A three-dimensional mineralization prediction method and system based on GOCAD

Abstract

The present invention relates to a three-dimensional mineralization prediction method and system based on GOCAD. The method comprises: obtaining geological data according to the area to be predicted; constructing a geological surface model of the area to be predicted according to the geological data based on GOCAD software; obtaining a geological SGrid grid model of the area to be predicted by intersecting the geological surface model with the original grid model; obtaining each exploration line profile data of the area to be predicted; constructing an ore body surface model of the area to be predicted according to all exploration line profile data based on GOCAD software; obtaining an ore body SGrid grid model of the area to be predicted by intersecting the ore body surface model with the original grid model; determining the mineralization probability of each grid corresponding area in the geological SGrid grid model based on the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted based on the weight of evidence method. The present invention can improve the versatility of mineral prediction.

Description

Technical Field

[0001] The present invention relates to the field of mineral prediction, and in particular to a three-dimensional mineralization prediction method and system based on GOCAD. Background Art

[0002] Uranium has a very special strategic position as a nuclear material. As the surface and near-surface uranium minerals are almost exhausted, finding deep hidden minerals has become a top priority. In recent years, with the widespread application of 3D geological modeling technology, quantitative prediction methods based on 3D geological models have also been applied from 2D to 3D space.

[0003] Three-dimensional quantitative prediction is to predict the location and resource volume of underground minerals. In the existing technology, methods such as evidence weight method, information volume method, fractal method, etc. can be used to predict minerals, but the application of the above methods is dependent on professionally developed software and is not universal. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional mineralization prediction method and system based on GOCAD to improve the versatility of mineral prediction.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A three-dimensional mineralization prediction method based on GOCAD, comprising:

[0007] Obtain geological data according to the area to be predicted;

[0008] Based on GOCAD software, a geological surface model of the area to be predicted is constructed according to the geological data;

[0009] The geological SGrid grid model of the area to be predicted is obtained by intersecting the geological surface model with the original grid model; the original grid model is a grid model established according to the modeling range;

[0010] Obtain profile data of each exploration line in the area to be predicted;

[0011] Based on the GOCAD software, a surface model of the ore body in the area to be predicted is constructed according to all the exploration line profile data;

[0012] The ore body surface model is intersected with the original grid model to obtain the ore body SGrid grid model of the area to be predicted;

[0013] According to the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted, the mineralization probability of each grid corresponding to the area in the geological SGrid grid model is determined based on the weight of evidence method.

[0014] Optionally, obtaining geological data according to the area to be predicted specifically includes:

[0015] According to the area to be predicted, geological data with a scale of 1:500 to 1:2000 are obtained; the geological data include: geological map, exploration line profile and mid-section plan;

[0016] Performing image correction and digital processing on the geological data to obtain pre-processed geological data;

[0017] The preprocessed geological data is converted from two-dimensional to three-dimensional space using three-dimensional software 3Dmine or Surpac, and the spatial data required for three-dimensional modeling is extracted to obtain the geological data.

[0018] Optionally, constructing a geological surface model of the area to be predicted based on the geological data based on GOCAD software specifically includes:

[0019] Using a discrete smooth interpolation method, spatial fitting is performed based on the geological data to construct an open surface;

[0020] Based on the open surface, a closed surface is constructed through a 3D model module to obtain the geological surface model.

[0021] Optionally, the GOCAD software is used to construct a surface model of the ore body in the area to be predicted according to all the exploration line profile data, specifically including:

[0022] The profile connection method is used to connect all the exploration line profile data in the form of triangular surfaces to form a closed surface, thereby obtaining the ore body surface model.

[0023] Optionally, the method of determining the mineralization probability of each grid corresponding to the area in the geological SGrid grid model based on the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted based on the weight of evidence method specifically includes:

[0024] Based on the evidence weight method, according to the known ore bodies in the ore body SGrid grid model, the prior probability and related parameters of each evidence factor in the geological SGrid grid model are determined; the evidence factor is the favorable mineralization information in the geological SGrid grid model, and the favorable mineralization information includes strata, rock bodies, fractures, geophysical anomalies and geochemical anomalies; the related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance;

[0025] Determining the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model based on the prior probability and related parameters of each factor; each grid in the geological SGrid grid model includes multiple evidence factors;

[0026] According to the logarithmic posterior likelihood ratio of each grid, the posterior probability corresponding to the grid is determined to obtain the mineralization probability of each grid.

[0027] The present invention also provides a three-dimensional mineralization prediction system based on GOCAD, comprising:

[0028] A geological data acquisition module is used to acquire geological data according to the area to be predicted;

[0029] A geological surface model building module is used to build a geological surface model of the area to be predicted according to the geological data based on GOCAD software;

[0030] A geological SGrid grid model acquisition module is used to obtain a geological SGrid grid model of the area to be predicted by intersecting the geological surface model with the original grid model; the original grid model is a grid model established according to the modeling range;

[0031] An exploration line profile data acquisition module is used to acquire the profile data of each exploration line in the area to be predicted;

[0032] An ore body surface model building module is used to build an ore body surface model of the area to be predicted based on all exploration line profile data based on the GOCAD software;

[0033] An ore body SGrid grid model acquisition module is used to obtain the ore body SGrid grid model of the area to be predicted by intersecting the ore body surface model with the original grid model;

[0034] The mineralization probability determination module is used to determine the mineralization probability of each grid corresponding to the geological SGrid grid model based on the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted, based on the evidence weight method.

[0035] Optionally, the geological data acquisition module specifically includes:

[0036] A geological data acquisition unit, used to acquire geological data at a scale of 1:500 to 1:2000 according to the area to be predicted; the geological data includes: a geological map, a profile of an exploration line, and a mid-section plan;

[0037] A preprocessing unit, used for performing image correction and digital processing on the geological data to obtain preprocessed geological data;

[0038] The space conversion unit is used to convert the preprocessed geological data from two-dimensional to three-dimensional space through three-dimensional software 3Dmine or Surpac, and extract the spatial data required for three-dimensional modeling to obtain the geological data.

[0039] Optionally, the geological surface model construction module specifically includes:

[0040] A fitting unit, used for performing spatial fitting based on the geological data by using a discrete smooth interpolation method to construct an open surface;

[0041] The 3D model construction unit is used to construct a closed surface through a 3D model based on the open surface to obtain the geological surface model.

[0042] Optionally, the ore body surface model construction module specifically includes:

[0043] The profile connection unit is used to connect all the exploration line profile data by means of triangular surfaces using a profile connection method to form a closed surface, thereby obtaining the ore body surface model.

[0044] Optionally, the mineralization probability determination module specifically includes:

[0045] A priori probability and related parameter determination unit is used to determine the priori probability and related parameters of each evidence factor in the geological SGrid grid model based on the evidence weight method according to the known ore bodies in the ore body SGrid grid model; the evidence factor is the favorable mineralization information in the geological SGrid grid model, and the favorable mineralization information includes strata, rock bodies, fractures, geophysical anomalies and geochemical anomaly information; the related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance;

[0046] A logarithmic posterior likelihood ratio determination unit, used to determine the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model based on the prior probability and related parameters of each factor; each grid in the geological SGrid grid model includes multiple evidence factors;

[0047] The mineralization probability determination unit is used to determine the posterior probability corresponding to each grid according to the logarithmic posterior likelihood ratio of each grid, so as to obtain the mineralization probability of each grid.

[0048] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0049] The present invention can use GOCAD three-dimensional software to calculate the mineralization probability (posterior probability), expand the application scenarios of the software, and solve the problem of lack of three-dimensional quantitative prediction software due to copyright issues, providing important technical support for three-dimensional mineralization prediction and target area optimization, and improving the versatility of mineral prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 It is a schematic flow chart of the three-dimensional mineralization prediction method based on GOCAD of the present invention;

[0052] Figure 2 It is a structural schematic diagram of the three-dimensional mineralization prediction system based on GOCAD of the present invention;

[0053] Figure 3 is the posterior probability histogram of the Lujing-Shabazi area;

[0054] Figure 4 This is the posterior probability distribution map of the Lujing-Shabazi area. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Figure 1 The figure is a schematic flow chart of the three-dimensional mineralization prediction method based on GOCAD of the present invention. Figure 1 As shown, the three-dimensional mineralization prediction method based on GOCAD of the present invention comprises the following steps:

[0058] Step 100: Obtain geological data according to the area to be predicted. Specifically, first, obtain geological data with a scale of 1:500 to 1:2000 according to the area to be predicted, and the geological data mainly include: geological map, exploration line profile and mid-section plan. Then, the geological data is image corrected and digitized using MapGis or AutoCad software to obtain digital geological data. Finally, the digitized geological data is converted from two-dimensional to three-dimensional space using three-dimensional software 3Dmine or Surpac, and on this basis, the spatial data such as points and lines required for three-dimensional modeling are extracted to obtain the geological data. The geological data is stored in dxf format.

[0059] Step 200: Based on GOCAD software, a geological surface model of the area to be predicted is constructed according to geological data. The present invention uses GOCAD (Geological Object Computer Aided Design) three-dimensional modeling software developed by the University of Nancy, France to construct a three-dimensional geological surface model. The three-dimensional geological surface model is constructed using a discrete smooth interpolation method, which mainly uses geological data as linear constraints for spatial fitting to construct an open surface; then based on the open surface, a closed surface is obtained by establishing a 3dmodel module, that is, a geological surface model is obtained.

[0060] Step 300: The geological surface model is intersected with the original grid model to obtain the geological SGrid grid model of the area to be predicted. The boundary of the SGrid (original grid model) can be determined according to the area to be predicted, and the grid model is established according to the modeling range; the SGrid model is constrained and intersected by the geological surface model (i.e., a closed surface), and the geological bodies, fractures, etc. are divided into different surface areas to obtain the geological SGrid grid model.

[0061] Step 400: Obtaining profile data of each exploration line in the area to be predicted.

[0062] Step 500: Based on GOCAD software, construct an ore body surface model of the area to be predicted according to the exploration line profile data of all known ore bodies. The construction of the three-dimensional ore body surface model mainly uses the profile connection method to connect the ore body interpretation lines on all exploration line profiles through triangular surfaces to form a closed surface to obtain the ore body surface model.

[0063] Step 600: The ore body surface model is intersected with the original grid model to obtain the ore body SGrid grid model of the area to be predicted. According to the ore body SGrid grid model, the number of grids constrained by the known ore body can be counted, and the grid parameters of the known ore body can be obtained.

[0064] Step 700: According to the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted, the mineralization probability of each grid corresponding to the area in the geological SGrid grid model is determined based on the evidence weight method. The specific process is as follows:

[0065] Step 1: Based on the evidence weight method, determine the prior probability and related parameters of each evidence factor in the geological SGrid grid model. The related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance. First, take each extracted favorable mineralization information as an evidence factor, and calculate the prior probability of the evidence factor and the positive weight W that is favorable to mineralization according to the known ore bodies in the ore body SGrid grid model. + , negative weight W that is not conducive to mineralization - And significance C. The higher the C value, the closer the evidence factor is to the mineralization. The favorable mineralization information includes strata, rock mass, fractures, geophysical anomalies and geochemical anomalies. The favorable mineralization information is divided into different areas in step 300 and can be directly obtained when the geological SGrid grid model is obtained. The relevant calculation formula is as follows:

[0066] Assume that the total number of grids in the geological SGrid grid model corresponding to the area to be predicted is T, and the total number of grids of known ore bodies is D. Then the prior probability of the known ore body d is: P(d) = D / T, which is converted into the prior favorableness:

[0067]

[0068] Let e1, e2, e3, e4…e n There are n evidence factors, represented by evidence layers. Take the jth evidence layer, e j It indicates the appearance of evidence, i.e. the appearance of geological elements and identification marks related to mineralization, such as rock mass, faults, geochemical anomalies, geophysical anomalies, etc.; It means that geological evidence does not appear; Represents an unmineralized unit, then the formula is:

[0069]

[0070]

[0071]

[0072] In the formula, is the positive weight of the jth evidence factor that is favorable to mineralization, is the negative weight of the jth evidence factor that is unfavorable to mineralization, C j is the significance of the jth evidence factor.

[0073] Step 2: Based on the prior probability and related parameters of each factor, determine the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model; then determine the posterior probability corresponding to the grid according to the logarithmic posterior likelihood ratio of each grid, and obtain the mineralization probability of each grid. Each grid in the geological SGrid grid model includes multiple evidence factors, and the formula is as follows:

[0074]

[0075]

[0076] In the formula, O prior (d) is the prior favorableness, lnO post (d) is the logarithmic posterior likelihood ratio of the grid, P post (d) is the posterior probability.

[0077] According to formulas (1)-(4), the relevant parameters W of each evidence layer can be calculated: + , W - With C, we assign it to the corresponding evidence layer domain through the construction attribute function of the SGrid model. The specific operation process is as follows: (1) Create W for each evidence layer + , W - , lnO prior (d) Equal attributes and assign values; (2) Create the complement of each evidence layer within the scope of the predicted area and assign values. The complement refers to all grids outside the grid occupied by the evidence layer. The assignment rule is: when the attribute of a certain evidence layer is a positive weight, the attribute value of its complement is set to 0; when the attribute of the complement is a negative weight, the attribute of the evidence layer is set to 0. (3) Use the ApplyScript script (calculation function) to add all the attributes of each evidence layer to make each grid obtain the final weight, and calculate the logarithmic posterior likelihood ratio of each grid lnO through formula ⑤⑥. post (d) and the posterior probability P post (d) Finally, through hierarchical screening of posterior probability, the area is segmented across relatively large areas to provide a basis for delineating mineralization target areas or prospective areas.

[0078] Figure 2 Schematic diagram of the structure of the three-dimensional mineralization prediction system based on GOCAD of the present invention. Figure 2 As shown, the three-dimensional mineralization prediction system based on GOCAD of the present invention includes the following structure:

[0079] The geological data acquisition module 201 is used to acquire geological data according to the area to be predicted.

[0080] The geological surface model building module 202 is used to build a geological surface model of the area to be predicted according to the geological data based on GOCAD software.

[0081] The geological SGrid grid model acquisition module 203 is used to obtain the geological SGrid grid model of the area to be predicted by intersecting the geological surface model with the original grid model; the original grid model is a grid model established according to the modeling range.

[0082] The exploration line profile data acquisition module 204 is used to acquire the exploration line profile data of the area to be predicted.

[0083] The ore body surface model building module 205 is used to build the ore body surface model of the area to be predicted based on all exploration line profile data based on the GOCAD software.

[0084] The ore body SGrid grid model acquisition module 206 is used to obtain the ore body SGrid grid model of the area to be predicted by intersecting the ore body surface model with the original grid model.

[0085] The mineralization probability determination module 207 is used to determine the mineralization probability of each grid corresponding to the geological SGrid grid model based on the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted based on the evidence weight method.

[0086] As a specific embodiment, in the three-dimensional mineralization prediction system based on GOCAD of the present invention, the geological data acquisition module 201 specifically includes:

[0087] The geological data acquisition unit is used to acquire geological data with a scale of 1:500 to 1:2000 according to the area to be predicted; the geological data includes: geological map, exploration line profile and mid-section plan.

[0088] The preprocessing unit is used to perform image correction and digital processing on the geological data to obtain preprocessed geological data.

[0089] The space conversion unit is used to convert the preprocessed geological data from two-dimensional to three-dimensional space through three-dimensional software 3Dmine or Surpac, and extract the spatial data required for three-dimensional modeling to obtain the geological data.

[0090] As a specific embodiment, in the three-dimensional mineralization prediction system based on GOCAD of the present invention, the geological surface model construction module 202 specifically includes:

[0091] The fitting unit is used to adopt a discrete smooth interpolation method to perform spatial fitting based on the geological data to construct an open surface.

[0092] The 3D model construction unit is used to construct a closed surface based on the open surface through a 3D model module to obtain the geological surface model.

[0093] As a specific embodiment, in the three-dimensional mineralization prediction system based on GOCAD of the present invention, the ore body surface model construction module 205 specifically includes:

[0094] The profile connection unit is used to connect all the exploration line profile data by means of triangular surfaces using a profile connection method to form a closed surface, thereby obtaining the ore body surface model.

[0095] As a specific embodiment, in the three-dimensional mineralization prediction system based on GOCAD of the present invention, the mineralization probability determination module 207 specifically includes:

[0096] The prior probability and related parameter determination unit is used to determine the prior probability and related parameters of each evidence factor in the geological SGrid grid model based on the evidence weight method and according to the known ore bodies in the ore body SGrid grid model; the evidence factor is the favorable mineralization information in the geological SGrid grid model, and the favorable mineralization information includes strata, rock bodies, fractures, geophysical anomalies and geochemical anomaly information; the related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance.

[0097] The logarithmic posterior likelihood ratio determination unit is used to determine the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model based on the prior probability and related parameters of each factor; each grid in the geological SGrid grid model includes multiple evidence factors.

[0098] The mineralization probability determination unit is used to determine the posterior probability corresponding to each grid according to the logarithmic posterior likelihood ratio of each grid, so as to obtain the mineralization probability of each grid.

[0099] A specific example is provided below to further illustrate the solution of the present invention.

[0100] This example predicts the mineralization in the Lujing-Shabazi area. The specific process is as follows:

[0101] Step 1: Collect the 1:2000 geological map of the Lujing deposit, the 1:500 exploration line profile and other map data, correct and digitize the images of these data and convert them into three dimensions, and extract the three-dimensional modeling elements such as spatial points and lines.

[0102] Step 2: Use GOCAD semi-automatic process to establish the surface, including geological boundaries, fault planes, etc. Through the established geological interface, the surface model of the geological body is constructed to form a closed surface, including Cambrian strata, Indosinian medium-coarse-grained porphyritic granite, medium-fine-grained two-mica granite in the second stage of the early Yanshan period, and fine-grained biotite granite in the third stage of the early Yanshan period.

[0103] Step 3: Determine the modeling range of the three-dimensional grid model as 491240.48~497821.38, 2833186.74~2836308.32, and set the cube size to 30m×30m×20m, with a total of 1224599 cubes. The closed surfaces of the geological bodies obtained in step 2 are divided into different areas to form the SGrid grid model of the geological body.

[0104] Step 4: A total of 83 exploration line profiles were collected. Through the preliminary processing, the ore body interpretation lines of each profile were obtained. The ore body interpretation lines with the same name on each profile were connected from front to back through the triangulation network, and finally the two sides of the triangulation network were closed to form a closed ore body surface model, totaling 121. The closed ore body surface was used to clip the grid model established in step 3, and finally the grid model of the ore body was obtained.

[0105] Step 5: There are 6 evidence factors for the Lujing deposit. By counting the number of blocks for each evidence factor and the number of known ore bodies within the range of the evidence factor, the positive and negative weights and significance C value of each evidence factor are calculated according to the formula, as shown in Table 1.

[0106] Table 1 Evidence factor weight table

[0107]

[0108]

[0109] Step 6: Through the above steps, the weight value and prior probability value are obtained. Call the Apply script script function to calculate the posterior probability likelihood ratio, establish the posterior probability attribute, and use the script function again to calculate the posterior probability value. Its distribution histogram and distribution diagram are as follows Figure 3 and Figure 4 The posterior probability is divided into segments across relatively large areas through the distribution of the histogram, providing a basis for delineating mineralization target areas or prospecting areas.

[0110] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0111] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A three-dimensional mineralization prediction method based on GOCAD, characterized in that: include: Obtain geological data according to the area to be predicted; Based on GOCAD software, a geological surface model of the area to be predicted is constructed according to the geological data; The geological SGrid grid model of the area to be predicted is obtained by intersecting the geological surface model with the original grid model; the original grid model is a grid model established according to the modeling range; specifically, the SGrid grid model is constrained and intersected by the geological surface model, and the geological body and the fracture are divided into different domains to obtain the geological SGrid grid model; the geological surface model is a closed surface; at the same time, when the geological SGrid grid model is obtained by dividing into different domains, favorable mineralization information can be directly obtained, and the favorable mineralization information includes strata, rock masses, fractures, geophysical anomalies and geochemical anomalies information; Obtain profile data of each exploration line in the area to be predicted; Based on the GOCAD software, a surface model of the ore body in the area to be predicted is constructed according to all the exploration line profile data; The ore body surface model is intersected with the original grid model to obtain the ore body SGrid grid model of the area to be predicted; According to the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted, the mineralization probability of each grid corresponding to the area in the geological SGrid grid model is determined based on the weight of evidence method, specifically including: Based on the evidence weight method, according to the known ore bodies in the ore body SGrid grid model, the prior probability and related parameters of each evidence factor in the geological SGrid grid model are determined; the evidence factor is the favorable mineralization information in the geological SGrid grid model, and the favorable mineralization information includes strata, rock bodies, fractures, geophysical anomalies and geochemical anomalies; the related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance; The relevant calculation formula is as follows: The prior probability of the known ore body d is: P(d) = D / T, which is converted into the prior favorableness: Among them, T is the total number of grids in the geological SGrid grid model corresponding to the area to be predicted, and D is the total number of grids of known ore bodies; In the formula, e n There are n evidence factors, represented by evidence layers. Take the jth evidence layer. is the positive weight of the jth evidence factor that is favorable to mineralization, is the negative weight of the jth evidence factor that is unfavorable to mineralization, C j is the significance of the jth evidence factor, e j Indicates that evidence has emerged. It means that geological evidence has not appeared. Indicates unmineralized unit; Determining the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model based on the prior probability and related parameters of each factor; each grid in the geological SGrid grid model includes multiple evidence factors; According to the logarithmic posterior likelihood ratio of each grid, the posterior probability corresponding to the grid is determined to obtain the mineralization probability of each grid.

2. The three-dimensional mineralization prediction method based on GOCAD according to claim 1, characterized in that: The step of obtaining geological data according to the area to be predicted specifically includes: According to the area to be predicted, geological data with a scale of 1:500 to 1:2000 are obtained; the geological data include: geological map, exploration line profile and mid-section plan; Performing image correction and digital processing on the geological data to obtain pre-processed geological data; The preprocessed geological data is converted from two-dimensional to three-dimensional space using three-dimensional software 3Dmine or Surpac, and the spatial data required for three-dimensional modeling is extracted to obtain the geological data.

3. The three-dimensional mineralization prediction method based on GOCAD according to claim 1, characterized in that: The method of constructing a geological surface model of the area to be predicted based on the geological data based on the GOCAD software specifically includes: Using a discrete smooth interpolation method, spatial fitting is performed based on the geological data to construct an open surface; Based on the open surface, a closed surface is constructed through a 3dmodel module to obtain the geological surface model.

4. The three-dimensional mineralization prediction method based on GOCAD according to claim 1, characterized in that: Based on the GOCAD software, the surface model of the ore body in the area to be predicted is constructed according to all the exploration line profile data, specifically including: The profile connection method is used to connect all the exploration line profile data in the form of triangular surfaces to form a closed surface, thereby obtaining the ore body surface model.

5. A three-dimensional mineralization prediction system based on GOCAD, characterized in that: include: A geological data acquisition module is used to acquire geological data according to the area to be predicted; A geological surface model building module is used to build a geological surface model of the area to be predicted according to the geological data based on GOCAD software; The geological SGrid grid model acquisition module is used to obtain the geological SGrid grid model of the area to be predicted by intersecting the geological surface model with the original grid model; the original grid model is a grid model established according to the modeling range; specifically, the SGrid grid model is constrained and intersected by the geological surface model, and the geological body and the fracture are divided into different domains to obtain the geological SGrid grid model; the geological surface model is a closed surface; at the same time, when the geological SGrid grid model is obtained by dividing into different domains, favorable mineralization information can be directly obtained, and the favorable mineralization information includes strata, rock masses, fractures, geophysical anomalies and geochemical anomalies information; An exploration line profile data acquisition module is used to acquire the profile data of each exploration line in the area to be predicted; An ore body surface model building module is used to build an ore body surface model of the area to be predicted based on all exploration line profile data based on the GOCAD software; An ore body SGrid grid model acquisition module is used to obtain the ore body SGrid grid model of the area to be predicted by intersecting the ore body surface model with the original grid model; The mineralization probability determination module is used to determine the mineralization probability of each grid corresponding to the area in the geological SGrid grid model based on the ore body SGrid grid model and the geological SGrid grid model of the area to be predicted, based on the weight of evidence method, and specifically includes: A priori probability and related parameter determination unit is used to determine the priori probability and related parameters of each evidence factor in the geological SGrid grid model based on the evidence weight method according to the known ore bodies in the ore body SGrid grid model; the evidence factor is the favorable mineralization information in the geological SGrid grid model, and the favorable mineralization information includes strata, rock bodies, fractures, geophysical anomalies and geochemical anomaly information; the related parameters include positive weights that are favorable to mineralization, negative weights that are unfavorable to mineralization, and significance; The relevant calculation formula is as follows: The prior probability of the known ore body d is: P(d) = D / T, which is converted into the prior favorableness: Among them, T is the total number of grids in the geological SGrid grid model corresponding to the area to be predicted, and D is the total number of grids of known ore bodies; In the formula, e n There are n evidence factors, represented by evidence layers. Take the jth evidence layer. is the positive weight of the jth evidence factor that is favorable to mineralization, is the negative weight of the jth evidence factor that is unfavorable to mineralization, C j is the significance of the jth evidence factor, e j Indicates that evidence has emerged. It means that geological evidence has not appeared. Indicates unmineralized unit; A logarithmic posterior likelihood ratio determination unit, used to determine the logarithmic posterior likelihood ratio of each grid in the geological SGrid grid model based on the prior probability and related parameters of each factor; each grid in the geological SGrid grid model includes multiple evidence factors; The mineralization probability determination unit is used to determine the posterior probability corresponding to each grid according to the logarithmic posterior likelihood ratio of each grid, so as to obtain the mineralization probability of each grid.

6. The three-dimensional mineralization prediction system based on GOCAD according to claim 5 is characterized in that: The geological data acquisition module specifically includes: A geological data acquisition unit, used to acquire geological data at a scale of 1:500 to 1:2000 according to the area to be predicted; the geological data includes: a geological map, a profile of an exploration line, and a mid-section plan; A preprocessing unit, used for performing image correction and digital processing on the geological data to obtain preprocessed geological data; The space conversion unit is used to convert the preprocessed geological data from two-dimensional to three-dimensional space through three-dimensional software 3Dmine or Surpac, and extract the spatial data required for three-dimensional modeling to obtain the geological data.

7. The GOCAD-based three-dimensional mineralization prediction system according to claim 5 is characterized in that: The geological surface model construction module specifically includes: A fitting unit, used for performing spatial fitting based on the geological data by using a discrete smooth interpolation method to construct an open surface; The 3D model construction unit is used to construct a closed surface based on the open surface through a 3D model module to obtain the geological surface model.

8. The GOCAD-based three-dimensional mineralization prediction system according to claim 5 is characterized in that: The ore body surface model construction module specifically includes: The profile connection unit is used to connect all the exploration line profile data by means of triangular surfaces using a profile connection method to form a closed surface, thereby obtaining the ore body surface model.

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