Mine fracture and ore-free fracture identification method

Abstract

The invention discloses a mine fracture and non-mine fracture identification method, which particularly relates to the technical field of geological exploration, and comprises the following steps: based on a geological database of a mine area, establishing a three-dimensional data volume model of the mine area fracture, dividing the data volume model into monitoring sub-areas, extracting each fracture structure feature of the mine area in each monitoring sub-area, and determining the fracture structure feature of the mine area in each monitoring sub-area; the method comprises the following steps: firstly selecting an ore-bearing fault zone with mineralization potential in a mining area, extracting fault structure complexity degree characteristics and mineralization information of each monitoring sub-area through a data volume model based on a priority sequence, calculating a fault structure complexity index and a mineralization element abnormal index, and sequentially evaluating the correlation between fracture and mineralization based on the priority sequence. According to the method, a priority ranking mechanism is introduced, screening is carried out firstly, then fine evaluation is carried out, the data processing efficiency and accuracy are improved, the prospecting efficiency is improved, exploration has targeting performance, and standardization and objectification of the recognition process are achieved through multi-dimensional index quantification and correlation model construction.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and more specifically, to a method for identifying mineralized fractures and non-mineralized fractures. Background Technology

[0002] Mineralization faults are key structural features controlling the occurrence and enrichment of ore bodies. Accurate identification of mineralization faults versus non-mineralization faults directly determines the efficiency of mineral exploration and the cost of resource development. In deep mineral exploration and exploration in complex structural areas, quickly distinguishing between the two is crucial for shortening the exploration cycle and avoiding ineffective drilling. The Zhuanshanzi gold deposit is located at the transition zone between the Paleo-Asian Ocean metallogenic belt and the Circum-Pacific metallogenic belt, with highly complex geological structures and mineralization processes. The area features well-developed folds and faults, and intense and frequent magmatic activity, providing favorable conditions for the formation of gold deposits in the region.

[0003] In order to accelerate the new round of strategic action for mineral exploration breakthroughs, respond to the national policy of strengthening domestic exploration and development of important energy mineral resources and increasing reserves and production, give full play to the decisive role of the market in resource allocation, and in light of the current status of gold reserves in this region, it is necessary to conduct a systematic study on the main ore-controlling structures in the mining area, so as to discover their spatial relationship with the ore body and their metallogenic mechanism.

[0004] However, in practical use, it still has some shortcomings. For example, the existing mineralized fracture and non-mineralized fracture identification technology relies on a single physical property parameter. In complex structural mining areas, the physical property anomalies overlap severely, resulting in a high misjudgment rate and waste of exploration resources.

[0005] Traditional technologies are limited by data acquisition and processing methods, lacking the ability to evaluate the depth distribution of mineral resources in mining areas. They mostly rely on surface geological observations to infer deep features, resulting in limited ability to identify deeply buried faults. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for identifying mineralized fractures and non-mineralized fractures, which addresses the problems raised in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying mineralized fractures and non-mineralized fractures, comprising the following steps:

[0008] Step S01: Based on the geological database of the mining area, establish a three-dimensional data volume model of the faults in the mining area.

[0009] Step S02: Divide the three-dimensional data volume model of the mining area fracture into n monitoring sub-regions, and number each monitoring sub-region of the three-dimensional data volume model of the mining area fracture.

[0010] Step S03: Extract the structural features of each fault in the mining area within each monitoring sub-region through the data volume model, construct a mineralization favorability assessment model for the mining area within each monitoring sub-region, and prioritize the selection of mineralized fault zones with mineralization potential in the mining area.

[0011] Step S04: Based on the priority order, extract the fracture structure complexity features of each monitoring sub-region through the data volume model, and calculate the fracture structure complexity index of each monitoring sub-region.

[0012] Step S05: Based on the priority order, extract the mineralization information of each monitoring sub-region through the data volume model, and calculate the ore-forming element anomaly index of each ore-bearing fault zone.

[0013] Step S06: By obtaining the fracture tectonic complexity index and ore-forming element anomaly index of each monitoring sub-region, the correlation between fractures and mineralization in each monitoring sub-region is evaluated in order of priority to determine the fracture type.

[0014] Preferably, the establishment of a three-dimensional data volume model of the mining area fracture specifically involves:

[0015] Step S11: Based on the geological database of the mining area, all data are uniformly converted to the same geodetic coordinate system and projection system to eliminate coordinate deviations caused by different sources;

[0016] Step S12: Standardize all data in the geological database to eliminate the influence of dimensions;

[0017] Step S13: According to the exploration accuracy requirements, define the cell size of the three-dimensional grid, establish regular three-dimensional grid cells, and use geostatistical methods to interpolate discrete sampling point data into each three-dimensional grid cell to construct a three-dimensional spatial model of the data.

[0018] Step S14: Extract surface fault traces from geological maps and remote sensing interpretation, extract the dip, dip angle and extension depth information of faults on the profile from magnetic inversion profiles, and obtain the accurate depth, width and orientation of the fault zone from borehole cores.

[0019] Step S15: Extract the surface fault traces as constraints, use the attitude information in the profile and borehole as trend control, generate a smooth fault surface that conforms to geological laws, and construct a three-dimensional spatial model of the fault.

[0020] Step S16: The data three-dimensional spatial model and the fracture three-dimensional spatial model are fused and displayed in a three-dimensional scene to establish a three-dimensional data volume model of the mining area fracture.

[0021] Preferably, the division of the three-dimensional data volume model of the mining area fault into n monitoring sub-regions is specifically as follows:

[0022] Based on the established three-dimensional data volume model of the mining area fracture, the three-dimensional data volume model of the mining area fracture is divided into n monitoring sub-regions according to the method of equal area division, and each monitoring sub-region of the three-dimensional data volume model of the mining area fracture is numbered sequentially as 1,2,...j,...m.

[0023] Preferably, the construction of the mineralization favorability assessment model specifically involves:

[0024] Step S31: The extracted data for each fracture structure feature are: fracture length, fracture depth, fracture width, and fracture dip angle;

[0025] Step S32: Based on the length, depth, width and dip angle of each fracture in the mining area within each monitoring sub-region and the maximum values ​​of the length, depth and width of the fracture within the mining area, construct a mineralization favorability assessment model and calculate the mineralization favorability index of the fractures in the mining area within each monitoring sub-region.

[0026] Step S33: Obtain the mineralization favorability index of the faults in each monitoring sub-region. Arrange the monitoring sub-regions of the three-dimensional data volume model of the faults in the mining area from large to small according to the value of the mineralization favorability index. The larger the mineralization favorability index, the greater the mineralization potential of the faults in the mining sub-region. Then, the highest priority is given to obtain the priority order of the mineralized fault zones with mineralization potential in the mining area.

[0027] Preferably, the fault structure complexity index of each ore-bearing fault zone is specifically:

[0028] Step S41: Extract the fracture structure complexity features of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The fracture structure complexity features include the number of fractures, fracture width, fracture length, and area of ​​the monitoring sub-region.

[0029] Step S42: Based on priority order, calculate the fault structure complexity index of the monitoring sub-region with the greatest mineralization potential first. The fault structure complexity index is obtained by the number of faults, the width of the faults, the length of the faults, and the area of ​​the monitoring sub-region. The larger the index value, the more complex the fault structure in the region.

[0030] Preferably, the anomaly index of ore-forming elements in each ore-bearing fault zone is as follows:

[0031] Step S51: Extract mineralization information of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The mineralization information includes element content, background value corresponding to the element, and standard deviation corresponding to the element content.

[0032] Step S52: Based on priority order, calculate the ore-forming element anomaly index of the monitoring sub-region with the greatest ore-forming potential first. The ore-forming element anomaly index is obtained by the content of any element, the background value of the element, and the standard deviation of the content of the element. The larger the index value, the more significant the degree of anomaly of the ore-forming elements in the region.

[0033] Preferably, the correlation between fractures and mineralization in each monitored sub-region is as follows:

[0034] The fault structure complexity index and ore-forming element anomaly index of each monitoring sub-region were obtained. A correlation evaluation model was used to evaluate the correlation between faults and mineralization in each monitoring sub-region in turn. The greater the correlation, the greater the possibility that the fault in the region is an ore-bearing fault.

[0035] Preferably, the determination of the fracture type specifically includes:

[0036] The correlation between fractures and mineralization in each monitoring sub-region of the three-dimensional data volume model of the fractures in the mining area is obtained. Based on the priority order, the correlation between fractures and mineralization in the first priority monitoring sub-region is judged. If the correlation index between fractures and mineralization in the monitoring sub-region is greater than a preset threshold, the fracture in the monitoring sub-region is judged to be a mineralized fracture; if the correlation index between fractures and mineralization in the monitoring sub-region is less than or equal to the preset threshold, the fracture in the monitoring sub-region is judged to be a non-mineralized fracture.

[0037] The technical effects and advantages of this invention are as follows:

[0038] 1. This invention provides a method for identifying mineralized and non-mineralized faults. Based on a geological database of a mining area, a three-dimensional data volume model of the faults in the mining area is established. This transforms the fault model from a simple geometric surface into an intelligent geological body integrating multi-dimensional information, intuitively reflecting the distribution pattern of the faults in three-dimensional space and their correlation with surrounding geological bodies. The data volume model is divided into multiple monitoring sub-regions. The structural features of each fault in the mining area are extracted from each monitoring sub-region, and a mineralization favorability assessment model for each monitoring sub-region is constructed. Mineralized fault zones with mineralization potential are prioritized for selection, thereby verifying the mineralized faults with the highest priority. A priority system is introduced. The sorting mechanism, through screening followed by detailed evaluation, improves data processing efficiency and accuracy, enhances mineral exploration efficiency, and makes exploration verification more targeted. Based on priority order, the correlation between fractures and mineralization in the first priority monitoring sub-region is judged sequentially. If the correlation index between fractures and mineralization in the monitoring sub-region is greater than a preset threshold, the fracture in the monitoring sub-region is judged to be a mineralized fracture; if the correlation index between fractures and mineralization in the monitoring sub-region is less than or equal to the preset threshold, the fracture in the monitoring sub-region is judged to be a non-mineralized fracture, thereby identifying the fracture type. Through multi-dimensional index quantification and correlation model construction, the identification process is standardized and objective.

[0039] 2. This invention provides a method for identifying mineralized and non-mineralized fractures. Based on priority order, it extracts the fracture structural complexity features and mineralization information of each monitored sub-region through a three-dimensional data volume model of fractures in the mining area. It prioritizes calculating the fracture structural complexity index and ore-forming element anomaly index of the monitored sub-region with the highest mineralization potential. Using a correlation evaluation model, it sequentially assesses the correlation between fractures and mineralization in each monitored sub-region. The greater the correlation, the greater the probability that the fracture in the area is a mineralized fracture. The fracture structural complexity index converts the fracture structural complexity of the sub-region into a quantifiable value. The ore-forming element anomaly index analyzes the element content data to assess the anomaly intensity of ore-forming elements in the sub-region, thereby reflecting the intensity and type of mineralization. Through the priority ranking mechanism, it identifies high-potential mineralized fracture zones and prioritizes the evaluation of mineral resources, reducing resource waste in low-value areas. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a method for identifying mineralized fractures and non-mineralized fractures according to the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 As shown, the present invention provides a method for identifying mineralized fractures and non-mineralized fractures, comprising the following steps:

[0043] Step S01: Based on the geological database of the mining area, establish a three-dimensional data volume model of the faults in the mining area.

[0044] In one possible design, the establishment of a three-dimensional data volume model of the mining area fractures specifically involves:

[0045] Step S11: Based on the geological database of the mining area, all data are uniformly converted to the same geodetic coordinate system and projection system to eliminate coordinate deviations caused by different sources;

[0046] Step S12: Standardize all data in the geological database to eliminate the influence of dimensions;

[0047] Step S13: According to the exploration accuracy requirements, define the cell size of the three-dimensional grid, establish regular three-dimensional grid cells, and use geostatistical methods to interpolate discrete sampling point data into each three-dimensional grid cell to construct a three-dimensional spatial model of the data.

[0048] Step S14: Extract surface fault traces from geological maps and remote sensing interpretation, extract the dip, dip angle and extension depth information of faults on the profile from magnetic inversion profiles, and obtain the accurate depth, width and orientation of the fault zone from borehole cores.

[0049] Step S15: Extract the surface fault traces as constraints, use the attitude information in the profile and borehole as trend control, generate a smooth fault surface that conforms to geological laws, and construct a three-dimensional spatial model of the fault.

[0050] Step S16: The data three-dimensional spatial model and the fracture three-dimensional spatial model are fused and displayed in a three-dimensional scene to establish a three-dimensional data volume model of the mining area fracture.

[0051] Step S02: This step involves dividing the three-dimensional data volume model of the mining area fracture into n monitoring sub-regions and numbering each monitoring sub-region of the three-dimensional data volume model of the mining area fracture.

[0052] In one possible design, the three-dimensional data volume model of the mining area fracture is divided into n monitoring sub-regions, specifically as follows:

[0053] Based on the established three-dimensional data volume model of the mining area fracture, the three-dimensional data volume model of the mining area fracture is divided into n monitoring sub-regions according to the method of equal area division, and each monitoring sub-region of the three-dimensional data volume model of the mining area fracture is numbered sequentially as 1,2,...j,...m.

[0054] Step S03: Extract the structural features of each fault in the mining area within each monitoring sub-region through the data volume model, construct a mineralization favorability assessment model for the mining area within each monitoring sub-region, and prioritize the selection of mineralized fault zones with mineralization potential in the mining area.

[0055] In one possible design, the construction of the mineralization favorability assessment model is specifically as follows:

[0056] Step S31: The extracted data for each fracture structure feature are: fracture length, fracture depth, fracture width, and fracture dip angle;

[0057] Step S32: Based on the length, depth, width and dip angle of each fracture in the mining area within each monitoring sub-region and the maximum values ​​of the length, depth and width of the fracture within the mining area, construct a mineralization favorability assessment model and calculate the mineralization favorability index of the fractures in the mining area within each monitoring sub-region.

[0058] Step S33: Obtain the mineralization favorability index of the faults in each monitoring sub-region. Arrange the monitoring sub-regions of the three-dimensional data volume model of the faults in the mining area from large to small according to the value of the mineralization favorability index. The larger the mineralization favorability index, the greater the mineralization potential of the faults in the mining sub-region. Then, the highest priority is given to obtain the priority order of the mineralized fault zones with mineralization potential in the mining area.

[0059] In this embodiment, it should be specifically noted that the calculation formula for the mineralization favorableness index is as follows:

[0060]

[0061] in, Let represent the mineralization favorability index of the fault in the mining area within the j-th monitoring sub-region. Let represent the fracture length of the i-th fracture in the j-th monitoring sub-region. This represents the maximum value of the fracture length within the mining area of ​​the j-th monitoring sub-region. Let represent the fracture depth of the i-th fracture in the j-th monitoring sub-region. This represents the maximum value of the fault depth within the mining area of ​​the j-th monitoring sub-region. Let represent the fracture width of the i-th fracture in the j-th monitoring sub-region. This represents the maximum value of the fracture width within the mining area of ​​the j-th monitoring sub-region. Let represent the fracture dip angle of the i-th fracture in the j-th monitoring sub-region. , , , The fracture length, fracture depth, fracture width, and fracture dip angle are weighted coefficients, respectively, and are obtained through training with known mineral deposits. n represents the total number of fractures.

[0062] Specifically, a mineralization favorability index is introduced to achieve a consistent evaluation of the mineralization potential of faults. The higher the mineralization favorability index, the greater the mineralization potential of the faults in the mining area. This allows for the verification of the highest priority mineralized faults, improving data processing efficiency and accuracy, and ultimately increasing mineral exploration efficiency.

[0063] Step S04: Based on the priority order, extract the fracture structure complexity features of each monitoring sub-region through the data volume model, and calculate the fracture structure complexity index of each monitoring sub-region.

[0064] In one possible design, the fracture structural complexity index of each ore-bearing fault zone is specifically:

[0065] Step S41: Extract the fracture structure complexity features of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The fracture structure complexity features include the number of fractures, fracture width, fracture length, and area of ​​the monitoring sub-region.

[0066] Step S42: Based on priority order, calculate the fault structure complexity index of the monitoring sub-region with the greatest mineralization potential first. The fault structure complexity index is obtained by the number of faults, the width of the faults, the length of the faults, and the area of ​​the monitoring sub-region. The larger the index value, the more complex the fault structure in the region.

[0067] In this embodiment, it should be specifically noted that the formula for calculating the fracture structure complexity index is as follows:

[0068]

[0069] in, Let be the fracture structure complexity index of the j-th monitoring sub-region. Let represent the total number of fractures in the j-th monitoring sub-region. Represented as the area of ​​the monitored sub-region;

[0070] Specifically, the higher the fault structure complexity index, the more complex the fault structure in the region and the richer the mineral resources.

[0071] Step S05: Based on priority order, extract mineralization information of each monitoring sub-region through the data volume model, and calculate the ore-forming element anomaly index of each ore-bearing fault zone.

[0072] In one possible design, the anomaly indices of ore-forming elements in each ore-bearing fault zone are specifically as follows:

[0073] Step S51: Extract mineralization information of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The mineralization information includes element content, background value corresponding to the element, and standard deviation corresponding to the element content.

[0074] Step S52: Based on priority order, calculate the ore-forming element anomaly index of the monitoring sub-region with the greatest ore-forming potential first. The ore-forming element anomaly index is obtained by the content of any element, the background value of the element, and the standard deviation of the content of the element. The larger the index value, the more significant the degree of anomaly of the ore-forming elements in the region.

[0075] In this embodiment, it should be specifically noted that the calculation formula for the ore-forming element anomaly index is as follows:

[0076]

[0077] in, Let be the anomaly index of ore-forming elements in the j-th monitoring sub-region. Let the content of any element in the j-th monitoring sub-region be denoted as . The background value of this element. The standard deviation of the element's content;

[0078] Specifically, fault structures provide a favorable geological environment for the enrichment of ore-forming elements. By analyzing the element content data and assessing the anomalous intensity of ore-forming elements in each region, the intensity and type of mineralization can be reflected.

[0079] Step S06: By obtaining the fracture structure complexity index and ore-forming element anomaly index of each monitoring sub-region, the correlation between fractures and mineralization in each monitoring sub-region is evaluated in order of priority to determine the fracture type.

[0080] In one possible design, the correlation between fractures and mineralization in each monitored sub-region is specifically as follows:

[0081] The fault structure complexity index and ore-forming element anomaly index of each monitoring sub-region were obtained. A correlation evaluation model was used to evaluate the correlation between faults and mineralization in each monitoring sub-region in turn. The greater the correlation, the greater the possibility that the fault in the region is an ore-bearing fault.

[0082] In this embodiment, it should be specifically noted that the correlation evaluation model is as follows:

[0083]

[0084] in, The correlation between fractures and mineralization in the j-th monitoring sub-region is represented as follows. Let be the fracture structure complexity index of the j-th monitoring sub-region. This represents the maximum value of the fracture structure complexity index across all monitored sub-regions. Let be the anomaly index of ore-forming elements in the j-th monitoring sub-region. This represents the maximum value of the anomaly index of ore-forming elements across all monitored sub-regions. , These represent the weights of the fracture structure complexity index and the ore-forming element anomaly index, respectively. + =1, the specific value can be adjusted according to the mineralization characteristics of the mining area and the actual data;

[0085] Specifically, the higher the correlation index between fracture and mineralization, the higher the correlation between fracture and mineralization in the monitored sub-region, and the greater the likelihood that the fracture is a mineralization fracture.

[0086] In one possible design, the determination of the fracture type specifically refers to:

[0087] The correlation between fractures and mineralization in each monitoring sub-region of the three-dimensional data volume model of the fractures in the mining area is obtained. Based on the priority order, the correlation between fractures and mineralization in the first priority monitoring sub-region is judged. If the correlation index between fractures and mineralization in the monitoring sub-region is greater than a preset threshold, the fracture in the monitoring sub-region is judged to be a mineralized fracture; if the correlation index between fractures and mineralization in the monitoring sub-region is less than or equal to the preset threshold, the fracture in the monitoring sub-region is judged to be a non-mineralized fracture.

[0088] In this embodiment, it should be specifically explained that the present invention establishes a three-dimensional data volume model of the mining area's faults based on the geological database of the mining area. This transforms the fault model from a simple geometric surface into an intelligent geological body integrating multi-dimensional information, intuitively reflecting the distribution pattern of the faults in three-dimensional space and their correlation with surrounding geological bodies. The data volume model is divided into multiple monitoring sub-regions, and the structural features of each fault in the mining area are extracted within each monitoring sub-region. A mineralization favorability assessment model for the mining area within each monitoring sub-region is constructed, prioritizing the selection of ore-bearing fault zones with mineralization potential. The ore-bearing faults with the highest priority are then verified, introducing a priority ranking system. The mechanism improves data processing efficiency and accuracy through screening followed by detailed evaluation, thereby increasing mineral exploration efficiency and making exploration verification more targeted. Based on priority order, the correlation between fractures and mineralization in the first priority monitoring sub-region is judged. If the correlation index between fractures and mineralization in the monitoring sub-region is greater than a preset threshold, the fracture in the monitoring sub-region is judged to be a mineralized fracture; if the correlation index between fractures and mineralization in the monitoring sub-region is less than or equal to the preset threshold, the fracture in the monitoring sub-region is judged to be a non-mineralized fracture, thus identifying the fracture type. Through multi-dimensional index quantification and correlation model construction, the identification process is standardized and objective.

[0089] This invention, based on a priority order, extracts the structural complexity characteristics and mineralization information of each monitored sub-region through a three-dimensional data volume model of the mining area's faults. It prioritizes calculating the structural complexity index and ore-forming element anomaly index of the monitored sub-region with the highest mineralization potential. A correlation evaluation model is used to sequentially assess the correlation between faults and mineralization in each monitored sub-region; a higher correlation indicates a greater likelihood that the fault in that region is a mineralization fault. The structural complexity index transforms the structural complexity of the sub-region's faults into quantifiable values. The ore-forming element anomaly index analyzes elemental content data to assess the anomaly intensity of ore-forming elements in the sub-region, thereby reflecting the intensity and type of mineralization. Through this priority ranking mechanism, high-potential mineral-bearing fault zones are identified, and mineral resource evaluation is completed first, reducing resource waste in low-value areas.

[0090] 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.

[0091] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0092] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying mineralized fractures and non-mineralized fractures, characterized in that, Includes the following steps: Step S01: Based on the geological database of the mining area, establish a three-dimensional data volume model of the faults in the mining area; Step S02: Divide the three-dimensional data volume model of the mining area fault into n monitoring sub-regions, and number each monitoring sub-region of the three-dimensional data volume model of the mining area fault; Step S03: Extract the structural features of each fault in the mining area within each monitoring sub-region through the data volume model, construct a mineralization favorability assessment model for the mining area within each monitoring sub-region, and prioritize the selection of ore-bearing fault zones with mineralization potential in the mining area; Step S04: Based on the priority order, extract the fracture structure complexity features of each monitoring sub-region through the data volume model, and calculate the fracture structure complexity index of each monitoring sub-region; Step S05: Based on the priority order, extract the mineralization information of each monitoring sub-region through the data volume model, and calculate the ore-forming element anomaly index of each ore-bearing fault zone; Step S06: By obtaining the fracture tectonic complexity index and ore-forming element anomaly index of each monitoring sub-region, the correlation between fractures and mineralization in each monitoring sub-region is evaluated in order of priority to determine the fracture type.

2. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The specific steps for establishing a three-dimensional data volume model of the fracture in the mining area are as follows: Step S11: Based on the geological database of the mining area, all data are uniformly converted to the same geodetic coordinate system and projection system to eliminate coordinate deviations caused by different sources; Step S12: Standardize all data in the geological database to eliminate the influence of dimensions; Step S13: According to the exploration accuracy requirements, define the cell size of the three-dimensional grid, establish regular three-dimensional grid cells, and use geostatistical methods to interpolate discrete sampling point data into each three-dimensional grid cell to construct a three-dimensional spatial model of the data. Step S14: Extract surface fault traces from geological maps and remote sensing interpretation, extract the dip, dip angle and extension depth information of faults on the profile from magnetic inversion profiles, and obtain the accurate depth, width and orientation of the fault zone from borehole cores. Step S15: Extract the surface fault traces as constraints, use the attitude information in the profile and borehole as trend control, generate a smooth fault surface that conforms to geological laws, and construct a three-dimensional spatial model of the fault. Step S16: The data three-dimensional spatial model and the fracture three-dimensional spatial model are fused and displayed in a three-dimensional scene to establish a three-dimensional data volume model of the mining area fracture.

3. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The three-dimensional data volume model of the mining area faults is divided into n monitoring sub-regions, specifically: Based on the established three-dimensional data volume model of the mining area fracture, the three-dimensional data volume model of the mining area fracture is divided into n monitoring sub-regions according to the method of equal area division, and each monitoring sub-region of the three-dimensional data volume model of the mining area fracture is numbered sequentially as 1,2,...j,...m.

4. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The specific construction of the mineralization favorability assessment model is as follows: Step S31: The extracted data for each fracture structure feature are: fracture length, fracture depth, fracture width, and fracture dip angle; Step S32: Based on the length, depth, width and dip angle of each fracture in the mining area within each monitoring sub-region and the maximum values ​​of the length, depth and width of the fracture within the mining area, construct a mineralization favorability assessment model and calculate the mineralization favorability index of the fractures in the mining area within each monitoring sub-region. Step S33: Obtain the mineralization favorability index of the faults in each monitoring sub-region. Arrange the monitoring sub-regions of the three-dimensional data volume model of the faults in the mining area from large to small according to the value of the mineralization favorability index. The larger the mineralization favorability index, the greater the mineralization potential of the faults in the mining sub-region. Then, the highest priority is given to obtain the priority order of the mineralized fault zones with mineralization potential in the mining area.

5. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The specific fault structure complexity index of each ore-bearing fault zone is as follows: Step S41: Extract the fracture structure complexity features of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The fracture structure complexity features include the number of fractures, fracture width, fracture length, and area of ​​the monitoring sub-region. Step S42: Based on priority order, calculate the fault structure complexity index of the monitoring sub-region with the greatest mineralization potential first. The fault structure complexity index is obtained by the number of faults, the width of the faults, the length of the faults, and the area of ​​the monitoring sub-region. The larger the index value, the more complex the fault structure in the region.

6. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The specific anomaly indices of ore-forming elements in each ore-bearing fault zone are as follows: Step S51: Extract mineralization information of each monitoring sub-region through the three-dimensional data volume model of the fracture in the mining area. The mineralization information includes element content, background value corresponding to the element, and standard deviation corresponding to the element content. Step S52: Based on priority order, calculate the ore-forming element anomaly index of the monitoring sub-region with the greatest ore-forming potential first. The ore-forming element anomaly index is obtained by the content of any element, the background value of the element, and the standard deviation of the content of the element. The larger the index value, the more significant the degree of anomaly of the ore-forming elements in the region.

7. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The specific correlations between fractures and mineralization in each monitored sub-region are as follows: The fault structure complexity index and ore-forming element anomaly index of each monitoring sub-region were obtained. A correlation evaluation model was used to evaluate the correlation between faults and mineralization in each monitoring sub-region in turn. The greater the correlation, the greater the possibility that the fault in the region is an ore-bearing fault.

8. The method for identifying mineralized and non-mineralized fractures according to claim 1, characterized in that: The determination of the fracture type specifically refers to: The correlation between fractures and mineralization in each monitoring sub-region of the three-dimensional data volume model of the fractures in the mining area is obtained. Based on the priority order, the correlation between fractures and mineralization in the first priority monitoring sub-region is judged. If the correlation index between fractures and mineralization in the monitoring sub-region is greater than a preset threshold, the fracture in the monitoring sub-region is judged to be a mineralized fracture; if the correlation index between fractures and mineralization in the monitoring sub-region is less than or equal to the preset threshold, the fracture in the monitoring sub-region is judged to be a non-mineralized fracture.

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