomial orthogonality constraint, that is, the weighted sum of the weights and the coordinates of the sampling points is zero. The above conditions are expressed as a system of linear equations. The coefficient matrix of the equations consists of the calculation results of the radial basis functions and the polynomial matrix. The unknown parameters include the weights of the radial basis functions and the polynomial coefficients. The system of equations is solved by numerical linear algebra to obtain the implicit function parameters. The function values ​​are calculated in three-dimensional space using the implicit function. The zero isosurface of the implicit function constitutes the three-dimensional morphology of the geological interface. Finally, the zero isosurface of the implicit function is extracted using the isosurface extraction algorithm. Multiple interface models are combined to construct a complete three-dimensional geological geometric model.

[0010] Furthermore, based on spatial statistical analysis, the geological elements in the three-dimensional geological model are statistically analyzed to calculate the tin mineralization index of each grid cell. The method used is as follows: Based on the constructed 3D geological geometric model, cubic grid cells with equal side lengths are selected. Professional geoscience software is used to automatically divide the 3D region. Each cubic grid cell has unique spatial coordinates and spatial extent. For each grid cell, geological element parameters are statistically analyzed to calculate the tin mineralization index of each grid cell. The formula used is as follows: in, , , They represent the first The fracture density, fissure density, and porosity of the rock in each cubic grid cell. Indicates the first The total length of all rock fractures within a cubic grid cell Indicates the first The total volume of rock within a cubic grid cell Indicates the first The total surface area of ​​all rock fissures within a cubic grid cell. Indicates the first Rock skeleton volume within a cubic grid cell It is the index of the cube grid cell, and , This represents the total number of cubic mesh elements in the three-dimensional geological geometric model.

[0011] Furthermore, a hotspot analysis method was used in conjunction with the tin mineralization index of each grid cell to screen and identify potential ore-bearing areas. The method used was as follows: For each cubic mesh cell in the 3D geological geometry model, the number of sampling points within its body is counted, and this is combined with the weight coefficients corresponding to each sampling point in the model. The weights of each tin mineralization index in each cubic grid cell are obtained through weighted calculation. The hotspot index of each cubic grid cell is then calculated based on hotspot analysis, using the following formula: in, Indicates the first Hotspot index of each cubic grid cell Indicates the first In the cubic grid cell, the first The weight of each tin mineralization index Indicates the first In the cubic grid cell, the first Observed values ​​of tin mineralization index, This is an index for the tin mineralization index within a cubic grid cell, and , The types of tin mineralization indices in cubic grid cells. For the first The observed average value of tin mineralization index within each cubic grid cell. For the first The overall standard deviation of tin mineralization index observations within a cubic grid cell; Calculate the hotspot index of all cubic mesh elements to obtain the hotspot index distribution of the entire model. Select the first cubic mesh element As hotspot units, based on the neighborhood clustering analysis method, hotspot units that share a surface in space are defined as adjacency relationships. Then, using the connectivity analysis method, hotspot units with adjacency relationships are connected to each other to form an independent connected block. The connected block is a group of spatially continuous hotspot units, and each connected block corresponds to a potential mining area candidate region.

[0012] Furthermore, mineralization characteristic parameters are extracted within the identified candidate ore-bearing areas. These parameters include the fracturing channel coefficient, the porosity fracturing reservoir coefficient, and the microscopic permeability coefficient. The method used is as follows: The number of hotspot units in each selected candidate potential ore-bearing area is counted to obtain the average values ​​of fracture density, fissure density, and porosity of the rocks in each candidate area. Based on the cross-feature correlation combination method, combined with the average values ​​of fracture density, fissure density, and porosity of the rocks, the mineralization characteristic parameters in each candidate potential ore-bearing area are calculated using the following formula: in, , , These represent the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient, respectively, in each candidate region of the potential ore area. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average porosity of rocks in each candidate area of ​​potential ore deposits.

[0013] Furthermore, a mineralization probability index function is established, and the mineralization probability index value of each spatial grid cell within the candidate region of the potential ore deposit is analyzed to form a distribution dataset of the mineralization probability index in three-dimensional space. The method used is as follows: Normalized mineralization characteristic parameters were obtained from candidate areas of each potential ore zone. A mineralization probability exponential function was established based on Logistic regression probability theory, using the following formula: in, This represents the mineralization probability exponential function for each candidate potential ore area, used to indicate the probability that the candidate potential ore area belongs to a tin mineralization region. , , These are the normalized values ​​of the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient within each candidate potential ore area. This is a constant term used to represent the basic mineralization probability of each candidate potential ore-bearing area. , , These are the regression coefficients corresponding to the three mineralization characteristic parameters, reflecting the degree and direction of each parameter's influence on the mineralization probability; The representative coordinates of each candidate potential ore area are obtained by calculating the arithmetic mean of the center points of all hotspot units in each candidate area. The formula used is as follows: in, This represents the representative coordinates within each candidate region of potential mineral deposits. This represents the number of hotspot units in the candidate region of the potential mining area. This is an index for hotspot cells in the candidate region of the potential mining area, and , , , These represent the first candidate regions of each potential mineral area. The horizontal, vertical, and axial coordinates of each hotspot unit in three-dimensional space; The representative coordinates of each potential mineralization candidate area and the corresponding mineralization probability index are recorded to form a three-dimensional spatially distributed dataset.

[0014] Furthermore, tin mineralization candidate areas were selected from the potential ore-bearing areas. Based on the spatial distribution of the mineralization probability index, the tin mineralization candidate area with the highest mineralization probability index value was selected as the drilling target for tin mining. The method used was as follows: The mineralization probability threshold is set based on the mean-doubled standard deviation method. The mean and standard deviation of the mineralization probability index for all candidate potential ore-bearing areas are calculated, and the mineralization probability threshold is set as follows: in, This represents the established threshold for the probability of mineralization. This represents the average mineralization probability index of all candidate potential ore-bearing areas. The standard deviation of the mineralization probability index for all candidate potential ore-bearing areas. It is the adjustment coefficient, and , used to emphasize a significant probability value that is above the average level; The mineralization probability index of each potential ore candidate area is compared with the set mineralization probability threshold. Potential ore candidate areas with mineralization probability index values ​​exceeding the mineralization probability threshold are marked as tin mineralization candidate areas. The mineralization probability indices of all tin mineralization candidate areas are arranged in descending order. The tin mineralization candidate area with the largest mineralization probability index value is selected as the mining target, and the representative coordinates of the tin mineralization candidate area are used as the drilling target point for tin mining.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates various types of geological data through a weighted adaptive heterogeneous data fusion algorithm and uses a spatial registration algorithm to achieve a unified coordinate system and accurate data alignment, thereby improving the comprehensive utilization rate and spatial positioning accuracy of the data. It adopts a geologically constrained implicit surface modeling technique and combines it with a radial basis function interpolation method to effectively express the spatial morphology of complex geological interfaces, overcoming the shortcomings of traditional modeling methods in handling irregular and complex geological structures. This invention also obtains mineralization characteristic parameters by combining tin mineralization indices across features, establishes a mineralization probability index function using Logistic regression, and realizes a quantitative evaluation of the mineralization probability of potential ore-bearing areas. This overcomes the problems of strong subjectivity and lack of quantitative analysis in traditional mineral exploration methods. Finally, the mineralization probability threshold is dynamically set according to the mean-doubled standard deviation method, which can adapt to different mineralization conditions in different mining areas, screen out the tin mineralization candidate areas with the greatest exploration potential, and accurately locate drilling targets through clustering and ranking methods, which greatly improves the scientific nature of target area selection and mineral exploration efficiency. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: Please see Figure 1 A method for tin ore exploration based on three-dimensional geological modeling, the specific steps of which include: Step 1: Collect multi-source geological data obtained from drilling, geophysical exploration, geochemical exploration, and remote sensing. Based on a weighted adaptive heterogeneous data fusion algorithm, fuse the multi-source geological data, and use a spatial registration algorithm to unify the coordinate system. Perform data cleaning and format standardization to establish a geological information database.

[0020] In a specific embodiment of the present invention, by fusing heterogeneous data from multiple sources such as drilling, geophysical exploration, geochemical exploration, and remote sensing, and by using weighted adaptive adjustment of the influence of different data sources, the measurement uncertainties of each data source are comprehensively considered, effectively reducing the impact of errors from a single data source on the results, significantly improving the integrity and accuracy of geological information. A spatial registration algorithm, especially a rigid transformation model, is adopted to unify the three-dimensional spatial coordinate system of each data source, eliminating spatial deviations caused by data acquisition methods, equipment errors, etc., ensuring accurate correspondence of multi-source data in the same geographic space, and providing a reliable spatial foundation for subsequent three-dimensional modeling.

[0021] Furthermore, multi-source geological data are fused, and a spatial registration algorithm is used to unify the coordinate system to establish a geological information database. The method used is as follows: Collect various geological data, including drilling, geophysical exploration, geochemical exploration and remote sensing data, including numerical, vector and image data. Perform format conversion and check and proofreading on various types of data, and use the mean method to fill in missing values. A weighted adaptive heterogeneous data fusion algorithm is used to fuse preprocessed multi-source geological data. The weights of each data source are determined based on their error variance. The formula for calculating the weights of each data source is as follows: in, Indicates the first The weight of each data source, It is the first The error variance of a data source is used to represent the measurement uncertainty of the data from that data source. It is the first Confidence level of each data source For the index of the data source, This represents the total number of data sources. The fused geological data source at any point in the spatial coordinate system is: in, Indicates the spatial location after fusion. Comprehensive geological data values ​​at the location, Indicates the first The data source is located in the spatial position The corresponding geological data value; Based on the spatial registration algorithm, the fused data sources are unified to the same spatial coordinate system. A rigid transformation model is used, minimizing the corresponding point error and combining this model to unify the coordinates of all 3D points in the original point set of the data source to the same spatial coordinate system. The formula used is as follows: in, Represents the original coordinates. These are the transformed coordinates. This is an error function representing the total squared error between two sets of corresponding points after rotation and translation transformations. The independent variable of the function is the rotation matrix. With translation vector , Represents the first point in the set of origins. Coordinates of a point, Indicates the first target point in the target point set. Coordinates of a point, It is a rotation matrix and must satisfy orthogonality constraints. It is a translation vector. The index of the coordinates of the points in the original point set, and , It is the total number of coordinates of the original point set points; Outlier detection is performed on data points within the same coordinate system using the Z-score statistical method. Outliers are removed, and based on the fused, registered, and cleaned data, a database structure is designed, data is imported, and indexes are created to form a geological information database.

[0022] Step 2: Based on the established geological information database, the implicit surface modeling technique with geological constraints is used, combined with the radial basis function interpolation algorithm and the geological elements in the database to construct a three-dimensional geological geometric model.

[0023] In a specific embodiment of this invention, compared to traditional interpolation methods for establishing three-dimensional geological geometric models, we selected implicit surface modeling technology constrained by geology. Combined with radial basis function interpolation algorithms, and utilizing high-quality discrete interface sampling points from a geological database, we constructed a continuous, smooth, and realistic three-dimensional geological geometric model. This method not only effectively depicts the spatial morphology of complex geological interfaces, avoiding the shortcomings of traditional explicit modeling in handling irregular geological bodies, but also ensures the overall trend stability of the model by introducing a low-order linear polynomial part, improving the accuracy and reliability of modeling. Furthermore, by assigning positive and negative values ​​to the implicit function through auxiliary sampling points, we achieved accurate differentiation between the inner and outer regions of the geological interface, enhancing the physical rationality of the model. At the same time, by using a system of linear equations to solve for the weight parameters and polynomial coefficients, we ensured the unique solution and numerical stability of the implicit function.

[0024] Furthermore, a three-dimensional geological geometric model was established, based on the following method: Extracting a set of discrete points representing geological interfaces from a geological database. These sampling points serve as the foundation for model construction. Based on implicit surface modeling techniques grounded in geological constraints, an implicit function is defined using radial basis functions to reflect the geometry of the geological interface. This function consists of two parts: a smooth interpolation component formed by a weighted summation of the sampling points using radial basis functions, and a low-order linear polynomial component. The expression for this implicit function is as follows: in, Represents any point in space The implicit function value at that location. Radial basis functions describe any point in space. With the sampling points The function value corresponding to the distance between them. For the first The radial basis function weight coefficients corresponding to each sampling point Represents the distance between any two points. It is used to express the overall linear trend of implicit functions, avoiding the interpolation model from deviating from the actual geological features over a large range. , , , These are polynomial coefficients. , , It is any point in space Spatial coordinate components at the location; To determine the weight parameters and polynomial coefficients of the implicit function, two conditions must be met: First, the function value at each sampling point must match the preset value. To distinguish between the internal and external regions of the geological interface, auxiliary sampling points are generated near the interface, and the implicit function is assigned positive and negative values ​​for different spatial locations. Second, to ensure the uniqueness of the interpolation solution, the weight coefficients and polynomial coefficients in the implicit function must satisfy the polynomial orthogonality constraint, that is, the weighted sum of the weights and the coordinates of the sampling points is zero. The above conditions are expressed as a system of linear equations. The coefficient matrix of the equations consists of the calculation results of the radial basis functions and the polynomial matrix. The unknown parameters include the weights of the radial basis functions and the polynomial coefficients. The system of equations is solved by numerical linear algebra to obtain the implicit function parameters. The function values ​​are calculated in three-dimensional space using the implicit function. The zero isosurface of the implicit function constitutes the three-dimensional morphology of the geological interface. Finally, the zero isosurface of the implicit function is extracted using the isosurface extraction algorithm. Multiple interface models are combined to construct a complete three-dimensional geological geometric model.

[0025] Step 3: Divide the three-dimensional geological model into grid units, and statistically analyze the geological elements in the three-dimensional geological model based on spatial statistical analysis to calculate the tin mineralization index of each grid unit. Then, use hotspot analysis combined with the tin mineralization index of each grid unit to screen and identify each grid unit to form candidate areas of potential ore deposits.

[0026] In a specific embodiment of this invention, step 3 utilizes the combined application of spatial statistical analysis and hotspot analysis to achieve precise quantification and efficient screening of tin mineralization indicators in a three-dimensional geological model. First, spatial statistical analysis meticulously analyzes geological faults, fissures, and porosity within the three-dimensional grid cells, quantitatively calculating key indicators such as fault density, fissure density, and porosity, providing a scientific basis for identifying favorable tin mineralization spaces. Then, hotspot analysis, combining the weights of each indicator, comprehensively evaluates the mineralization degree of each grid cell, quickly locating hotspot areas of high mineralization anomalies. Furthermore, it uses connected component analysis to cluster and integrate spatially continuous high mineralization units, ultimately effectively screening out potential favorable mineral exploration areas. This technical process not only improves the efficiency and spatial accuracy of mineralization anomaly identification but also significantly enhances the scientific validity and reliability of mineral exploration area prediction, providing a solid foundation for refined mineral exploration through data analysis and spatial decision-making.

[0027] It should be noted that the fracture density, fissure density, and porosity of rocks are used as indicators of tin mineralization because these geological characteristics directly reflect the degree of fracturing and alteration of the rock mass, determining the migration channels of fluids and the accumulation space of mineralized materials. Fractures and fissures provide channels for the migration and accumulation of mineralized fluids, promoting and intensifying tin mineralization; while porosity affects the rock's storage capacity and the residence time of fluids, thus influencing the scale and quality of ore body formation. Therefore, these indicators can effectively characterize the key geological conditions in the tin mineralization process and have sufficient geological rationale as important parameters for assessing and predicting potential mineralization areas.

[0028] Furthermore, based on spatial statistical analysis, the geological elements in the three-dimensional geological model are statistically analyzed to calculate the tin mineralization index of each grid cell. The method used is as follows: Based on the constructed 3D geological geometric model, cubic grid cells with equal side lengths are selected. Professional geoscience software is used to automatically divide the 3D region. Each cubic grid cell has unique spatial coordinates and spatial extent. For each grid cell, geological element parameters are statistically analyzed to calculate the tin mineralization index of each grid cell. The formula used is as follows: in, , , They represent the first The fracture density, fissure density, and porosity of the rock in each cubic grid cell. Indicates the first The total length of all rock fractures within a cubic grid cell Indicates the first The total volume of rock within a cubic grid cell Indicates the first The total surface area of ​​all rock fissures within a cubic grid cell. Indicates the first Rock skeleton volume within a cubic grid cell It is the index of the cube grid cell, and , This represents the total number of cubic mesh elements in the three-dimensional geological geometric model.

[0029] It should be noted that the calculation of the hotspot index is achieved by assigning weights to multiple tin mineralization indicators, realizing a multi-factor weighted comprehensive evaluation. Furthermore, the centering and normalization processes are completed by subtracting the observed mean and dividing by the standard deviation, eliminating the differences in dimensions and scales between different indicators, ensuring the fairness and comparability of the calculation. At the same time, combined with the statistical characteristics of the weights, the formula has a solid statistical foundation, which can effectively reveal the abnormal clustering phenomenon in spatial data, accurately reflect the comprehensive intensity and spatial anomaly of mineralization indicators within the grid unit, and thus provide a reliable quantitative basis for the scientific screening of potential ore areas.

[0030] Furthermore, a hotspot analysis method was used in conjunction with the tin mineralization index of each grid cell to screen and identify potential ore-bearing areas. The method used was as follows: For each cubic mesh cell in the 3D geological geometry model, the number of sampling points within its body is counted, and this is combined with the weight coefficients corresponding to each sampling point in the model. The weights of each tin mineralization index in each cubic grid cell are obtained through weighted calculation. The hotspot index of each cubic grid cell is then calculated based on hotspot analysis, using the following formula: in, Indicates the first Hotspot index of each cubic grid cell Indicates the first In the cubic grid cell, the first The weight of each tin mineralization index Indicates the first In the cubic grid cell, the first Observed values ​​of tin mineralization index, This is an index for the tin mineralization index within a cubic grid cell, and , The types of tin mineralization indices in cubic grid cells. For the first The observed average value of tin mineralization index within each cubic grid cell. For the first The overall standard deviation of tin mineralization index observations within a cubic grid cell; Calculate the hotspot index of all cubic mesh elements to obtain the hotspot index distribution of the entire model. Select the first cubic mesh element As hotspot units, based on the neighborhood clustering analysis method, hotspot units that share a surface in space are defined as adjacency relationships. Then, using the connectivity analysis method, hotspot units with adjacency relationships are connected to each other to form an independent connected block. The connected block is a group of spatially continuous hotspot units, and each connected block corresponds to a potential mining area candidate region.

[0031] Step 4: Extract mineralization characteristic parameters within the identified candidate potential ore areas to establish a mineralization probability index function, analyze the mineralization probability index value of each spatial grid cell within the candidate potential ore areas, and form a distribution dataset of the mineralization probability index in three-dimensional space.

[0032] In a specific embodiment of the present invention, by extracting and calculating mineralization characteristic parameters such as the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient within the candidate area of ​​potential ore deposits, the geological structure and reservoir conditions favorable to mineralization within the area can be comprehensively quantified and characterized. Furthermore, a mineralization probability index function is established using a Logistic regression model, transforming various mineralization characteristic parameters into specific probability values. This enables precise quantification and ranking of the mineralization favorability of each candidate area. Combined with spatial coordinates, a three-dimensional probability distribution dataset is generated, thereby providing an intuitive and efficient decision-making basis for spatial prediction of tin resources and selection of prospecting target areas.

[0033] It should be noted that in the formula for calculating the fracture channel coefficient, fracture density and fissure density represent the macroscopic and microscopic fracture characteristics of the rock, respectively. Combining these two through square root calculations can more effectively reflect the connectivity of fluid transport in the ore body and the degree of channel development, reflecting possible paths for fluid transport and material enrichment. Here, the geometric mean calculation in square root form balances the contributions of both, preventing one indicator from being too large or too small and dominating the result. This reflects the interdependence and synergistic effect of the two indicators, weakens the influence of extreme values ​​on the parameters, and thus more stably and reasonably reflects the comprehensive characteristics of the fracture channel. In the formula for calculating the porosity-fractured reservoir coefficient, porosity is the main manifestation of reservoir space. Combined with fracture density, it reflects the combined influence of reservoir volume and fracture degree. The product of the two reflects the combined influence of reservoir volume and fracture channels, enhancing the fluid storage and transport environment. The multiplication directly reflects the amplified coupling relationship between the two factors; that is, only when both fracture density and porosity are high... The value of [the formula] will increase significantly, more effectively capturing the spatial conditions conducive to the accumulation and storage of ore-forming fluids. In the formula for calculating the microscopic permeability coefficient, this formula is a harmonic average of fracture density and porosity, reflecting the balance between the two in the microscopic permeability process. The harmonic average emphasizes the influence of the smaller value, meaning that the microscopic permeability performance during the ore-forming process is limited by the weaker factor. That is, if either fracture density or porosity is low, it will limit the overall permeability. This form is more in line with the physical law of the "weakest link effect" in the permeability mechanism and can more realistically reflect the limiting factors of permeability conditions.

[0034] Furthermore, mineralization characteristic parameters are extracted within the identified candidate ore-bearing areas. These parameters include the fracturing channel coefficient, the porosity fracturing reservoir coefficient, and the microscopic permeability coefficient. The method used is as follows: The number of hotspot units in each selected candidate potential ore-bearing area is counted to obtain the average values ​​of fracture density, fissure density, and porosity of the rocks in each candidate area. Based on the cross-feature correlation combination method, combined with the average values ​​of fracture density, fissure density, and porosity of the rocks, the mineralization characteristic parameters in each candidate potential ore-bearing area are calculated using the following formula: in, , , These represent the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient, respectively, in each candidate region of the potential ore area. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average porosity of rocks in each candidate area of ​​potential ore deposits.

[0035] It should be noted that the determination of tin mineralization areas is essentially a binary classification problem of "yes / no". Logistic regression theory can map multiple continuous or discrete input features to probability values ​​between 0 and 1, which can intuitively reflect the probability that the candidate area of ​​the potential ore area belongs to the tin mineralization area. Furthermore, Logistic regression theory can map different mineralization characteristic parameters into probabilities through linear combination and sigmoid function, so as to achieve a comprehensive evaluation of mineralization potential under multivariate conditions, taking into account the weight and synergistic effect of various factors.

[0036] Furthermore, a mineralization probability index function is established, and the mineralization probability index value of each spatial grid cell within the candidate region of the potential ore deposit is analyzed to form a distribution dataset of the mineralization probability index in three-dimensional space. The method used is as follows: Normalized mineralization characteristic parameters were obtained from candidate areas of each potential ore zone. A mineralization probability exponential function was established based on Logistic regression probability theory, using the following formula: in, This represents the mineralization probability exponential function for each candidate potential ore area, used to indicate the probability that the candidate potential ore area belongs to a tin mineralization region. , , These are the normalized values ​​of the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient within each candidate potential ore area. This is a constant term used to represent the basic mineralization probability of each candidate potential ore-bearing area. , , These are the regression coefficients corresponding to the three mineralization characteristic parameters, reflecting the degree and direction of each parameter's influence on the mineralization probability. In the above formula, the mineralization probability index of each candidate potential ore area is directly proportional to the normalized fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient within the area. Since the fracture channel coefficient comprehensively reflects the development status of fractures and pores in the rock, these channels are important channels for the migration and accumulation of mineralizing fluids. The larger the value, the more developed the channels, and the easier it is for the fluids to flow and accumulate. The abundance of fracture channels means that mineralizing fluids can more effectively enter and circulate in the potential ore area, which is conducive to the transport of metal elements and the formation of ore bodies, thereby increasing the mineralization potential and probability. The larger, The larger the porosity and the higher the degree of fragmentation, the better. A larger reservoir space and higher degree of fragmentation mean more ore-forming fluids can be stored and retained. Favorable reservoir conditions provide ample storage space and a conducive reaction environment for mineralizing fluids, which is beneficial for mineral precipitation and enrichment, thus improving the mineralization effect and probability. The larger, The larger the permeability coefficient, the better the permeability, reflecting the permeability of the rock's fine structure. Higher permeability allows ore-forming fluids to penetrate more smoothly and react more readily with the surrounding rock. Excellent micro-permeability conditions promote fluid diffusion and material exchange, which is beneficial for the uniform enrichment of mineral components, enhancing the likelihood of ore body formation and the continuity of its spatial distribution. The larger, The larger; The larger the value, the more favorable the mineralization geological conditions of the candidate area, the higher the mineralization potential, and the greater the likelihood that it is a tin mineralization area.

[0037] It should be noted that the fitting coefficients in the above function formulas... , , , We use the maximum likelihood method to determine this. Specifically, we first obtain a set of known tin mineralization region sample data, which includes... Each spatial grid cell represents a sample, and each spatial grid cell has normalized mineralization characteristic parameters. , , and the corresponding mineralization labels Typically, labels are binary, where 1 indicates the unit belongs to a tin mineralization region, and 0 indicates it does not. The goal is to fit a mineralization probability exponential function. Assuming the samples are independent, the joint probability of the entire sample is: in, It is the likelihood function, which represents the likelihood of a given set of parameters. In the case of, the joint probability of all observed data, , This indicates a chain multiplication, meaning the product is performed on all samples. For sample index, The total number of samples. Indicates the first The true label of each sample This indicates that the sample actually belongs to a tin-mineralized area. This indicates that the sample does not belong to the tin mineralization area. It is a "selective exponentiation" of probability, which represents the first... The probability that a sample is predicted as a "tin mineralization area" ranges from 0 to 1. When the true label is 1, the likelihood term takes the predicted probability of 1 / 2. When the true label is 0, the likelihood term is 1. Used to express the probability contribution under different labels in a probabilistic model, its function is to make the sample with "label 1" contribute to the predicted probability, and the sample with "label 0" contribute 1, thereby maximizing the overall fit of the model to the actual observed data. To simplify the calculation, the logarithm of the above function is taken. Since the mineralization probability is a nonlinear function of the parameters, the optimal parameters cannot be directly solved. Therefore, a numerical optimization method is used to solve it. The gradient of the log-likelihood function with respect to each parameter reflects the direction and magnitude of parameter adjustment. Subsequently, the parameter values ​​are updated using the gradient information through continuous iteration until the log-likelihood function converges, that is, the parameter changes tend to stabilize. This process maximizes the matching degree between the model's predicted mineralization probability and the actual sample labels, thereby obtaining the best regression coefficients and achieving an effective fit to the exponential function of the mineralization probability.

[0038] The representative coordinates of each candidate potential ore area are obtained by calculating the arithmetic mean of the center points of all hotspot units in each candidate area. The formula used is as follows: in, This represents the representative coordinates within each candidate region of potential mineral deposits. This represents the number of hotspot units in the candidate region of the potential mining area. This is an index for hotspot cells in the candidate region of the potential mining area, and , , , These represent the first candidate regions of each potential mineral area. The horizontal, vertical, and axial coordinates of each hotspot unit in three-dimensional space; The representative coordinates of each potential mineralization candidate area and the corresponding mineralization probability index are recorded to form a three-dimensional spatially distributed dataset.

[0039] Step 5: Set the mineralization probability threshold, screen out tin mineralization candidate areas from the potential ore-bearing areas, and select the tin mineralization candidate area with the highest mineralization probability index value as the drilling target for tin mining based on the spatial distribution of the mineralization probability index.

[0040] In a specific embodiment of this invention, by setting a mineralization probability threshold based on the mean-doubled standard deviation method, candidate areas with mineralization potential significantly higher than the average level are scientifically distinguished, effectively screening tin mineralization candidate areas. This avoids the bias of subjective human judgment, improves the accuracy and reliability of prospecting target areas, and the introduction of an adjustment coefficient makes the threshold flexible, allowing the screening rigor to be adjusted according to the actual geological background, ensuring that the selected target areas have both a high mineralization probability and do not overlook potential mineralization areas. Finally, by ranking and selecting the tin mineralization candidate areas with the highest mineralization probability as drilling targets, the quantitative utilization and spatial optimization layout of the mineralization probability index are realized, improving the scientific level and exploration efficiency of tin prospecting, and reducing exploration risks and costs.

[0041] Furthermore, tin mineralization candidate areas were selected from the potential ore-bearing areas. Based on the spatial distribution of the mineralization probability index, the tin mineralization candidate area with the highest mineralization probability index value was selected as the drilling target for tin mining. The method used was as follows: The mineralization probability threshold is set based on the mean-doubled standard deviation method. The mean and standard deviation of the mineralization probability index for all candidate potential ore-bearing areas are calculated, and the mineralization probability threshold is set as follows: in, This represents the established threshold for the probability of mineralization. This represents the average mineralization probability index of all candidate potential ore-bearing areas. The standard deviation of the mineralization probability index for all candidate potential ore-bearing areas. It is the adjustment coefficient, and , used to emphasize a significant probability value that is above the average level; The mineralization probability index of each potential ore candidate area is compared with the set mineralization probability threshold. Potential ore candidate areas with mineralization probability index values ​​exceeding the mineralization probability threshold are marked as tin mineralization candidate areas. The mineralization probability indices of all tin mineralization candidate areas are arranged in descending order. The tin mineralization candidate area with the largest mineralization probability index value is selected as the mining target, and the representative coordinates of the tin mineralization candidate area are used as the drilling target point for tin mining.

[0042] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0043] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0044] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0045] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for tin ore exploration based on three-dimensional geological modeling, characterized in that, The specific steps include: Step 1: Collect multi-source geological data obtained from drilling, geophysical exploration, geochemical exploration and remote sensing. Based on a weighted adaptive heterogeneous data fusion algorithm, fuse the multi-source geological data, and use a spatial registration algorithm to unify the coordinate system. Perform data cleaning and format standardization to establish a geological information database. Step 2: Based on the established geological information database, the implicit surface modeling technique with geological constraints is used, combined with the radial basis function interpolation algorithm and the geological elements in the database to construct a three-dimensional geological geometric model. Step 3: Divide the three-dimensional geological model into grid cells, and statistically analyze the geological elements in the three-dimensional geological model based on spatial statistical analysis to calculate the tin mineralization index of each grid cell. Then, use hotspot analysis combined with the tin mineralization index of each grid cell to screen and identify each grid cell to form candidate areas of potential ore deposits. Step 4: Extract mineralization characteristic parameters within the determined candidate potential ore areas to establish a mineralization probability index function, analyze the mineralization probability index value of each spatial grid cell within the candidate potential ore areas, and form a distribution dataset of the mineralization probability index in three-dimensional space. Step 5: Set the mineralization probability threshold, screen out tin mineralization candidate areas from the potential ore-bearing areas, and select the tin mineralization candidate area with the highest mineralization probability index value as the drilling target for tin mining based on the spatial distribution of the mineralization probability index.

2. The tin ore prospecting method based on three-dimensional geological modeling according to claim 1, characterized in that, The geological information database is established by fusing multi-source geological data and using a spatial registration algorithm to unify the coordinate system. The method used is as follows: Collect various geological data, including drilling, geophysical exploration, geochemical exploration and remote sensing data, including numerical, vector and image data. Perform format conversion and check and proofreading on various types of data, and use the mean method to fill in missing values. A weighted adaptive heterogeneous data fusion algorithm is used to fuse preprocessed multi-source geological data. The weights of each data source are determined based on their error variance. The formula for calculating the weights of each data source is as follows: in, Indicates the first The weight of each data source, It is the first The error variance of a data source is used to represent the measurement uncertainty of the data from that data source. It is the first Confidence level of each data source For the index of the data source, This represents the total number of data sources. The fused geological data source at any point in the spatial coordinate system is: in, Indicates the spatial location after fusion. Comprehensive geological data values ​​at the location, Indicates the first The data source is located in the spatial position The corresponding geological data value; Based on the spatial registration algorithm, the fused data sources are unified to the same spatial coordinate system. A rigid transformation model is used, minimizing the corresponding point error and combining this model to unify the coordinates of all 3D points in the original point set of the data source to the same spatial coordinate system. The formula used is as follows: in, Represents the original coordinates. These are the transformed coordinates. This is an error function representing the total squared error between two sets of corresponding points after rotation and translation transformations. The independent variable of the function is the rotation matrix. With translation vector , Represents the first point in the set of origins. Coordinates of a point, Indicates the first target point in the target point set. Coordinates of a point, It is a rotation matrix and must satisfy orthogonality constraints. It is a translation vector. The index of the coordinates of the points in the original point set, and , It is the total number of coordinates of the original point set points; Outlier detection is performed on data points within the same coordinate system using the Z-score statistical method. Outliers are removed, and based on the fused, registered, and cleaned data, a database structure is designed, data is imported, and indexes are created to form a geological information database.

3. The tin ore prospecting method based on three-dimensional geological modeling according to claim 2, characterized in that, The method used to establish the three-dimensional geological geometric model is as follows: Extracting a set of discrete points representing geological interfaces from a geological database. These sampling points serve as the foundation for model construction. Based on implicit surface modeling techniques grounded in geological constraints, an implicit function is defined using radial basis functions to reflect the geometry of the geological interface. This function consists of two parts: a smooth interpolation component formed by a weighted summation of the sampling points using radial basis functions, and a low-order linear polynomial component. The expression for this implicit function is as follows: in, Represents any point in space The implicit function value at that location. Radial basis functions describe any point in space. With the sampling points The function value corresponding to the distance between them. For the first The radial basis function weight coefficients corresponding to each sampling point Represents the distance between any two points. It is used to express the overall linear trend of implicit functions, avoiding the interpolation model from deviating from the actual geological features over a large range. , , , These are polynomial coefficients. , , It is any point in space Spatial coordinate components at the location; To determine the weight parameters and polynomial coefficients of the implicit function, two conditions must be met: First, the function value at each sampling point must match the preset value. To distinguish between the internal and external regions of the geological interface, auxiliary sampling points are generated near the interface, and the implicit function is assigned positive and negative values ​​for different spatial locations. Second, to ensure the uniqueness of the interpolation solution, the weight coefficients and polynomial coefficients in the implicit function must satisfy the polynomial orthogonality constraint, that is, the weighted sum of the weights and the coordinates of the sampling points is zero. The above conditions are expressed as a system of linear equations. The coefficient matrix of the equations consists of the calculation results of the radial basis functions and the polynomial matrix. The unknown parameters include the weights of the radial basis functions and the polynomial coefficients. The system of equations is solved by numerical linear algebra to obtain the implicit function parameters. The function values ​​are calculated in three-dimensional space using the implicit function. The zero isosurface of the implicit function constitutes the three-dimensional morphology of the geological interface. Finally, the zero isosurface of the implicit function is extracted using the isosurface extraction algorithm. Multiple interface models are combined to construct a complete three-dimensional geological geometric model.

4. The tin ore prospecting method based on three-dimensional geological modeling according to claim 3, characterized in that, The geological elements in the three-dimensional geological model are statistically analyzed using spatial statistical analysis methods to calculate the tin mineralization index of each grid cell. The method used is as follows: Based on the constructed 3D geological geometric model, cubic grid cells with equal side lengths are selected. Professional geoscience software is used to automatically divide the 3D region. Each cubic grid cell has unique spatial coordinates and spatial extent. For each grid cell, geological element parameters are statistically analyzed to calculate the tin mineralization index of each grid cell. The formula used is as follows: in, , , They represent the first The fracture density, fissure density, and porosity of the rock in each cubic grid cell. Indicates the first The total length of all rock fractures within a cubic grid cell Indicates the first The total volume of rock within a cubic grid cell Indicates the first The total surface area of ​​all rock fissures within a cubic grid cell. Indicates the first Rock skeleton volume within a cubic grid cell It is the index of the cube grid cell, and , This represents the total number of cubic mesh elements in the three-dimensional geological geometric model.

5. A tin ore prospecting method based on three-dimensional geological modeling according to claim 4, characterized in that, Hotspot analysis was used in conjunction with tin mineralization indices of each grid cell to screen and identify potential ore-bearing areas. The method used was as follows: For each cubic mesh cell in the 3D geological geometry model, the number of sampling points within its body is counted, and this is combined with the weight coefficients corresponding to each sampling point in the model. The weights of each tin mineralization index in each cubic grid cell are obtained through weighted calculation. The hotspot index of each cubic grid cell is then calculated based on hotspot analysis, using the following formula: in, Indicates the first Hotspot index of each cubic grid cell Indicates the first In the cubic grid cell, the first The weight of each tin mineralization index Indicates the first In the cubic grid cell, the first Observed values ​​of tin mineralization index, This is an index for the tin mineralization index within a cubic grid cell, and , The types of tin mineralization indices in cubic grid cells. For the first The observed average value of tin mineralization index within each cubic grid cell. For the first The overall standard deviation of tin mineralization index observations within a cubic grid cell; Calculate the hotspot index of all cubic mesh elements to obtain the hotspot index distribution of the entire model. Select the first cubic mesh element As hotspot units, based on the neighborhood clustering analysis method, hotspot units that share a surface in space are defined as adjacency relationships. Then, using the connectivity analysis method, hotspot units with adjacency relationships are connected to each other to form an independent connected block. The connected block is a group of spatially continuous hotspot units, and each connected block corresponds to a potential mining area candidate region.

6. A tin ore prospecting method based on three-dimensional geological modeling according to claim 5, characterized in that, Mineralization characteristic parameters are extracted within the identified candidate ore-bearing areas. These parameters include the fracturing channel coefficient, the porosity fracturing reservoir coefficient, and the microscopic permeability coefficient. The method used is as follows: The number of hotspot units in each selected candidate potential ore-bearing area is counted to obtain the average values ​​of fracture density, fissure density, and porosity of the rocks in each candidate area. Based on the cross-feature correlation combination method, combined with the average values ​​of fracture density, fissure density, and porosity of the rocks, the mineralization characteristic parameters in each candidate potential ore-bearing area are calculated using the following formula: in, , , These represent the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient, respectively, in each candidate region of the potential ore area. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average fracture density of rocks in each candidate area of ​​potential ore deposits. It is the average porosity of rocks in each candidate area of ​​potential ore deposits.

7. A tin ore prospecting method based on three-dimensional geological modeling according to claim 6, characterized in that, A mineralization probability index function is established, and the mineralization probability index value of each spatial grid cell within the candidate region of the potential ore deposit is analyzed to form a distribution dataset of the mineralization probability index in three-dimensional space. The method used is as follows: Normalized mineralization characteristic parameters were obtained from candidate areas of each potential ore zone. A mineralization probability exponential function was established based on Logistic regression probability theory, using the following formula: in, This represents the mineralization probability exponential function for each candidate potential ore area, used to indicate the probability that the candidate potential ore area belongs to a tin mineralization region. , , These are the normalized values ​​of the fracture channel coefficient, porosity fracture reservoir coefficient, and microscopic permeability coefficient within each candidate potential ore area. This is a constant term used to represent the basic mineralization probability of each candidate potential ore-bearing area. , , These are the regression coefficients corresponding to the three mineralization characteristic parameters, reflecting the degree and direction of each parameter's influence on the mineralization probability; The representative coordinates of each candidate potential ore area are obtained by calculating the arithmetic mean of the center points of all hotspot units in each candidate area. The formula used is as follows: in, This represents the representative coordinates within each candidate region of potential mineral deposits. This represents the number of hotspot units in the candidate region of the potential mining area. This is an index for hotspot cells in the candidate region of the potential mining area, and , , , These represent the first candidate regions of each potential mineral area. The horizontal, vertical, and axial coordinates of each hotspot unit in three-dimensional space; The representative coordinates of each potential mineralization candidate area and the corresponding mineralization probability index are recorded to form a three-dimensional spatially distributed dataset.

8. A tin ore prospecting method based on three-dimensional geological modeling according to claim 7, characterized in that, Tin mineralization candidate areas were selected from potential ore-bearing areas. Based on the spatial distribution of the mineralization probability index, the tin mineralization candidate area with the highest mineralization probability index value was selected as the drilling target for tin mining. The method used was as follows: The mineralization probability threshold is set based on the mean-doubled standard deviation method. The mean and standard deviation of the mineralization probability index for all candidate potential ore-bearing areas are calculated, and the mineralization probability threshold is set as follows: in, This represents the established threshold for the probability of mineralization. This represents the average mineralization probability index of all candidate potential ore-bearing areas. The standard deviation of the mineralization probability index for all candidate potential ore-bearing areas. It is the adjustment coefficient, and , used to emphasize a significant probability value that is above the average level; The mineralization probability index of each potential ore candidate area is compared with the set mineralization probability threshold. Potential ore candidate areas with mineralization probability index values ​​exceeding the mineralization probability threshold are marked as tin mineralization candidate areas. The mineralization probability indices of all tin mineralization candidate areas are arranged in descending order. The tin mineralization candidate area with the largest mineralization probability index value is selected as the mining target, and the representative coordinates of the tin mineralization candidate area are used as the drilling target point for tin mining.

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