Granite type uranium mine prediction method based on digital model

Abstract

The invention belongs to the technical field of three-dimensional uranium mine geological resource prediction research, and particularly relates to a granite type uranium mine prediction method based on a digital model, which comprises the following steps: S1, constructing a three-dimensional digital geological model of a research area; s2, dividing prediction units, establishing a three-dimensional block model of a research area, and establishing an attribute model of each ore control element for metallogenic prediction; s3, according to the three-dimensional block model of the research area, uranium metallogenic favorable information is extracted; s4, statistical analysis and assignment are carried out on uranium metallogenic favorable information; s5, comprehensive metallogenic prediction is carried out through an evidence weight method and an information amount method; and S6, delineating a uranium metallogenic prospective area and estimating the resource quantity to realize three-dimensional quantitative prediction of the granite type uranium mine. The method can efficiently, quickly and objectively evaluate the uranium mineralization potential of a region.

Description

Technical Field

[0001] This invention belongs to the field of three-dimensional uranium geological resource prediction research technology, specifically involving a method for predicting granite-type uranium deposits based on digital models. Background Technology

[0002] In recent years, the international uranium geology community has witnessed rapid development in 3D modeling and prediction technologies. Extensive predictive research has been conducted in deep geology and geophysical structures, successfully predicting and guiding the exploration and discovery of deep uranium mineralization bodies. With the development of information technology and GIS technology, mineral resource evaluation methods and tools are constantly being improved and enhanced, particularly the traditional methods for mineral resource evaluation, simplifying the prediction and evaluation process and achieving scientific, convenient, efficient, and quantitative operations. Currently, comprehensive mineral resource evaluation and prediction has entered the information age. 3D modeling and spatial analysis tools are continuously being improved, and the selection and assignment of predictive variables, among other predictive information transformation processes, have been automated, improving the timeliness and objectivity of mineral resource evaluation. Information technology can directly access and manipulate data based on spatial relationships, constructing a system engineering framework that integrates ore-forming information analysis and extraction, unit division, predictive factor optimization, location prediction, and quantitative evaluation of mineral resources.

[0003] Granite-type uranium deposits are characterized by complex terrain and deep ore bodies, making them concealed and difficult to identify, thus increasing the difficulty of prospecting. Traditional two-dimensional prediction methods are no longer sufficient for modern needs. Extending traditional two-dimensional prospecting to three-dimensional space is the mainstream research direction for the location and quantitative prediction of concealed underground ore bodies. Research on three-dimensional mineralization prediction in granite-type uranium deposits is of great significance for the development of mineralization prediction theory and the search for concealed ore bodies. It expands the scope of prospecting thinking and provides new prospecting strategies. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting granite-type uranium deposits based on digital models. This method constructs a three-dimensional digital model of granite-type uranium deposits, extracts favorable mineralization information and performs statistical analysis, analyzes the relationship between various prediction factors and uranium mineralization, especially the direct and indirect reflections of control, association, or results, and uses a three-dimensional quantitative prediction method to complete the quantitative prediction and evaluation work. It can efficiently, quickly, and objectively evaluate the uranium mineralization potential of a region and is an effective method for medium-to-large scale three-dimensional comprehensive information uranium mineral exploration.

[0005] Technical solution to achieve the purpose of this invention:

[0006] A method for predicting granite-type uranium deposits based on a digital model, comprising:

[0007] Step S1: Construct a three-dimensional digital geological model of the study area;

[0008] Step S2: Divide the prediction units, establish a three-dimensional block model of the study area, and establish attribute models of each ore-controlling element for mineralization prediction;

[0009] Step S3: Extract favorable information for uranium mineralization based on the three-dimensional block model of the study area;

[0010] Step S4: Statistical analysis and value assignment of favorable information on uranium mineralization;

[0011] Step S5: Combine the evidence weight method and the information quantity method to predict mineralization;

[0012] Step S6: Delineate uranium mineralization potential areas and estimate resource volume to achieve three-dimensional quantitative prediction of granite-type uranium deposits.

[0013] Further, step S1 includes:

[0014] Based on the strata revealed by boreholes in the study area, the borehole elevation and depth were analyzed to construct a digital geological model of the stratigraphy-rock mass of the granite-type uranium deposit. The two-dimensional structural lines of the study area were transformed into a three-dimensional structural model. The three-dimensional structural model was overlaid with the stratigraphy-rock mass digital geological model to analyze the spatial relationship between the structure and the ore body. Within the uranium deposit and its surrounding area, the boundaries of the structural alteration zones in the existing exploration line profiles were used to construct a three-dimensional digital geological model of the structural alteration zones of the uranium deposit and its surrounding area.

[0015] The comprehensive construction of the stratigraphic-rock mass digital geological model, the three-dimensional structural model, and the three-dimensional digital geological model of the tectonic alteration zone of the granite-type uranium deposit, further integrates the topographic surface model, the ore body model, and the uranium component anomaly model to form a three-dimensional digital geological model of the granite-type uranium deposit.

[0016] Further, step S2 includes:

[0017] A three-dimensional block model of the study area is established based on the scope of the digital geological model: according to the requirements of accuracy and efficiency of three-dimensional mineralization prediction calculation, the size of the block unit is set, the total number of block units in the block model is counted, and a three-dimensional block model of the study area is established.

[0018] Based on the three-dimensional block model, attribute models of each ore-controlling element for mineralization prediction are established: For each ore-controlling element, corresponding attribute values ​​are assigned to each unit block of the three-dimensional block model, and attribute models of each ore-controlling element are established.

[0019] Further, step S3 includes:

[0020] Based on the three-dimensional block model of the study area constructed in step S2, spatial distribution data of important ore-controlling elements such as rock mass, structure, alteration zone, and geochemical anomaly are extracted; for each type of ore-controlling element, the favorable interval of each ore-controlling element is selected by statistical analysis of its spatial correlation with known ore bodies, and mineralization favorable elements are screened from each ore-controlling element to determine favorable information for uranium mineralization.

[0021] Within the spatial scope of the three-dimensional block model, the distribution characteristics and patterns of each ore-controlling element in different unit blocks are analyzed, and the influence of each unit block on mineral prediction is explored. Based on the determined favorable intervals of each ore-controlling element, the number of unit blocks that meet the conditions of each favorable interval and the number of these unit blocks that coincide with known mineralized blocks are counted. The hierarchical information of each ore-controlling element is calculated, and a statistical table of hierarchical information is established.

[0022] Based on the established hierarchical information quantity statistics table, the favorable intervals of each ore-controlling element are integrated with the corresponding hierarchical information quantity to construct a hierarchical information quantity comprehensive statistical chart of ore-forming information that reflects the comprehensive distribution of the favorable degree of ore-forming, thereby realizing the quantification and visualization of ore-forming prediction information.

[0023] Further, step S4 includes:

[0024] Based on the attribute model established in step S2, and combined with the favorable range of ore-controlling elements and ore-forming favorable elements in the study area in step S3, the borehole ratio of each ore-forming favorable element in different ranges of variation and different mineralization levels is analyzed and studied. The information values ​​of each ore-forming favorable element are extracted and analyzed quantitatively, the degree of ore control of each ore-forming favorable element is statistically analyzed, and each ore-forming favorable element is assigned an ore-forming favorable factor value.

[0025] Furthermore, the mineralization favorable factor value in step S4 is determined based on the borehole ratio of different mineralization levels within different variation ranges of the favorable mineralization factors, combined with the knowledge and experience of geological experts. The value range is 0 to 1, and the higher the value, the more favorable it is.

[0026] Further, step S5 includes:

[0027] The evidence weight method is used to classify and statistically analyze the posterior probability values, and the information quantity method is used to statistically analyze the mineral-bearing information in different information quantity ranges. By coupling the evidence weight method and the information quantity method, the range of favorable mineralization areas is comprehensively delineated.

[0028] Further, step S6 includes:

[0029] Based on the favorable mineralization area delineated in step S5, the calculation results of the combined evidence weight method and information quantity method are used to apply dual constraints and statistical analysis to obtain the favorable mineralization blocks in the exploration area. Based on the block distribution pattern of each mineralization factor, the geological background and mineralization regularity of the study area are further combined in space to jointly predict the location of the unknown deep ore bodies in the favorable area. Through human-machine collaboration, the prospective mineralization area is delineated.

[0030] Based on the relationship between existing resources and known ore body volume, the resource quantity of the prediction area is estimated, and the ore content of favorable blocks in each level of the prediction area is calculated to obtain the predicted number of new blocks.

[0031] The beneficial technical effects of this invention are as follows:

[0032] 1. The present invention provides a method for predicting granite-type uranium deposits based on digital models, which can efficiently, quickly and objectively evaluate the uranium mineralization potential of granite-type uranium deposits. It is an effective three-dimensional uranium mineralization prospecting method based on comprehensive information.

[0033] 2. The present invention provides a method for predicting granite-type uranium deposits based on a digital model, which breaks through the two-dimensional abstract method for selecting uranium target areas. It uses an intuitive three-dimensional digital model and combines various mineralization favorable information to complete the spatial superposition analysis of multiple prediction factors, form a three-dimensional spatial uranium mineralization favorable information assignment, and delineate uranium mineralization prospect areas, ultimately achieving three-dimensional quantitative prediction.

[0034] 3. The present invention provides a method for predicting granite-type uranium deposits based on digital models, which can economically and efficiently evaluate the uranium mineralization potential of a region. It is also an effective method for three-dimensional mineral exploration using big data and comprehensive information. Attached Figure Description

[0035] Figure 1 A flowchart of a method for predicting granite-type uranium deposits based on a digital model provided by the present invention;

[0036] Figure 2 This is a digital geological model diagram of the rock mass of the Mianhuakeng uranium deposit in an embodiment of the present invention;

[0037] Figure 3 This is a digital geological model diagram of the structural alteration zone of the Mianhuakeng uranium deposit in an embodiment of the present invention;

[0038] Figure 4 This is a cubic block model diagram of the prediction unit for the Mianhuakeng uranium deposit in an embodiment of the present invention;

[0039] Figure 5 This is a three-dimensional spatial quantitative prediction map of the Mianhuakeng uranium deposit in an embodiment of the present invention. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0041] like Figure 1 As shown, this invention provides a method for predicting granite-type uranium deposits based on a digital model. It utilizes a three-dimensional geological model and a digital quantitative prediction model for granite-type uranium deposits, applying the mineralization favorability calculation method within the prediction and evaluation subsystem. By fully leveraging previous data, a three-dimensional geological model and a three-dimensional digital quantitative prediction model are established for the study area. Mineralization favorability information is extracted and statistically analyzed to calculate uranium mineralization favorability, initially delineating potential uranium mineralization areas. Then, through limited drilling verification, the geological characteristics and prospecting information of uranium mineralization are further analyzed, gradually narrowing the uranium exploration range and ultimately identifying the target area. The specific steps include:

[0042] Constructing and establishing three-dimensional geological models and three-dimensional digital quantitative prediction models

[0043] Step S1: Construct a three-dimensional digital geological model of the study area

[0044] Based on the strata revealed by boreholes in the study area, the borehole elevation and depth were analyzed to construct a digital geological model of the stratigraphy-rock mass of the granite-type uranium deposit. Two-dimensional structural lines in the study area were converted into a three-dimensional structural model, which was then overlaid with the stratigraphy-rock mass digital geological model to analyze the spatial relationship between the structures and the ore bodies. Within the uranium deposit and its surrounding area, structural alteration zone boundaries were constructed using existing exploration line profiles, generating structural alteration zone boundary files. These files were imported into 3D modeling software, and a three-dimensional digital geological model of the uranium deposit and its surrounding structural alteration zones was established using modeling methods. In addition, topographic and geomorphic surface models, ore body models, and uranium component anomaly models were also constructed. By comprehensively constructing the stratigraphy-rock mass digital geological model, the three-dimensional structural model, and the three-dimensional structural alteration zone model of the granite-type uranium deposit, and further integrating the topographic and geomorphic surface model, the ore body model, and the uranium component anomaly model, a three-dimensional digital geological model of the granite-type uranium deposit was formed.

[0045] Step S2: Divide the prediction unit, establish a three-dimensional block model of the study area, and establish attribute models of each ore-controlling element for mineralization prediction.

[0046] A three-dimensional block model of the study area is established based on the scope of the digital geological model: according to the requirements of accuracy and efficiency of three-dimensional mineralization prediction calculation, the size of the block unit is set, the total number of block units in the block model is counted, and a three-dimensional block model of the study area is established.

[0047] Based on the three-dimensional block model, attribute models of each ore-controlling element for mineralization prediction are established: For each ore-controlling element, corresponding attribute values ​​are assigned to each unit block of the three-dimensional block model, and attribute models of each ore-controlling element are established.

[0048] Step S3: Extract favorable information for uranium mineralization based on the three-dimensional block model of the study area.

[0049] Based on the three-dimensional block model of the study area constructed in step S2, spatial distribution data of important ore-controlling elements such as rock mass, structure, alteration zone, and geochemical anomaly are extracted; for each type of ore-controlling element, the favorable interval of each ore-controlling element is selected by statistical analysis of its spatial correlation with known ore bodies (blocks), and mineralization favorable elements are screened from each ore-controlling element to determine favorable information for uranium mineralization.

[0050] Within the spatial scope of the three-dimensional block model, the distribution characteristics and patterns of each ore-controlling element in different unit blocks are analyzed, and the influence of each unit block on mineral prediction is explored. Based on the determined favorable intervals of each ore-controlling element, the number of unit blocks that meet the conditions of each favorable interval and the number of these unit blocks that overlap with known mineralized blocks (ore blocks) are counted. The hierarchical information of each ore-controlling element is calculated, and a statistical table of hierarchical information is established.

[0051] Based on the established hierarchical information quantity statistics table, the favorable intervals of each ore-controlling element are integrated with the corresponding hierarchical information quantity to construct a hierarchical information quantity comprehensive statistical chart of ore-forming information that reflects the comprehensive distribution of the favorable degree of ore-forming, thereby realizing the quantification and visualization of ore-forming prediction information.

[0052] Step S4: Statistical analysis and value assignment of favorable information on uranium mineralization.

[0053] Based on the attribute model established in step S2, and combined with the favorable range of ore-controlling elements and ore-forming favorable elements in the study area in step S3, the borehole ratio of each ore-forming favorable element in different ranges of variation and different mineralization levels is analyzed and studied. The information values ​​of each ore-forming favorable element are extracted and analyzed quantitatively, the degree of ore control of each ore-forming favorable element is statistically analyzed, and each ore-forming favorable element is assigned an ore-forming favorable factor value.

[0054] The value of favorable mineralization factors is generally determined based on the borehole ratio of different mineralization levels within different variation ranges of the favorable mineralization factors, combined with the knowledge and experience of geological experts. The value range is 0 to 1, and the higher the value, the more favorable it is.

[0055] Step S5: Comprehensive mineralization prediction using the evidence weight method and the information quantity method.

[0056] The evidence weight method is used to classify and statistically analyze the posterior probability values, and the information quantity method is used to statistically analyze the mineral-bearing information in different information quantity ranges. By coupling the evidence weight method and the information quantity method, the range of favorable mineralization areas is comprehensively delineated.

[0057] Step S6: Delineate uranium mineralization potential areas and estimate resource quantities to achieve three-dimensional quantitative prediction of granite-type uranium deposits.

[0058] Based on the favorable mineralization area delineated in step S5, the calculation results of the combined evidence weight method and information quantity method are used to apply dual constraints and statistical analysis to obtain the favorable mineralization blocks in the exploration area. Based on the block distribution pattern of each mineralization factor, the geological background and mineralization regularity of the study area are further combined in space to jointly predict the location of unknown deep ore bodies in the favorable area. Through human-machine collaboration, the prospective mineralization area is delineated.

[0059] Based on the relationship between existing resources and known ore body volume, the resource quantity of the prediction area is estimated, and the ore content of favorable blocks in each level of the prediction area is calculated to obtain the predicted number of new blocks.

[0060] Example 1

[0061] Taking the Mianhuakeng granite-type uranium deposit and its surrounding area as an example, this embodiment provides a method for predicting granite-type uranium deposits based on a digital model, specifically including the following steps:

[0062] Step S1: Construct a three-dimensional digital geological model of the study area

[0063] Based on the strata revealed by boreholes in the study area, and analyzing the borehole elevation and depth, the rock mass model of the Mianhuakeng granite-type uranium deposit was constructed as follows: the upper part is the Indosinian Youdong rock mass, and the lower part is the Yanshanian Yangtze River rock mass, with a modeling depth range of -1000m. The completed digital geological model of the rock mass is as follows: Figure 2 As shown, the two-dimensional structural lines of the study area are constructed into a three-dimensional structural model and visualized. The structural model is overlaid with the stratigraphic model to display the spatial distribution and extension direction of the structures, the spatial variation relationship between the stratigraphy and the structures, and to analyze the spatial relationship between the structures and the ore bodies. Within the Mianhuakeng uranium deposit and its surrounding area, the structural alteration zone boundaries in the existing exploration line profile are used, supplemented by the structural alteration zone boundaries in the mid-section plan view. The structural alteration zone boundaries are modified according to the software data requirements. The modified structural alteration zone boundary files are imported into the 3D modeling software, and a digital geological model of the Mianhuakeng uranium deposit and its surrounding structural alteration zones is established based on the modeling method, as shown below. Figure 3 As shown in the figure. In addition, topographic and geomorphological surface models, ore body models, and uranium component anomaly models were also constructed. Based on the established digital geological model, subsequent three-dimensional geological prediction models were built.

[0064] Step S2: Divide the prediction unit, establish a three-dimensional block model of the study area, and establish attribute models of each ore-controlling element for mineralization prediction.

[0065] Based on the scope of the digital geological model established in step S1, a three-dimensional block model of the study area is established, such as... Figure 4As shown, the model block size adopts a row × column × layer approach, counting the total number of unit blocks in the block model, and simultaneously establishing attribute models for each ore-controlling element in the prediction model. To maximize computer processing speed while ensuring the block unit size meets the accuracy requirements of 3D mineralization prediction calculations, the entire solid model area is divided into blocks with a block unit size of 20m × 20m × 20m. The entire study area is divided into a total of 512,360 unit blocks, of which 17,511 are known ore-bearing ore bodies. A 3D spatial reconstruction model of each ore-controlling element is then established.

[0066] Step S3: Extract favorable information for uranium mineralization based on the three-dimensional block model of the study area.

[0067] Based on the three-dimensional block model of the study area established in step S2, important ore-controlling elements such as rock mass, structure, alteration, and geochemical anomalies are extracted, favorable intervals for each element are selected, and favorable mineralization information is determined. The impact of various geological elements on mineral prediction is explored by studying the distribution of each geological element unit within the study area. Based on the statistical analysis of information quantities at different levels for each element, the number of blocks and ore blocks is determined, and a comprehensive statistical map of mineralization information with graded information quantities is established.

[0068] Step S4: Statistical analysis and value assignment of favorable information on uranium mineralization.

[0069] Based on the attribute model established in step S2, and combined with the metallogenic geological regularities (favorable ranges of ore-controlling elements) and favorable ore-forming elements in step S3, the borehole ratios of each element at different mineralization levels within different variation ranges are analyzed and studied. Information values ​​of each favorable ore-forming element are extracted and analyzed quantitatively, and the degree of ore control by each favorable ore-forming element is statistically analyzed. The near-north-south trending ore-bearing structure is the most favorable condition for uranium mineralization in this area. Each favorable ore-forming element is assigned a favorable ore-forming factor value. Generally, the value is determined based on the borehole ratios of different mineralization levels within different variation ranges of the factors in each favorable ore-forming element, combined with the understanding and experience of geological experts. The value range is 0–1, with higher values ​​indicating greater favorable conditions.

[0070] Step S5: Comprehensive mineralization prediction using the evidence weight method and the information quantity method.

[0071] Numerical simulation methods were used to quantitatively study the favorable information for uranium mineralization in step S4 through the evidence weight method and the information quantity method. The results of the two geological prediction and analysis methods were subjected to dual constraints to comprehensively analyze the impact of various ore-controlling elements on mineralization. In the evidence weight method, the posterior probability directly reflects the magnitude of the prospecting probability; the higher the posterior probability value, the more favorable the mineralization of the block. The posterior probability values ​​were statistically graded, and the number of ore bodies, the number of blocks, the proportion of blocks, the proportion of ore bodies, and the ratio of ore body ratio to block ratio within the interval were statistically analyzed when the posterior probability value exceeded a certain value. Based on the comprehensive analysis of the evidence weight method results, favorable mineralization intervals were selected. Under the guidance of the prospecting prediction model, prediction elements were selected, and the information quantity method was used for mineralization prediction. Statistical analysis of mineralization information in different information quantity value intervals showed that the higher the information quantity value, the higher the mineralization value area. A prediction method coupling three-dimensional evidence weight and prospecting information quantity was adopted. As the information quantity value and posterior probability value increased, the mineralization rate of the delineated favorable area increased, gradually narrowing the range of the favorable area.

[0072] Step S6: Delineate uranium mineralization potential areas and estimate resource quantities to achieve three-dimensional quantitative prediction of granite-type uranium deposits.

[0073] Based on the favorable mineralization areas delineated in step S5, the locations of unknown deep ore bodies within these favorable areas are jointly predicted. Through a combined human-machine approach, the potential mineralization areas are delineated based on the spatial distribution of various mineralization factors, regional geological characteristics, and the degree of mineralization favorability. A total of one Class A mineralization prediction area, two Class B mineralization prediction areas, and one Class C mineralization prediction area were delineated in the study area. Figure 5 As shown. The resource quantity in the predicted area is estimated based on the relationship between existing resources and known ore body volume. The ore-bearing rate of favorable blocks in each level of the predicted area is calculated to obtain the predicted number of new blocks. According to the mineralization prediction formula: Where C represents the resource quantity in the predicted area, and V sum The volume of the ore body is estimated for the total prediction area, where V0 is the known ore body volume and N is the proven resource quantity (in tons). The newly added uranium resource quantity (in tons) is calculated, and the total predicted resource quantity (in tons) is determined.

[0074] The present invention has been described in detail above with reference to the accompanying drawings and embodiments. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. All contents not described in detail in the present invention can be derived from existing technologies.

Claims

1. A method for predicting a granite-type uranium deposit based on a digital model, characterized in that, include: Step S1: Construct a three-dimensional digital geological model of the study area; Step S2: Divide the prediction units, establish a three-dimensional block model of the study area, and establish attribute models of each ore-controlling element for mineralization prediction; Step S3: Extract favorable information for uranium mineralization based on the three-dimensional block model of the study area; Step S4: Statistical analysis and value assignment of favorable information on uranium mineralization; Step S5: Combine the evidence weight method and the information quantity method to predict mineralization; Step S6: Delineate uranium mineralization potential areas and estimate resource volume to achieve three-dimensional quantitative prediction of granite-type uranium deposits.

2. A method for predicting granite-type uranium deposits based on a digital model according to claim 1, characterized in that, Step S1 includes: Based on the strata revealed by boreholes in the study area, the borehole elevation and depth were analyzed to construct a digital geological model of the stratigraphy-rock mass of the granite-type uranium deposit. The two-dimensional structural lines of the study area were transformed into a three-dimensional structural model. The three-dimensional structural model was overlaid with the stratigraphy-rock mass digital geological model to analyze the spatial relationship between the structure and the ore body. Within the uranium deposit and its surrounding area, the boundaries of the structural alteration zones in the existing exploration line profiles were used to construct a three-dimensional digital geological model of the structural alteration zones of the uranium deposit and its surrounding area. The comprehensive construction of the stratigraphic-rock mass digital geological model, the three-dimensional structural model, and the three-dimensional digital geological model of the tectonic alteration zone of the granite-type uranium deposit, further integrates the topographic surface model, the ore body model, and the uranium component anomaly model to form a three-dimensional digital geological model of the granite-type uranium deposit.

3. A method of predicting granite-type uranium deposits based on a digital model according to claim 2, characterized in that, Step S2 includes: A three-dimensional block model of the study area is established based on the scope of the digital geological model: according to the requirements of accuracy and efficiency of three-dimensional mineralization prediction calculation, the size of the block unit is set, the total number of block units in the block model is counted, and a three-dimensional block model of the study area is established. Based on the three-dimensional block model, attribute models of each ore-controlling element for mineralization prediction are established: For each ore-controlling element, corresponding attribute values ​​are assigned to each unit block of the three-dimensional block model, and attribute models of each ore-controlling element are established.

4. The method for predicting granite-type uranium deposits based on a digital model according to claim 3, characterized in that, Step S3 includes: Based on the three-dimensional block model of the study area constructed in step S2, spatial distribution data of important ore-controlling elements such as rock mass, structure, alteration zone, and geochemical anomaly are extracted; for each type of ore-controlling element, the favorable interval of each ore-controlling element is selected by statistical analysis of its spatial correlation with known ore bodies, and mineralization favorable elements are screened from each ore-controlling element to determine favorable information for uranium mineralization. Within the spatial scope of the three-dimensional block model, the distribution characteristics and patterns of each ore-controlling element in different unit blocks are analyzed, and the influence of each unit block on mineral prediction is explored. Based on the determined favorable intervals of each ore-controlling element, the number of unit blocks that meet the conditions of each favorable interval and the number of these unit blocks that coincide with known mineralized blocks are counted. The hierarchical information of each ore-controlling element is calculated, and a statistical table of hierarchical information is established. Based on the established hierarchical information quantity statistics table, the favorable intervals of each mineralization control element are integrated with the corresponding hierarchical information quantity to construct a comprehensive statistical chart of mineralization information reflecting the comprehensive distribution of the favorable degree of mineralization, thereby realizing the quantification and visualization of mineralization prediction information.

5. The method for predicting granite-type uranium deposits based on a digital model according to claim 4, characterized in that, Step S4 includes: Based on the attribute model established in step S2, and combined with the favorable range of ore-controlling elements and ore-forming favorable elements in the study area in step S3, the borehole ratio of each ore-forming favorable element in different ranges of variation and different mineralization levels is analyzed and studied. The information values ​​of each ore-forming favorable element are extracted and analyzed quantitatively, the degree of ore control of each ore-forming favorable element is statistically analyzed, and each ore-forming favorable element is assigned an ore-forming favorable factor value.

6. The method for predicting granite-type uranium deposits based on a digital model according to claim 5, characterized in that, In step S4, the favorable mineralization factor value is determined based on the borehole ratio of different mineralization levels within different variation ranges of the favorable mineralization factors, combined with the knowledge and experience of geological experts. The value range is 0 to 1, and the higher the value, the more favorable it is.

7. The method for predicting granite-type uranium deposits based on a digital model according to claim 5, characterized in that, Step S5 includes: The posterior probability values ​​are classified and statistically analyzed using the evidence weight method, and mineral-bearing information in different information value ranges is statistically analyzed using the information quantity method. By coupling the evidence weight method and the information quantity method, the range of favorable mineralization areas is comprehensively delineated.

8. The method for predicting granite-type uranium deposits based on a digital model according to claim 7, characterized in that, Step S6 includes: Based on the favorable mineralization area delineated in step S5, the calculation results of the combined evidence weight method and information quantity method are used to apply dual constraints and statistical analysis to obtain the favorable mineralization blocks in the exploration area. Based on the block distribution pattern of each mineralization factor, the geological background and mineralization regularity of the study area are further combined in space to jointly predict the location of the unknown deep ore bodies in the favorable area. Through human-machine collaboration, the prospective mineralization area is delineated. Based on the relationship between existing resources and known ore body volume, the resource quantity of the prediction area is estimated, and the ore content of favorable blocks in each level of the prediction area is calculated to obtain the predicted number of new blocks.

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