A Method and System for Delineating Lead-Zinc Ore Target Areas Based on Alteration Mineral Mapping Results

Abstract

This invention provides a method and system for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results. It relates to the field of computer technology. First, it acquires regional alteration mineral mapping results and information on regional ore-controlling conditions. Then, using the lateral distribution span, vertical burial depth, and mineral assemblage in the regional alteration mineral mapping results, combined with regional geological evolution history data, it constructs an alteration mineral zonation sequence. Next, based on the spatial distribution morphology of mineralization structures, the physical and mechanical properties of stratigraphy, and the heat transfer path of magmatic activity in the regional ore-controlling conditions information, it quantifies the synergistic response intensity of various ore-controlling conditions to the formation of alteration minerals. Combining the above results, it locks the spatial location and extension range of lead-zinc ore mineralization windows. Finally, based on the ore-forming windows and element enrichment states, it delineates the spatial range of the target area and optimizes the boundaries, generating lead-zinc ore target area delineation results. This method can effectively guide lead-zinc ore exploration and development, improving resource discovery rate and development efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results. Background Technology

[0002] In lead-zinc mine exploration and development, accurately delineating target areas is crucial for improving exploration efficiency, reducing development costs, and achieving efficient resource utilization. Traditional lead-zinc mine target area delineation methods primarily rely on single geological information or limited geological indicators. For example, some methods rely solely on the spatial distribution of ore-forming structures. While the morphology of ore-forming structures can indicate a certain mineralization probability, using them alone fails to comprehensively consider other factors influencing lead-zinc mine formation, leading to significant deviations in prediction results. Other methods focus on stratigraphic lithology analysis, inferring the mineralization environment by studying the physical and mechanical properties of stratigraphic lithology. However, stratigraphic lithology is only one aspect of mineralization, neglecting important information such as alteration minerals and magmatic activity, making accurate delineation of lead-zinc mine target areas difficult. In addition, previous studies on alteration minerals have mostly focused on describing their simple distribution without in-depth analysis of the zoning evolution of alteration minerals and their synergistic response with ore-controlling conditions. This has prevented the full utilization of the rich information contained in the alteration mineral mapping results, resulting in low accuracy and reliability of lead-zinc ore target area delineation, which is difficult to meet the needs of modern lead-zinc ore exploration and development. Summary of the Invention

[0003] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results, the method comprising:

[0004] The results of regional alteration mineral mapping and regional ore-controlling conditions are obtained. The results of regional alteration mineral mapping include the lateral distribution span, vertical burial depth, mineral assemblage composition and element enrichment state of various alteration minerals in the region. The regional ore-controlling conditions include the spatial distribution morphology of metallogenic structures, the physical and mechanical properties of stratigraphic lithology, the amount of material ejected from magmatic activity and the heat conduction path.

[0005] By combining the lateral distribution span, vertical burial depth and mineral assemblage in the regional alteration mineral mapping results with regional geological evolution history data, an alteration mineral zoning evolution sequence is constructed. This alteration mineral zoning evolution sequence includes the evolution process, spatial characteristics and stage correlation data of alteration mineral zoning in different periods.

[0006] Based on the spatial distribution of metallogenic structures, the physical and mechanical properties of stratigraphy and magmatic activity heat conduction paths in the information of regional ore-controlling conditions, response coefficients are calculated. Weighted summation of each response coefficient is performed to generate the synergistic response intensity of various ore-controlling conditions on the formation of altered minerals, and the synergistic response intensity of various ore-controlling conditions on the formation of altered minerals is quantified.

[0007] By combining the alteration mineral zoning evolution sequence and the co-response intensity, the spatial location and extension range of the lead-zinc ore mineralization window can be determined.

[0008] Based on the spatial location, extension range, and element enrichment status of the regional alteration mineral mapping results of the mineralization window, the spatial range of the target area is delineated and the boundary is optimized to generate the lead-zinc ore target area delineation results. The lead-zinc ore target area delineation results include the three-dimensional spatial boundary of the target area, the corresponding alteration mineral zoning characteristics, and the synergistic response parameters of the ore-controlling conditions.

[0009] Furthermore, embodiments of the present invention also provide a lead-zinc ore target area delineation system based on the analysis of alteration mineral mapping results, comprising:

[0010] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results by executing the machine-executable instructions.

[0011] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results.

[0012] Based on the above, by acquiring regional alteration mineral mapping results and regional ore-controlling condition information, multi-dimensional data closely related to lead-zinc mineralization were comprehensively integrated. Utilizing the lateral distribution span, vertical burial depth, and mineral assemblage composition from the regional alteration mineral mapping results, and combining this with regional geological evolution history data, an alteration mineral zonation evolution sequence was constructed. This revealed the temporal and spatial evolutionary patterns of alteration minerals, helping to grasp the dynamic changes in the mineralization process. Based on regional ore-controlling condition information, the synergistic response intensity of various ore-controlling conditions to alteration mineral formation was quantified, fully considering the interactions between multiple ore-controlling factors. It more accurately reflects the complexity of the mineralization environment, and combines the zoning evolution sequence of alteration minerals and the synergistic response intensity to lock the spatial location and extension range of lead-zinc mineralization windows, realizing the precise positioning of favorable mineralization areas. Finally, based on the mineralization window and element enrichment status, the spatial range of the target area is delineated and the boundary is optimized. The generated lead-zinc mineral target area delineation results include the three-dimensional spatial boundary of the target area, the corresponding alteration mineral zoning characteristics and synergistic response parameters of ore-controlling conditions, which has higher accuracy and can effectively guide the exploration and development of lead-zinc minerals, improve the resource discovery rate and development efficiency, and reduce exploration risks and costs. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the execution flow of the lead-zinc ore target area delineation method based on the analysis of alteration mineral mapping results provided in this embodiment of the invention.

[0014] Figure 2 This is a schematic diagram of exemplary hardware and software components of a lead-zinc ore target area delineation system based on alteration mineral mapping results analysis provided in an embodiment of the present invention. Detailed Implementation

[0015] Figure 1 This is a flowchart illustrating a method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results, provided in one embodiment of the present invention. A detailed description follows.

[0016] Step S110: Obtain regional alteration mineral mapping results and regional ore-controlling condition information. The regional alteration mineral mapping results include the lateral distribution span, vertical burial depth, mineral assemblage composition and element enrichment state of various alteration minerals in the region. The regional ore-controlling condition information includes the spatial distribution morphology of ore-forming structures, the physical and mechanical properties of stratigraphic lithology, the amount of material ejected from magmatic activity and the heat conduction path.

[0017] In this embodiment, before delineating the lead-zinc ore target area, basic geological data of the study area must be collected. The regional alteration mineral mapping results are obtained through remote sensing technology, ground geological surveys, and the integration of borehole data. Lateral distribution span is determined by interpreting remote sensing images and measuring the maximum horizontal projection distance of the same alteration mineral at different contour levels using a geographic information system. The data format is a polygonal vector file containing multiple coordinate points, each composed of latitude, longitude, and altitude information. Vertical burial depth data is obtained from borehole core logging, recording the location of alteration minerals in cores at different depths. The data is presented in a two-dimensional table with depth values ​​and corresponding mineral types, with depth values ​​increasing downwards from the borehole head. Mineral assemblage composition is obtained through rock thin section identification, using electron probe microanalysis to generate a text report containing mineral names, percentage content, and crystal structure parameters. Elemental enrichment status is obtained through soil geochemical measurements and rock chemical analysis. The data format is a dataset containing sampling point coordinates and elemental contents such as lead and zinc; all elemental contents have been converted to standardized Clarke values. Obtaining regional ore-controlling conditions involves multi-source data fusion. The spatial distribution of mineralized structures was constructed using structural geological mapping and seismic exploration data. A set of spatial coordinates for structural surfaces was generated using 3D modeling software, including attribute tables containing parameters such as strike, dip, and dip angle. The physical and mechanical properties of the stratigraphic lithology were obtained from laboratory rock mechanics tests. Compressive and tensile strength tests, as well as porosity and permeability measurements, were performed on collected rock samples. The results are expressed as the average test value and standard deviation for each lithological unit. The amount of material ejected from magmatic activity was calculated by combining volcanic rock strata thickness measurements with density calculations. The heat conduction path was obtained based on the spatial distribution of magmatic intrusions and geothermal gradient data, using heat flow simulation software to invert and obtain vector data of heat conduction direction and rate. During data collection, geologically sensitive data underwent anonymization processing, including encrypted offsetting of borehole coordinates and anonymization of raw test data, ensuring that data use complies with geological data management standards.

[0018] Step S120: Construct an alteration mineral zoning evolution sequence by combining the lateral distribution span, vertical burial depth and mineral assemblage composition in the regional alteration mineral mapping results with regional geological evolution history data.

[0019] In this embodiment, after obtaining the regional alteration mineral mapping results and regional geological evolution history data, the alteration mineral zonation evolution sequence is constructed. The regional geological evolution history data includes reports on the tectonic movement periods of the study area, stratigraphic sedimentary sequence columnar sections, isotopic dating data of magmatic intrusion events, and paleogeographic environment reconstruction maps retrieved from the geological survey database. The above data are organized into a structured geological event sequence table with time as the axis. Each event includes attributes such as occurrence time, duration, scope of influence, and main geological manifestations.

[0020] Step S121: Use geological profile analysis tools to extract the lateral distribution span data of each type of alteration mineral in the regional alteration mineral mapping results. This lateral distribution span data is obtained by measuring the maximum horizontal distance of mineral distribution at the same burial depth. During the measurement process, the regional topographic and geomorphological maps are used for correction.

[0021] In this embodiment, a geological profile analysis tool is used to process the regional alteration mineral mapping results. First, the regional digital elevation model and alteration mineral distribution vector map are loaded into the geographic information system, and a series of profile lines parallel to the main structural trends of the region are generated at set intervals. On each profile, multiple burial depth layers are selected, and on each layer, the distribution range of the same alteration mineral is determined using a polygon boundary tracing tool. The maximum horizontal distance along the profile line direction within this range is measured. Simultaneously, based on topographic maps, slope correction is applied to the measurement results in sloping areas, with the actual horizontal distance equal to the measured distance multiplied by the slope cosine. The average of the measurement results at the same depth layer across all profiles is taken as the lateral distribution span data of the alteration mineral at that burial depth. The data format is a three-dimensional array containing mineral type, burial depth, and lateral span value.

[0022] Step S122: Extract the vertical burial depth data of each type of altered mineral by interpreting the borehole core data. The vertical burial depth data is obtained by calculating the vertical distance between the mineral occurrence layer and the surface reference surface, and the influence value of topographic relief is deducted during the calculation.

[0023] In this embodiment, borehole data penetrating altered mineral zones are selected from the regional borehole database. Each borehole data set includes borehole coordinates, borehole inclination data, and a core logging log. First, the elevation of each borehole opening is calculated based on the borehole coordinates and a digital elevation model (DEM), serving as the surface reference level. Then, the top and bottom boundaries of altered mineral occurrences are identified in the core logging log; these depths are vertical distances measured downwards from the borehole opening. Based on the borehole inclination data, the depth of the inclined section is converted to vertical depth by multiplying the inclination depth by the cosine of the borehole inclination angle. Finally, the elevation of the altered mineral occurrence strata is obtained by subtracting the vertical depth of the top and bottom boundaries of the altered minerals from the borehole opening elevation. This elevation is then compared with the regional reference elevation to calculate the longitudinal burial depth data. For areas with significant topographic relief, a topographic correction coefficient for the borehole location is generated through interpolation. The original burial depth data is multiplied by this correction coefficient to eliminate topographic influence, forming the final longitudinal burial depth dataset. The data structure is a table containing borehole number, mineral type, top boundary depth, and bottom boundary depth.

[0024] Step S123: Extract the mineral assemblage composition data for each type of altered mineral using a mineral assemblage microscopic analysis system. This mineral assemblage composition data includes the types of symbiotic minerals, the ratio of the number of main minerals to accessory minerals, and the spatial arrangement of mineral particles.

[0025] In this embodiment, representative altered rock samples were selected for thin section preparation, with multiple parallel thin sections prepared for each sample. The thin sections were placed under a mineral assemblage microscopy system, which consists of a polarizing microscope, an image acquisition device, and mineral identification software. The software automatically identified the mineral types in the thin sections and recorded the names and frequencies of associated minerals. For the ratio of main minerals to accessory minerals, image segmentation technology was used to segment the mineral image into different mineral phases based on grayscale values, and the area percentage of each mineral phase was calculated as an approximation of the ratio. The spatial arrangement of mineral particles was determined by calculating the distribution frequency along the long axis of the particles. The long axis of the particles was divided into multiple directional intervals, and the proportion of particles in each interval was statistically analyzed to form an arrangement distribution matrix. All data were integrated into a mineral assemblage dataset containing sample number, mineral assemblage type, percentage content of each mineral, and the arrangement direction distribution matrix. The analysis results for each sample were obtained through multiple parallel measurements, and the average value was taken.

[0026] Step S124: Collect regional geological evolution history data, which includes the tectonic movement periods, stratigraphic sedimentary sequences, magmatic intrusion times, and paleogeographic environmental changes within the region.

[0027] In this embodiment, large-scale geological maps and corresponding geological reports of the study area were retrieved from the database of the Regional Geological Survey Institute. Information on tectonic movements was extracted, including the name, time of occurrence, nature of movement, and tectonic features of each movement. Stratigraphic sedimentary sequences were obtained through a comprehensive columnar section, recording the lithological assemblage, thickness, and contact relationships of strata at each age. Data on magmatic intrusion ages were derived from zircon isotopic dating results of regional granites, compiled into a table corresponding to intrusive body name, isotopic age, and rock type. Paleogeographic environmental change data were obtained by referencing regional paleogeographic research literature, extracting descriptions of paleotopography, paleoclimate, and sedimentary environments from different geological periods. These data were arranged chronologically to form a structured regional geological evolution timeline, with each time point containing corresponding geological events and environmental parameters.

[0028] Step S125: Based on the tectonic movement periods in the regional geological evolution history data, divide the time stages of alteration mineral formation, with each time stage corresponding to a specific tectonic movement intensity and influence range.

[0029] In this embodiment, the tectonic movement periods in the regional geological evolution history data are used as the main basis for time division. First, the start and end times of each tectonic movement period are determined. Based on the correlation of geological events, continuous tectonic activity is divided into different tectonic cycles. Each tectonic cycle is considered a large time unit, and then further subdivided into several time stages based on magmatic activity events and sedimentary discontinuities. The duration of each time stage is determined based on isotopic dating data and stratigraphic thickness estimation. A specific tectonic movement intensity parameter is assigned to each time stage. This parameter is determined based on the scale and density of tectonic features and is divided into three levels: strong, moderate, and weak. Simultaneously, the influence range of each stage's tectonic movement is recorded as a polygonal area represented by latitude and longitude coordinates. A time stage division table is formed, including time stage number, start time, end time, tectonic intensity, and influence range, serving as the time framework for subsequent mineral data classification.

[0030] Step S126: Classify and collect the lateral distribution span data, vertical burial depth data, and mineral assemblage composition data of each type of alteration mineral according to the corresponding formation time stage to form a staged mineral feature dataset.

[0031] In this embodiment, the formation time stage of each type of alteration mineral is determined based on the isotopic age data of the alteration minerals or their stratigraphic relationship with the ore-bearing strata. For alteration minerals lacking direct dating data, indirect inference is made through the correlation between the associated mineral assemblages and igneous rocks or strata of known ages. Each record in the lateral distribution span data, vertical burial depth data, and mineral assemblage composition data is assigned to the time stage divided in step S125 according to its corresponding mineral formation time stage. During the classification and collection process, a data association table is established to record the source, time stage number, and data reliability level of each data record. For alteration minerals spanning multiple time stages, they are proportionally assigned to the corresponding time stages based on their development degree in different stages. The final staged mineral feature dataset contains lateral, vertical, and assemblage feature data of all alteration minerals in each time stage. The data format is a data table in a relational database, and they are associated through time stage numbers.

[0032] Step S127: For each formation time stage, analyze the spatial overlap relationship of the lateral distribution span data and vertical burial depth data of various alteration minerals in the stage mineral characteristic dataset, deduce the distribution overlap area data and center spacing data of different minerals in the same time stage, and form a spatial distribution association dataset.

[0033] In this embodiment, spatial relationship analysis is performed on the phased mineral feature datasets for each formation time stage. First, the lateral distribution span and vertical burial depth data of all alteration minerals in that stage are converted into the boundaries of a cuboid in three-dimensional space. The base of the cuboid is determined by the maximum and minimum coordinates of the lateral distribution span, and the height is determined by the top and bottom boundaries of the vertical burial depth.

[0034] Step S1271: Extract the horizontal distribution span data and vertical burial depth data of various alteration minerals from the phased mineral feature dataset of each formation time stage.

[0035] In this embodiment, all records for the current processing time stage are filtered from the phased mineral feature dataset and grouped by mineral type. For each group of minerals, the maximum horizontal distance value and corresponding coordinate range in the lateral distribution span data, and the top and bottom boundary depth values ​​in the vertical burial depth data are extracted. The above data are organized into a mineral spatial parameter table containing mineral type, lateral coordinate range, and vertical depth range, which serves as the basic data for subsequent spatial geometry construction.

[0036] Step S1272: Based on the horizontal distribution span data and the vertical burial depth data, construct a three-dimensional spatial coordinate system for this time period. The first horizontal axis and the second horizontal axis of the coordinate system correspond to the horizontal plane coordinates, and the vertical axis corresponds to the vertical burial depth.

[0037] In this embodiment, a Gauss-Kruger projection is used to establish a planar coordinate system. The first horizontal axis represents the east direction, and the second horizontal axis represents the north direction, both in meters. The vertical axis represents the vertical depth direction, with the surface as the reference and downward as the positive direction, also in meters. The horizontal coordinate ranges of all minerals are converted to coordinate values ​​in this planar coordinate system, and the vertical depth range is directly used as the vertical axis coordinate. The origin of the coordinate system is set at the southwest corner of the study area to ensure that the coordinate values ​​of all minerals are positive, facilitating subsequent calculations and visualization.

[0038] Step S1273: Convert the distribution range of various alteration minerals into a spatial geometry in the three-dimensional spatial coordinate system. The boundary of the geometry is determined by the maximum and minimum values ​​of the lateral distribution and the maximum and minimum values ​​of the longitudinal burial depth.

[0039] In this embodiment, for each alteration mineral, an axis-aligned cuboid geometry is constructed in a three-dimensional coordinate system based on its lateral coordinate range and longitudinal depth range. The six faces of the cuboid are determined by the minimum and maximum values ​​of the first lateral axis, the second lateral axis, and the longitudinal axis, respectively. The geometric parameters of all minerals are stored as a geometry description file containing mineral identifiers and a list of vertex coordinates.

[0040] Step S1274: Using a spatial geometry intersection calculation tool, calculate the intersection of the spatial geometries corresponding to any two alteration minerals, record the three-dimensional spatial coordinate range data, volume data, and center position data of the intersection, and form a dataset of overlapping distribution areas.

[0041] In this embodiment, the geometric intersection calculation module of the 3D spatial analysis software is used to perform intersection calculations on the cuboid geometries of any two alteration minerals within the same time period. For each pair of minerals, their intersection geometry is calculated. This intersection geometry is also a cuboid, and its boundary is determined by the overlapping portion of the corresponding coordinate axis ranges of the two mineral geometries. If the ranges on a certain coordinate axis do not overlap, the intersection volume is zero, indicating that the two minerals do not have spatial overlap at that time period. The volume of the intersection geometry is calculated as follows: the volume equals the length of the first horizontal axis range multiplied by the length of the second horizontal axis range multiplied by the length of the vertical axis range. The center position data of the intersection is obtained by calculating the midpoint of each coordinate axis range of the intersection geometry; that is, each component of the center coordinate is the average of the maximum and minimum values ​​of the corresponding coordinate axis range. All calculation results are compiled into a dataset containing mineral pair identifiers, intersection coordinate ranges, volumes, and center coordinates of the overlapping region distribution.

[0042] Step S1275: Calculate the straight-line distance between the centers of the spatial geometry corresponding to any two altered minerals, record the straight-line distance data between the centers of each pair of altered minerals, and form a mineral center spacing dataset.

[0043] In this embodiment, the center coordinates of each altered mineral geometry are first calculated using the same method as the intersection center location calculation, i.e., the midpoint of each coordinate axis range. Then, for any two minerals, the straight-line distance in three-dimensional space is calculated based on their center coordinates. The calculation method is that the distance is equal to the square root of the sum of the squares of the differences in the first horizontal axis coordinates, the squares of the differences in the second horizontal axis coordinates, and the squares of the differences in the vertical axis coordinates. The calculated distance values ​​are stored together with the corresponding mineral pair identifiers to form a mineral center spacing dataset. For the same mineral pair, if it exists in multiple time stages, the distance data for different stages are recorded separately.

[0044] Step S1276: Analyze the proportional relationship between the volume data in the overlapping region dataset and the total volume of each mineral's spatial geometry, and analyze the proportional relationship between the corresponding distance data in the mineral center spacing dataset and the preset reference distance, and comprehensively deduce the spatial correlation tightness data.

[0045] In this embodiment, for each pair of minerals, the ratio of the intersection volume to the smaller of the volumes of the two mineral geometries is calculated to obtain the volume overlap ratio. Simultaneously, the distance between the center-to-center distance of the minerals is compared to a preset reference distance to obtain the distance ratio. The preset reference distance is set based on the average distribution range of minerals within the region. The spatial correlation tightness data is obtained by combining the volume overlap ratio and the distance ratio. The calculation method is that the spatial correlation tightness equals the volume overlap ratio multiplied by (one minus the distance ratio). The larger this value, the tighter the spatial correlation between the two minerals, ranging from zero to one. The calculation results are added to the spatial distribution correlation dataset as an important indicator for measuring the spatial relationship of minerals.

[0046] Step S1277: For combinations of three or more altered minerals, summarize the data of all pairs of overlapping distribution areas and the data of the straight-line distance between the centers, calculate the average correlation density data of the minerals in the combination, and form a combination aggregation state dataset.

[0047] In this embodiment, mineral assemblages composed of three or more alteration minerals are identified through cluster analysis. First, based on spatial correlation data, a hierarchical clustering algorithm is used to divide the minerals into different assemblages, with the clustering threshold set according to the geological characteristics of the study area. For each identified mineral assemblage, data on the overlapping areas and the straight-line distance between the centers of all pairs of minerals within the assemblage are collected. The average spatial correlation data within the assemblage is calculated as the average correlation strength of that assemblage. Simultaneously, the total volume and total overlapping volume of the mineral geometry within the assemblage are calculated to obtain the assemblage clustering coefficient. The above data is then compiled into a assemblage clustering state dataset containing assemblage identifiers, a list of mineral compositions, average correlation strength, and clustering coefficients, for subsequent zonal structure division.

[0048] Step S1278: Integrate the datasets of overlapping distribution areas, the datasets of mineral center spacing, the data on spatial correlation tightness, and the datasets of combined aggregation states to form a spatial distribution correlation dataset within the same time period.

[0049] In this embodiment, the datasets obtained in the above steps—distribution overlap area dataset, mineral center spacing dataset, spatial correlation density data, and assemblage aggregation state dataset—are linked and integrated. The datasets are connected using mineral identifiers and time stage numbers to form a comprehensive dataset containing mineral pair relationships, assemblage relationships, and spatial parameters. This comprehensive dataset is stored using a relational database table structure, containing a main table and multiple sub-tables. The main table records basic information about the time stage and mineral assemblages, while the sub-tables store detailed data such as overlap areas, center spacing, and correlation density. Database queries can quickly retrieve spatial distribution correlation information for any mineral or mineral assemblage at a specific time stage.

[0050] Step S128: Based on the spatial distribution association dataset, divide the main distribution zone, transitional distribution zone and peripheral distribution zone of alteration minerals in each time stage, determine the spatial boundary data of each zone, and form the alteration mineral zoning structure.

[0051] In this embodiment, the dominant mineral assemblages in each time period are identified using the combined clustering state dataset in the spatial distribution association dataset. A dominant mineral assemblage is defined as a combination with an average correlation strength greater than a set threshold and a high clustering coefficient ranking. Based on the spatial distribution range of the dominant mineral assemblages, the core region of the main distribution zone is determined. Through buffer analysis, the initial boundary of the main distribution zone is formed by extending outward from the core region by a certain distance; the extension distance is determined based on the average lateral distribution span of the minerals within the assemblage. The transitional distribution zone is the range extending outward from the boundary of the main distribution zone by a set multiple of the core region radius; the correlation strength of mineral assemblages within this range is within a set interval. The outer distribution zone is the area outside the transitional zone where the correlation strength of mineral assemblages is less than a set value. The spatial boundaries of each zone are determined using a polygon boundary extraction algorithm, and the boundary coordinates are stored as vector data, containing attributes such as zone type, boundary point coordinate sequence, and area, forming the alteration mineral zoning structure for that time period.

[0052] Step S129: Connect the alteration mineral zoning structures of each stage in chronological order of formation time, compare the boundary overlap data and mineral assemblage data of the zoning structures of the previous and subsequent stages, deduce the inherited area data and the modified area data, and form zoning evolution correlation data.

[0053] In this embodiment, the alteration mineral zoning structures of all time stages are arranged chronologically to form a zoning evolution sequence. For two adjacent time stages, a comparative analysis of the zoning structures is performed to identify the inheritance and modification relationships of the zoning.

[0054] Step S1291: Sort the alteration mineral zoning structures of each stage according to the chronological order of their formation time to form a time sequence of zoning structures.

[0055] In this embodiment, alteration mineral zoning structure data for each time stage is extracted from the phased mineral feature dataset and arranged in ascending order of time stage number to form a zoning structure time series. Each zoning structure includes the spatial boundaries of the main distribution zone, transitional distribution zone, and peripheral distribution zone, as well as the mineral assemblage composition data for that time stage. The above data is stored in a structured file in chronological order, with each time stage as an independent record containing information such as time identifier, zoning boundary data, and a list of mineral assemblages.

[0056] Step S1292: Extract the spatial boundary coordinates and mineral assemblage data of the main distribution zone, transitional distribution zone and peripheral distribution zone of the zonal structure in the previous stage of the time series.

[0057] In this embodiment, spatial boundary coordinate data of each distribution zone is extracted from the zonal structure data of the previous stage. This data is stored in the form of a sequence of polygon vertex coordinates. Simultaneously, mineral assemblage data within each distribution zone is extracted, including the main mineral types, their proportions, and their symbiotic relationships. This data is then organized into a previous-stage zonal characteristic table containing zone type, boundary coordinate list, and mineral assemblage details, serving as benchmark data for comparative analysis.

[0058] Step S1293: Extract the spatial boundary coordinates and mineral assemblage data of the main distribution zone, transitional distribution zone and peripheral distribution zone of the zonal structure in the later stage of the zonal structure time series.

[0059] In this embodiment, the same method as in step S1292 is used to extract the spatial boundary coordinates and mineral assemblage data of each distribution zone from the zonal structure data of the later stage, forming a zonal feature table for the later stage. This ensures that the data format is consistent between the earlier and later stages to facilitate spatial overlay and attribute comparison.

[0060] Step S1294: Use a spatial overlay comparison tool to overlay the boundary coordinate data of the previous stage and the next stage of the zonal structure, calculate the percentage of overlapping area, and determine the spatial coordinate data of the overlapping area.

[0061] In this embodiment, the spatial overlay analysis function of the geographic information system software is used to overlay the boundary data of the zonal structure of the previous and subsequent stages. For the main distribution zone, the area of ​​the overlapping region between the boundary of the subsequent stage and the boundary of the previous stage is calculated, and then divided by the total area of ​​the main distribution zone in the previous stage to obtain the percentage of overlapping area. If the percentage of overlapping area is greater than a set threshold, a significant spatial inheritance relationship is considered to exist. The spatial coordinate data of the overlapping area is extracted to form a polygon of the overlapping area, and its area, perimeter, and center coordinates are recorded.

[0062] Step S1295: Compare the mineral assemblage composition data of the zonal structure in the previous stage and the next stage, screen out areas with consistent mineral types and quantities, and determine the mineral assemblage inheritance area data.

[0063] In this embodiment, the mineral assemblage data of each distribution zone in the previous and subsequent zonal structures are compared. For each spatial location, mineral assemblage data of the previous and subsequent stages are obtained through spatial interpolation. The mineral types are compared to see if they are the same, and whether the difference in the content ratio of the main minerals is within a preset threshold. The regions that meet the conditions are determined as mineral assemblage inheritance regions, and their spatial coordinate range and mineral assemblage characteristics are recorded.

[0064] Step S1296: Merge the spatial coordinate data of the overlapping area with the mineral assemblage inherited area data to form zone inherited area data, and record the area data and spatial location data of the zone inherited area data.

[0065] In this embodiment, spatial intersection operations are performed on the spatial coordinate data of the overlapping region and the mineral assemblage inheritance region data to obtain regions that both spatially overlap and maintain mineral assemblage inheritance, i.e., zone inheritance regions. The area data of this region is calculated, its spatial location data is determined by the coordinates of the center point of the polygon, and the main mineral assemblage types and content ratios within this region are recorded to form a zone inheritance region data table.

[0066] Step S1297: Identify the regions in the zonation structure of the next stage that do not overlap with the previous stage, analyze the boundary change direction and magnitude data of the region, and compare the differences in mineral assemblage composition data.

[0067] In this embodiment, spatial difference operations are used to extract regions in the subsequent zoning structure that do not overlap with the previous stage. For these regions, the direction of change of their boundaries relative to the boundaries of the previous stage zoning is calculated, such as expansion, contraction, or migration. This direction is represented by the movement vector of the boundary centroid. The magnitude of the change is calculated by the average distance of the boundary movement, where the average distance equals the arithmetic mean of the movement distances of all boundary points. Simultaneously, the mineral assemblage data of this region is compared with the corresponding positions in the previous stage. Newly added mineral types, disappeared mineral types, and mineral types whose content ratio changes exceed a threshold are statistically analyzed to form mineral assemblage difference data.

[0068] Step S1298: Combining the tectonic movement and magmatic activity event data in the regional geological evolution history data, deduce the geological dynamic factors that lead to boundary changes and differences in mineral assemblages.

[0069] In this embodiment, historical data on regional geological evolution are consulted to identify the major geological events occurring in later stages relative to earlier stages, such as the nature of tectonic movements, the location and scale of magmatic intrusions, and changes in sedimentary environments. The correlation between these geological events and the direction, magnitude, and mineral assemblage differences of zonal boundary changes is analyzed. The derived geodynamic factor data are correlated with boundary change and mineral assemblage difference data to form a geodynamic interpretation table.

[0070] Step S1299: Based on the geological dynamic factor data, classify the transformation types, determine the regional scope data and transformation degree data of each transformation type, and form zonal transformation area data.

[0071] In this embodiment, based on the type of geological dynamic factors, the zonal alteration areas are divided into different alteration types, such as hydrothermal alteration, tectonic fracturing alteration, and sedimentary superposition alteration. For each alteration type, its regional extent data, i.e., the spatial coordinate boundary of the altered area, is determined. The alteration degree data is determined comprehensively by the degree of change in mineral assemblage and the magnitude of boundary movement, and is divided into three levels: strong alteration, moderate alteration, and weak alteration. The above data is compiled into a zonal alteration area data table, including alteration type, regional extent, alteration degree, and main geological dynamic factors.

[0072] Step S1210: Integrate the data of the zonal inheritance area, the data of the zonal transformation area, the data of geological dynamic factors, the data of boundary changes, and the data of mineral assemblage differences to form zonal evolution correlation data.

[0073] In this embodiment, the data of the inherited zoning region and the data of the modified zoning region are spatially integrated to ensure coverage of the entire range of the zoning structure in the subsequent stage. Geological dynamic factor data, boundary change data, and mineral assemblage difference data are associated with the inherited and modified regions, respectively, forming complete zoning evolution correlation data. This zoning evolution correlation data is stored in the form of a spatiotemporal matrix, where rows represent zoning units of the previous stage and columns represent zoning units of the subsequent stage. Matrix elements include information such as inheritance or modification type, geological dynamic factors, and the magnitude of change. This zoning evolution correlation data can be used to demonstrate the evolution of the altered mineral zoning structure over time.

[0074] Step S1211: Supplement the mineral assemblage composition change data, element enrichment data and formation environment parameters corresponding to each zonal structure, and combine them with the zonal evolution correlation data to form an alteration mineral zonal evolution sequence. This alteration mineral zonal evolution sequence includes the evolution process, spatial characteristics and stage correlation data of alteration mineral zoning in different periods.

[0075] In this embodiment, for each time-phase zonation structure, data on the changes in mineral assemblage composition over time within that phase are supplemented by comparing mineral assemblage data from different sub-phases within the same zonation structure. Element enrichment data are derived from regional geochemical measurements, extracting the average content and outlier distribution of ore-forming elements such as lead and zinc within the zonation structure range, and calculating the element enrichment coefficient, i.e., the ratio of the average element content within the zonation to the regional background value. Formation environment parameters, including temperature, pressure, and pH, are obtained through mineral inclusion testing and thermodynamic simulations; each zonation structure corresponds to a set of formation environment parameter ranges. The supplemented data and zonation evolution correlation data are integrated and arranged chronologically to form an alteration mineral zonation evolution sequence. This alteration mineral zonation evolution sequence, using a time axis as a guide, displays the spatial characteristics, mineral assemblage, element enrichment, and formation environment of each stage of the zonation structure, as well as the inheritance and transformation relationships between stages. The data format is a multidimensional dataset containing time phases, zonation structures, evolution correlations, and supplementary parameters.

[0076] Step S130: Based on the spatial distribution morphology of ore-forming structures, the physical and mechanical properties of stratigraphy and magmatic heat conduction pathways in the regional ore-controlling conditions information, quantify the synergistic response intensity of various ore-controlling conditions to the formation of alteration minerals.

[0077] In this embodiment, after obtaining information on regional ore-controlling conditions, it is necessary to quantitatively analyze three types of ore-controlling conditions—ore-forming structures, stratigraphic lithology, and magmatic activity—to assess the intensity of their synergistic effect on the formation of altered minerals. First, a correlation model is established between the three types of ore-controlling conditions and the conditions for altered mineral formation. Then, by calculating the response coefficients of each condition and combining them with weight allocation, the synergistic response intensity is obtained.

[0078] Step S131: Extract the spatial distribution morphology data of the mineralization structure from the regional ore-controlling conditions information using a 3D modeling tool. This spatial distribution morphology data includes the strike orientation, dip angle, dip angle, and width of the structural fracture zone.

[0079] In this embodiment, a 3D modeling tool is used to process the metallogenic structural data in the regional ore-controlling condition information. First, regional seismic exploration data, geological profile data, and borehole structural measurement data are imported, and a structural surface model is constructed in the 3D modeling software. The strike azimuth of each structure is extracted using a model analysis tool. The strike azimuth is represented by the strike angle of the structural surface, ranging from 0 to 360 degrees. The dip angle is the angle between the downward dip direction of the structural surface and true north, also ranging from 0 to 360 degrees. The dip angle is the angle between the structural surface and the horizontal plane, ranging from 0 to 90 degrees. The width data of the structural fracture zone is obtained by measuring the distribution range of fractured rocks on both sides of the structural surface. The maximum horizontal width of the fracture zone is measured in the 3D model along a direction perpendicular to the structural strike. The above data is organized into a spatial distribution morphology dataset containing structural identifiers, strike azimuth, dip angle, dip angle, and fracture zone width. The parameters of each structure are averaged after measurements from at least three different profiles.

[0080] Step S132: Use rock mechanics test data analysis tools to extract the physical and mechanical properties data of the strata lithology. The physical and mechanical properties data include the compressive strength, tensile strength, porosity and permeability data of the rock.

[0081] In this embodiment, test data of the main stratigraphic lithologies in the study area are retrieved from the regional rock mechanics database, and data processing is performed using a rock mechanics test data analysis tool. Compressive strength data is obtained from uniaxial compression tests, and the average value of each group of rock samples is taken, in megapascals (MPa). Tensile strength data is obtained through Brazilian splitting tests, and the average value is also taken, in MPa. Porosity data is measured using a helium porosimeter, and the results are expressed as a percentage. Permeability data is measured using the steady-state method, in millidarcy. For the same lithological unit, if multiple test data exist, the arithmetic mean and standard deviation are calculated, and outliers are removed. The processed data is classified according to stratigraphic lithological units, forming a physical and mechanical property dataset containing lithology name, compressive strength, tensile strength, porosity, and permeability. The sample size for each lithological unit is no less than a predetermined number.

[0082] Step S133: Use a heat conduction numerical simulation tool to extract heat conduction path data of magmatic activity. This heat conduction path data includes the direction, rate, heat source temperature and influence range of heat conduction, and at the same time extract the material ejection data of magmatic activity.

[0083] In this embodiment, a three-dimensional heat conduction model is established using a numerical simulation tool based on regional magmatic rock distribution data and geothermal measurement data. The heat source in the model is set as a known magmatic intrusion, the temperature of which is determined based on petrological studies. The direction of heat conduction is determined by the simulated temperature gradient vector, and the heat conduction rate is calculated based on the rock's thermal conductivity and temperature gradient, in the form that the heat conduction rate equals the thermal conductivity multiplied by the temperature gradient. The influence range data is determined by simulating the isotherm distribution at different time points, typically using the isotherm range corresponding to the temperature range of alteration mineral formation as the thermal influence range. The material ejection data of magmatic activity is obtained by measuring the thickness and distribution area of ​​volcanic rock strata and combining it with rock density, in the form that the material ejection amount equals the thickness multiplied by the area multiplied by the density, with the density value selected according to the rock type. The simulation and calculation results are compiled into a dataset containing magmatic activity identifiers, heat conduction direction vectors, heat conduction rates, heat source temperatures, influence range coordinates, material ejection amount, heat conduction paths, and ejection amount data.

[0084] Step S134: Collect physicochemical condition data for the formation of altered minerals, including the temperature range, pressure range, and chemical fluid composition data required for mineral formation.

[0085] In this embodiment, physicochemical conditions data for the formation of major alteration minerals in the study area were collected by reviewing regional alteration mineralogical research literature and laboratory analysis reports. Temperature range data were derived from mineral inclusion microthermometry results, with each mineral corresponding to a specific temperature range. Pressure range data were obtained through geological pressure gauge calculations, with results expressed in kilobars. Chemical fluid composition data, including fluid pH, redox potential, and major ion concentrations, were determined through fluid inclusion composition analysis and thermodynamic simulations. The above data were categorized by mineral type to form a physicochemical conditions dataset containing mineral name, temperature range, pressure range, pH range, redox potential range, and major ion concentration. Each data item was annotated with its source and reliability level.

[0086] Step S135: Establish the correlation mapping rules between ore-controlling conditions and alteration mineral formation, and establish matching standards between ore-forming structural spatial distribution morphology data and fluid channel conditions, strata lithology physical and mechanical property data and fluid permeability conditions, and magmatic activity parameters and thermodynamic conditions.

[0087] In this embodiment, based on geological theory and previous research, a correlation mapping rule is established between three types of ore-controlling conditions and alteration mineral formation conditions. For the matching criteria between ore-forming structures and fluid channel conditions, structures with a fracture zone width greater than a set value and a dip angle greater than a set angle are defined as effective fluid channels; channel efficiency increases when the angle between the strike azimuth and the regional principal stress direction is less than a set angle. The matching criteria between stratigraphic lithology and fluid permeability conditions are: rocks with porosity greater than a set percentage and permeability greater than a set value are conducive to fluid permeability; rocks with compressive strength less than a set value are prone to fracturing to form permeability channels. The matching criteria between magmatic activity and thermodynamic conditions include: the heat source temperature being at least a set temperature value higher than the lower limit of the alteration mineral formation temperature; the heat conduction rate being greater than a set value; and the material ejection volume being greater than a set volume, providing sufficient thermodynamic force. These criteria are quantified into specific thresholds and calculation formulas to form a correlation mapping rule table, which serves as the basis for subsequent response coefficient calculations.

[0088] Step S136: Based on the aforementioned association mapping rules, calculate the matching compliance rate between the width and dip angle of the mineralized fracture zone and the fluid channel conditions, and derive the first response coefficient.

[0089] In this embodiment, the width and dip angle of the fracture zone of the mineralized structure are standardized according to the association mapping rules. The standardized value of the fracture zone width is equal to the actual width divided by the standard width; if the result is greater than one, it is rounded down to one. The standardized value of the dip angle is equal to the actual dip angle divided by 90 degrees. The matching compliance rate is the product of the standardized value of the fracture zone width and the standardized value of the dip angle. If the angle between the structural strike azimuth and the direction of the regional principal stress is less than a set angle, it is multiplied by a set correction coefficient. The first response coefficient is equal to the matching compliance rate, ranging from zero to a set upper limit.

[0090] Step S137: Calculate the matching rate of rock porosity, permeability and fluid permeability conditions, and derive the second response coefficient.

[0091] In this embodiment, the rock porosity and permeability are standardized according to the association mapping rule to derive the second response coefficient.

[0092] For example, step S1371: Extract rock porosity data and permeability data from the physical and mechanical properties data of the strata lithology. The porosity data is recorded in percentage form, and the permeability data is measured in standard units.

[0093] In this embodiment, rock porosity and permeability data for the current analysis unit are extracted from the formation lithology physical and mechanical properties dataset. Porosity data is expressed as a percentage, and permeability data is expressed in millidarcy units. Outlier handling is ensured, and the data for each lithological unit is the average of a predetermined number of samples.

[0094] Step S1372: Collect fluid permeability condition data required for the formation of altered minerals, including the minimum porosity threshold and minimum permeability threshold required for fluid flow during mineral formation.

[0095] In this embodiment, the minimum threshold of fluid permeability conditions is extracted from the dataset of physicochemical conditions for alteration mineral formation. Based on the association mapping rules, the minimum porosity threshold is set to a set percentage, and the minimum permeability threshold is set to a set value. These thresholds are determined based on the fluid flow conditions required for the formation of the main alteration minerals within the region.

[0096] Step S1373: Calculate the ratio of rock porosity data to the minimum porosity threshold to obtain the porosity compliance rate; calculate the ratio of rock permeability data to the minimum permeability threshold to obtain the permeability compliance rate.

[0097] In this embodiment, the proportion of porosity meeting the standard is equal to the rock porosity data divided by the minimum porosity threshold. The proportion of permeability meeting the standard is equal to the rock permeability data divided by the minimum permeability threshold.

[0098] Step S1374: Calculate the average of the porosity compliance rate and the permeability compliance rate to obtain the fluid permeability condition matching degree. If the fluid permeability condition matching degree exceeds the upper limit standard, correct the matching degree to the upper limit standard; if the fluid permeability condition matching degree is lower than the lower limit standard, correct the matching degree to the lower limit standard.

[0099] In this embodiment, the fluid permeability matching degree is equal to the arithmetic mean of the proportion of porosity meeting the standard and the proportion of permeability meeting the standard. The upper and lower limits are determined based on the statistical analysis of fluid permeability conditions in known mineralized areas within the region. If the calculated matching degree exceeds the upper limit, it is corrected to the upper limit; if the matching degree is lower than the lower limit, it is corrected to the lower limit.

[0100] Step S1375: Extract the distribution area ratio data of the stratigraphic lithology in the region, adjust the corrected fluid permeability condition matching degree according to the distribution area ratio data, and use the adjusted matching degree value as the second response coefficient of the physical and mechanical properties of the stratigraphic lithology to the formation of altered minerals.

[0101] In this embodiment, the distribution area of ​​the current stratigraphic lithology is extracted from the regional geological map and divided by the total area of ​​the study area to obtain the distribution area percentage. The weighted adjustment method is that the second response coefficient is equal to the corrected fluid permeability matching degree multiplied by the distribution area percentage.

[0102] Step S1376: Extract the second response coefficient data of stratigraphy and lithology of known mineralized areas and the corresponding alteration mineral development data, verify the rationality of the calculated second response coefficient, and correct the deviation data.

[0103] In this embodiment, second response coefficient data of stratigraphic lithology and corresponding alteration mineral development data are extracted from a geological database of known lead-zinc ore-forming areas. The correlation between the two is analyzed by plotting a scatter plot. If the correlation coefficient is less than a set value, the calculation parameters of the second response coefficient are adjusted until the correlation reaches or exceeds the set value. For example, if verification reveals that the second response coefficient is generally too high, the upper limit standard is lowered or the weighting coefficient is reduced, and the calculation and verification are repeated until the result is reasonable.

[0104] Step S138: Calculate the matching rate of magmatic activity heat source temperature, heat conduction rate and thermodynamic conditions, and derive the third response coefficient by combining the correction factor of material ejection volume data on the thermal influence range.

[0105] In this embodiment, the heat source temperature and heat conduction rate of magmatic activity are standardized according to the correlation mapping rules. The standardized value of the heat source temperature is equal to (actual temperature minus the lower limit of the alteration mineral formation temperature) divided by the set temperature value; if the result is greater than one, it is rounded down to one. The standardized value of the heat conduction rate is equal to the actual rate divided by the set rate value; if the result is greater than one, it is rounded down to one. The thermodynamic condition matching compliance rate is the product of the standardized value of the heat source temperature and the standardized value of the heat conduction rate. The material eruption volume correction factor is equal to the actual eruption volume divided by the set volume value; if the result is greater than the set multiple, it is rounded down to the set multiple. The third response coefficient is equal to the thermodynamic condition matching compliance rate multiplied by the material eruption volume correction factor, ranging from zero to the set upper limit.

[0106] Step S139: Collect the corresponding data of the three types of response coefficients and the degree of development of alteration minerals in known lead-zinc ore mineralization areas, and use correlation analysis to determine the weight allocation data of the first response coefficient, the second response coefficient, and the third response coefficient.

[0107] In this embodiment, in order to determine the weights of the three types of response coefficients, it is necessary to collect relevant data from known mineralized areas and perform correlation analysis.

[0108] Step S1391: Collect a large amount of metallogenic tectonic response coefficient data, stratigraphic lithology response coefficient data, magmatic activity response coefficient data, and corresponding alteration mineral development data for known lead-zinc ore-forming areas. The known areas cover different geological backgrounds and ore deposit types.

[0109] In this embodiment, a set number of known metallogenic regions with different geological backgrounds and deposit types were selected from domestic and international lead-zinc deposit databases. For each region, the first response coefficient of metallogenic structures, the second response coefficient of stratigraphic lithology, the third response coefficient of magmatic activity, and the corresponding data on the development degree of alteration minerals were collected. The data on the development degree of alteration minerals were comprehensively evaluated using three indicators: alteration zone thickness, number of alteration mineral types, and mineral assemblage complexity. Each indicator was divided into a set number of levels, and the average value was taken as the comprehensive development degree index.

[0110] Step S1392: Standardize the collected metallogenic structural response coefficient data, stratigraphic lithology response coefficient data, magmatic activity response coefficient data, and alteration mineral development degree data to form a standardized dataset.

[0111] In this embodiment, the data on the three types of response coefficients and the degree of alteration mineral development are standardized to unify their numerical range to between zero and one. The standardization method is (actual value minus minimum value) divided by (maximum value minus minimum value). After standardizing all the data, a standardized dataset is formed containing a set number of samples, with each sample containing four standardized indicators.

[0112] Step S1393: Use correlation analysis algorithm to calculate the first correlation coefficient between the metallogenic tectonic response coefficient and the degree of alteration mineral development, the second correlation coefficient between the stratigraphic lithology response coefficient and the degree of alteration mineral development, and the third correlation coefficient between the magmatic activity response coefficient and the degree of alteration mineral development.

[0113] In this embodiment, the Pearson correlation coefficient algorithm is used to calculate the linear correlation coefficient between each response coefficient and the alteration mineral development degree index. The calculation process of the Pearson correlation coefficient is as follows: First, the covariance of the two variables is calculated. The covariance is equal to the sum of (standardized value of variable A minus the mean of variable A) and (standardized value of variable B minus the mean of variable B) for each sample, divided by (sample size minus one); then, the standard deviation of the two variables is calculated; finally, the correlation coefficient is equal to the covariance divided by the product of the standard deviations of the two variables. The first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are obtained through calculation.

[0114] Step S1394: Normalize the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient to obtain the first weight value, the second weight value, and the third weight value, and the sum of the three values ​​meets the preset ratio standard.

[0115] In this embodiment, the preset ratio standard is that the sum of the weight values ​​equals one. The normalization calculation method is to divide each correlation coefficient by the sum of the three correlation coefficients. The sum of the three is then checked to see if it meets the preset standard.

[0116] Step S1395: Verify the consistency between the weight values ​​and the actual contribution of the ore-controlling conditions in the known ore-forming areas, adjust the weight value deviation, and form weight allocation data.

[0117] In this embodiment, a set number of typical known mineralized areas are selected, and the actual contribution ratio of each ore-controlling condition is determined based on geological research. The calculated weight values ​​are compared with the actual contribution ratios to calculate the deviation. If the deviation exceeds a set percentage, the calculation method of the correlation coefficient or the sample selection is adjusted, and the weight values ​​are recalculated until the deviation between the weight values ​​and the actual contribution ratios is within the set percentage.

[0118] Step S1396: Record the first weight value, the second weight value, the third weight value and the adjustment basis to form a weight allocation scheme. This weight allocation scheme includes data on the contribution of various ore-controlling conditions to the formation of alteration minerals.

[0119] In this embodiment, the final determined first, second, and third weight values ​​are recorded in the weight allocation scheme, and the data collection process, correlation analysis method, verification samples, and adjustment basis are described in detail. Contribution data is directly represented by weight values. This weight allocation is stored as a structured file, containing a scheme identifier, a list of weight values, a verification report, and version information.

[0120] Step S1310: Based on the weighted data, the first response coefficient, the second response coefficient, and the third response coefficient are weighted and summed to generate the synergistic response intensity of various ore-controlling conditions to the formation of altered minerals. The synergistic response intensity is presented in a predefined numerical range.

[0121] In this embodiment, the formula for calculating the collaborative response intensity is: collaborative response intensity equals the first response coefficient multiplied by the first weight value, plus the second response coefficient multiplied by the second weight value, plus the third response coefficient multiplied by the third weight value. Predefined numerical intervals are divided into: weak response interval, moderate response interval, strong response interval, and extremely strong response interval. The collaborative response intensity level of a region is determined based on the calculation results. The collaborative response intensity calculation results of all regions are correlated with their corresponding spatial coordinates to form a spatial distribution map of collaborative response intensity.

[0122] Step S140: Combining the alteration mineral zoning evolution sequence and the co-response intensity, the spatial location and extension range of the lead-zinc ore mineralization window are determined.

[0123] In this embodiment, after obtaining the alteration mineral zoning evolution sequence and synergistic response intensity data, the spatial location and extension range of the lead-zinc mineralization window are determined through spatial overlay analysis and multi-factor comprehensive evaluation. The mineralization window refers to a region where alteration minerals are well-developed and ore-controlling conditions have a strong synergistic effect; it is a favorable location for lead-zinc mineralization.

[0124] Step S141: Extract the spatial boundary data of the main distribution zone of each stage of the alteration mineral zoning evolution sequence. The spatial boundary data of the main distribution zone includes the lateral distribution coordinates, longitudinal burial depth range and planar projected area data of the main distribution zone.

[0125] In this embodiment, spatial boundary data of the main distribution zones for each stage of the zoning structure are extracted from the alteration mineral zoning evolution sequence dataset, categorized by time period. Lateral distribution coordinates are represented by the latitude and longitude of polygon vertices, and the longitudinal burial depth range comprises the top and bottom boundaries of the main distribution zones. The planar projected area is obtained by calculating the area of ​​the polygons. The above data is then organized into a spatial parameter table for the main distribution zones, including time period identifiers, main distribution zone identifiers, a list of lateral coordinates, top boundary depths, bottom boundary depths, and planar areas.

[0126] Step S142: Select regions with values ​​higher than a preset threshold from the collaborative response intensity, and extract the spatial coordinate range data of mineralized structures, stratigraphic lithology and magmatic activity within the region to form a high-value mineralization control region dataset.

[0127] In this embodiment, the preset threshold is determined based on the distribution characteristics of the collaborative response intensity, generally taking the lower limit of the strong response interval. In the spatial distribution map of collaborative response intensity, all regions with collaborative response intensities greater than the preset threshold are extracted; these regions constitute high-value ore-controlling areas. For each high-value ore-controlling area, its spatial coordinate range is determined using a boundary extraction algorithm, including the maximum and minimum values ​​of the horizontal plane coordinates and the vertical depth range. Simultaneously, data on the distribution location of ore-forming structures, the distribution range of stratigraphic lithology, and the influence range of magmatic activity within this region are extracted, forming a high-value ore-controlling area dataset containing regional identifiers, spatial coordinate ranges, structural distribution, lithological distribution, and the range of magmatic activity.

[0128] Step S143: Use a spatial overlay analysis tool to overlay the spatial boundary data of the main distribution zones of each stage with the dataset of high-value ore-controlling areas, and extract the spatial coordinate data and time duration data of the overlapping areas to form a dataset of overlapping areas of each stage.

[0129] In this embodiment, the spatial overlay analysis function of Geographic Information System (GIS) software is used to overlay the spatial boundary data of the main distribution zones at each time stage with the dataset of high-value mineral-controlling areas. For each combination of main distribution zones and high-value mineral-controlling areas, the spatial intersection of the two is calculated to obtain the overlapping area. The spatial coordinate data of the overlapping area, the corresponding time stage, and the high-value mineral-controlling area identifier are recorded. If an overlapping area corresponds to multiple time stages, the information for each stage is recorded separately. The above data is organized into a stage overlapping area dataset containing overlapping area identifiers, spatial coordinates, a list of time stages, and high-value mineral-controlling area identifiers.

[0130] Step S144: Select regions in the overlapping area data of the screening stages where the number of existing stages exceeds a preset value, identify them as potential mineralization candidate regions, and extract the spatial range data, corresponding alteration mineral zoning evolution sequence data, and synergistic response intensity data of the potential mineralization candidate regions.

[0131] In this embodiment, the preset value is determined based on the number of time stages in the regional geological evolution history, generally taking a set percentage of the total number of stages. In the stage overlap region dataset, the number of surviving stages in each overlapping region is counted. Regions with more than the preset value of surviving stages are selected and identified as candidate regions for mineralization potential. For each candidate region, its spatial extent data, corresponding alteration mineral zoning evolution sequence data, and co-response intensity data are extracted to form a dataset of candidate regions for mineralization potential.

[0132] Step S145: Extract data on the combinational composition changes, elemental enrichment trends, and zoning structure integrity of alteration minerals from the alteration mineral zoning evolution sequence data corresponding to the candidate mineral potential areas to form a mineral characteristic dataset for the candidate areas.

[0133] In this embodiment, the data on changes in mineral composition is obtained by comparing the types and proportions of mineral assemblages at different time stages within the candidate region, and the change rate of major mineral types and the number of newly added or disappeared minerals are calculated. Elemental enrichment trend data is determined by analyzing the content change curves of ore-forming elements such as lead and zinc at different stages, and the annual growth rate of elemental content and enrichment anomaly multiples are calculated. Zonal structure integrity data includes the continuous distribution length of the main distribution zones and the proportion of fractured areas. The above data are organized by candidate region to form a candidate region mineral characteristic dataset containing region identifiers, changes in mineral composition, elemental enrichment trend parameters, and zonal structure integrity indicators.

[0134] Step S146: Extract the stability data of the synergistic effect and parameter change amplitude data of various mineralization control conditions from the synergistic response intensity data corresponding to the candidate mineralization potential areas to form a mineralization control feature dataset for the candidate areas.

[0135] In this embodiment, the stability data of synergistic effects is obtained by calculating the coefficient of variation of the synergistic response intensity within the candidate region. The coefficient of variation is equal to the standard deviation divided by the mean; the smaller the value, the higher the stability. The parameter variation data includes the maximum and average rate of change of the ore-forming tectonic response coefficient, stratigraphic lithology response coefficient, and magmatic activity response coefficient at different time stages. The above data are compiled into a candidate region ore-controlling feature dataset containing regional identifiers, synergistic effect stability coefficients, and the variation range of each response coefficient.

[0136] Step S147: Based on the element enrichment trend data in the mineral feature dataset of the candidate region, correct the mineral enrichment center coordinate data of the mineralization potential candidate region; combine the zonal structure integrity data, adjust the calculation weight of the geometric center coordinate, and derive the spatial center coordinate of the mineralization window.

[0137] In this embodiment, the spatial center coordinates of the mineralization window are determined by comprehensively considering the element enrichment trend and the integrity of the zoning structure.

[0138] Step S1471: Extract element enrichment trend data from the mineral feature dataset of the candidate region. This element enrichment trend data includes the content gradient change data of lead, zinc and associated beneficial elements, and the distribution data of high content areas.

[0139] In this embodiment, the content gradient variation data of lead, zinc, and associated beneficial elements, as well as the distribution data of high-content areas, are extracted from the mineral feature dataset of the candidate region. The content gradient variation data is used to generate elemental content contour maps using interpolation, and the content change rate in different directions is calculated. The high-content area distribution data is obtained by setting a content threshold and extracting areas exceeding the threshold from the contour maps, forming high-content area polygons. The above data are then integrated into an element enrichment trend dataset that includes element type, content gradient matrix, and coordinates of high-content areas.

[0140] Step S1472: Based on the element content gradient change data, determine the target enrichment region with the highest element enrichment degree, extract the three-dimensional spatial coordinate range of the target enrichment region, and calculate the geometric center of the target enrichment region as the initial mineral enrichment center coordinates.

[0141] In this embodiment, the region defined by the closed contour line with the highest elemental content is identified in the elemental content contour map as the target enrichment region. The three-dimensional spatial coordinate range of this region is extracted, with the horizontal coordinate being the vertex coordinates of the region's polygon and the vertical coordinate being the top and bottom boundary depths of the region. The geometric center of the target enrichment region is calculated, with the horizontal coordinate being the average of the polygon's vertex coordinates and the vertical coordinate being the average of the top and bottom boundary depths, thus obtaining the initial mineral enrichment center coordinates.

[0142] Step S1473: Extract the zonal structure integrity data from the candidate region mineral feature dataset. This zonal structure integrity data includes the continuous distribution length data of the main distribution zones and the proportion data of the broken areas.

[0143] In this embodiment, continuous distribution length data and fractured area proportion data of the main distribution zones are extracted from the candidate region mineral feature dataset. Continuous distribution length data is obtained by measuring the total length of continuous line segments at the zone boundaries, and the fractured area proportion data is the ratio of the area of ​​the fractured area within the zone to the total area of ​​the zone. The above data is then organized into a zone structure integrity data table containing zone identifiers, continuous distribution lengths, and fractured area proportions.

[0144] Step S1474: If the continuous distribution length data of the main distribution zone exceeds the preset value and the proportion of broken areas is lower than the preset value, the zoning structure is determined to be complete, and the weight of the mineral enrichment center coordinates and the weight of the geometric center coordinates of the mineralization potential candidate area are set to a set ratio; if the continuous distribution length data of the main distribution zone does not reach the preset value or the proportion of broken areas exceeds the preset value, the zoning structure is determined to be incomplete, and the weight of the mineral enrichment center coordinates and the weight of the geometric center coordinates of the mineralization potential candidate area are set to another set ratio.

[0145] In this embodiment, the preset continuous distribution length value and the threshold for the proportion of broken areas are set according to the regional geological characteristics. If the continuous distribution length of the main distribution zone is greater than the preset length and the proportion of broken areas is less than the preset threshold, the zoning structure is complete. In this case, the weight of the mineral enrichment center coordinates is set to a higher value, and the weight of the geometric center coordinates of the candidate mineralization potential area is set to a lower value, such as a ratio of seven to three. If the zoning structure is incomplete, the weight ratio is adjusted, such as setting it to five to five, to balance the influence of the mineral enrichment center and the regional geometric center.

[0146] Step S1475: Calculate the geometric center coordinates of the candidate region of mineralization potential. The geometric center coordinates of the first horizontal axis are the average of the maximum and minimum coordinate values ​​of the first horizontal axis. The geometric center coordinates of the second horizontal axis are the average of the maximum and minimum coordinate values ​​of the second horizontal axis. The geometric center coordinates of the vertical axis are the average of the maximum and minimum coordinate values ​​of the vertical axis.

[0147] In this embodiment, the horizontal first axis coordinate range of the mineralization potential candidate region is from the minimum value to the maximum value, and the geometric center coordinate is (maximum value plus minimum value) divided by two. The calculation method for the geometric center coordinates of the horizontal second axis and the vertical axis is the same. The geometric center coordinates of the mineralization potential candidate region obtained in the above manner serve as another reference point for calculating the spatial center coordinates.

[0148] Step S1476: Using a weighted average algorithm, the initial mineral enrichment center coordinates and the geometric center coordinates of the candidate mineralization potential area are calculated according to a set weight to obtain the spatial center coordinates of the mineralization window.

[0149] In this embodiment, the weighted average algorithm calculates each component of the spatial center coordinates as (initial mineral enrichment center coordinate component multiplied by the mineral enrichment center weight) plus (geometric center coordinate component of the mineralization potential candidate region multiplied by the geometric center weight). For example, if the initial mineral enrichment center coordinates of the first horizontal axis are A, the geometric center coordinates are B, and the weights are W1 and W2 respectively, then the spatial center coordinates of the first horizontal axis are A multiplied by W1 plus B multiplied by W2. The same calculation is performed on the second horizontal axis and the vertical axis coordinates to obtain the complete spatial center coordinates.

[0150] Step S1477: Verify whether the area corresponding to the spatial center coordinates is located within the mineralization potential candidate area. Combine the synergistic stability data in the mineralization control feature dataset of the candidate area to correct the coordinate deviation and finally determine the spatial center coordinates of the mineralization window.

[0151] In this embodiment, the calculated spatial center coordinates are compared with the boundary coordinates of the candidate mineralization potential region. If the coordinates are within the region, the verification is successful. If the coordinates are outside the region, the weight ratio is adjusted and the calculation is repeated until the spatial center coordinates are within the region. Simultaneously, if the synergistic stability coefficient in the mineralization control feature dataset of the candidate region is lower than a set threshold, the spatial center coordinates are fine-tuned, shifting towards regions with high synergistic response intensity. The shift amount is inversely proportional to the stability coefficient, ultimately determining the spatial center coordinates of the mineralization window.

[0152] Step S148: Referencing the combination composition variation data in the candidate region mineral feature dataset, determine the effective distribution span of the main distribution zone of alteration minerals, and calculate the lateral extension length of the mineralization window by combining the spatial center coordinate data.

[0153] In this embodiment, the combination composition change data is extracted from the mineral feature dataset of the candidate region to determine the effective distribution span of the main distribution zone of altered minerals, and then the lateral extension length is calculated.

[0154] For example, step S1481: extract the combination composition change data from the mineral feature dataset of the candidate region. The combination composition change data includes the change data of the quantity ratio of the main mineral and the secondary mineral along the spatial direction, the change data of the mineral symbiotic combination type along the spatial direction, and the change data of the spatial arrangement of mineral particles along the spatial direction.

[0155] In this embodiment, the data on changes in mineral composition is obtained by laying out survey lines along different spatial directions (such as east-west and north-south) and collecting mineral assemblage data at each point along the survey lines. The data on changes in the quantity ratio of primary minerals to secondary minerals is a sequence of primary and secondary mineral content ratios at each sampling point along the survey line; the data on changes in mineral symbiotic assemblage types is a sequence of mineral assemblage type codes at each sampling point along the survey line; and the data on changes in the spatial arrangement of mineral particles is a sequence of particle arrangement direction distribution matrices at each sampling point along the survey line. These data are then organized according to spatial direction to form a dataset on changes in mineral composition.

[0156] Step S1482: Based on the spatial variation data of the quantity ratio of the main mineral and the accessory mineral, identify the spatial coordinate data of the location where the quantity ratio of the main mineral and the accessory mineral changes significantly, as the first type of change boundary point data; based on the spatial variation data of the mineral symbiotic assemblage type, identify the spatial coordinate data of the location where the mineral symbiotic assemblage type changes significantly, as the second type of change boundary point data; based on the spatial variation data of the spatial arrangement of mineral particles, identify the spatial coordinate data of the location where the spatial arrangement of mineral particles changes regularly, as the third type of change boundary point data.

[0157] In this embodiment, for data on changes in the quantity ratio of primary minerals and secondary minerals, a mutation point detection algorithm is used to identify locations where the rate of change exceeds a set threshold, which are designated as first-type change boundary points. For data on changes in mineral coexistence assemblage types, when adjacent sampling points have different assemblage type codes, the intermediate position is recorded as a second-type change boundary point. For data on changes in the spatial arrangement of mineral particles, the difference in the distribution matrix of the arrangement direction of adjacent sampling points is calculated, and locations where the difference exceeds a set value are designated as third-type change boundary points. The spatial coordinate data of the above boundary points are recorded respectively to form a dataset of three types of change boundary points.

[0158] Step S1483: Merge the first type of change boundary point data, the second type of change boundary point data, and the third type of change boundary point data to form a comprehensive change boundary point dataset.

[0159] In this embodiment, the spatial coordinates of the three types of changing boundary point data are merged, and duplicate points and outliers (points that are too close to other points) are removed. A spatial coordinate clustering algorithm is used to merge boundary points whose distance is less than a set threshold into one boundary point, and the average coordinates of this merged boundary point are taken as the final boundary point coordinates. This forms a comprehensive changing boundary point dataset containing the spatial coordinates of all changing boundary points.

[0160] Step S1484: Perform spatial clustering analysis on the comprehensive change boundary point dataset to identify dense areas of change boundary points and determine the outer envelope boundary of each dense area of ​​change boundary points as effective distribution boundary data of the main distribution zone of alteration minerals.

[0161] In this embodiment, a density clustering algorithm is used to perform cluster analysis on the comprehensive change boundary point dataset. A clustering radius and a minimum number of points are set to divide the boundary points into different dense regions. For each dense region, a convex hull algorithm is used to calculate its outer envelope boundary, which is the effective distribution boundary of the main distribution zone of altered minerals. The effective distribution boundary data is stored in the form of a sequence of polygon vertex coordinates.

[0162] Step S1485: Based on the effective distribution boundary data of the main distribution zone of the altered minerals, calculate the effective distribution span data of the main distribution zone of the altered minerals. The effective distribution span data is the maximum straight-line distance between the effective distribution boundary data of the main distribution zone of the altered minerals along a set horizontal direction.

[0163] In this embodiment, the horizontal direction is defined as the vertical direction of the main structural orientation of the region. Among the polygon vertices of the effective distribution boundary data, two extreme points along the defined horizontal direction are identified, and the straight-line distance between these two points is calculated as the effective distribution span data. If the effective distribution boundary consists of multiple discontinuous polygons, the effective distribution span of each polygon is calculated separately, and the maximum value is taken as the final effective distribution span data.

[0164] Step S1486: Extract the spatial center coordinate data of the mineralization window. Using the spatial center coordinate data of the mineralization window as the origin, extend the line to both sides along the set horizontal direction corresponding to the effective distribution span data until the extension line touches the effective distribution boundary data of the main distribution zone of the altered minerals. Record the coordinate data of the touch points on both sides.

[0165] In this embodiment, two rays in opposite directions are generated along a predetermined horizontal direction, starting from the spatial center coordinates. The intersection point of each ray with the effective distribution boundary polygon is calculated, i.e., the touch point. If a ray has multiple intersection points with the boundary, the intersection point farthest from the center coordinates is taken as the touch point. The spatial coordinate data of the touch points on both sides are recorded.

[0166] Step S1487: Calculate the straight-line distance between the coordinates of the two touch points on both sides with the spatial center coordinates of the ore-forming window as the origin, and use the straight-line distance data as the lateral extension length data of the ore-forming window.

[0167] In this embodiment, the straight-line distance between the two touch points is calculated based on their coordinate data. This distance is the lateral extension length of the ore-forming window. The calculation method is that the lateral extension length is equal to the square root of the sum of the squares of the lateral coordinate differences and the squares of the longitudinal coordinate differences between the two points (considering only the horizontal direction). The calculation result is recorded as the lateral extension length data of the ore-forming window.

[0168] Step S149: Based on the synergistic stability data in the candidate region ore-controlling feature dataset, verify the reliability of the longitudinal burial depth of the main distribution zone, and calculate the longitudinal extension depth of the ore-forming window by combining the longitudinal burial depth range data.

[0169] In this embodiment, synergistic stability data is extracted from the candidate region ore-controlling feature dataset to verify and correct the vertical burial depth, and then the vertical extension depth is calculated.

[0170] For example, step S1491: extract the longitudinal burial depth range data from the candidate region mineral feature dataset. The longitudinal burial depth range data includes the top burial depth data and the bottom burial depth data of the main distribution zone of the altered minerals. Calculate the difference between the top burial depth data and the bottom burial depth data as the original longitudinal depth range data.

[0171] In this embodiment, the top burial depth in the longitudinal burial depth range data is the highest elevation depth of the main distribution zone, and the bottom burial depth is the lowest elevation depth. The original longitudinal depth range data is equal to the bottom burial depth minus the top burial depth (because depth is positive downwards).

[0172] Step S1492: Extract the synergistic stability data from the candidate region ore-controlling feature dataset. The synergistic stability data includes the stability data of the spatial distribution morphology of ore-forming structures, the stability data of the physical and mechanical properties of stratigraphic lithology, and the stability data of the heat conduction path of magmatic activity.

[0173] In this embodiment, the stability data of the spatial distribution morphology of mineralized structures is represented by the coefficient of variation of structural strike, dip, and dip angle at different time stages; the stability data of the physical and mechanical properties of stratigraphic lithology is represented by the coefficient of variation of parameters such as compressive strength and porosity; and the stability data of the heat conduction path of magmatic activity is represented by the coefficient of variation of heat conduction rate and temperature gradient. The smaller the coefficient of variation, the higher the stability.

[0174] Step S1493: Based on the stability data and trend data of the spatial distribution morphology of the mineralized structures, assess the degree of reliability impact of the mineralized structures on the original vertical depth range data, and generate a first reliability correction coefficient.

[0175] In this embodiment, if the coefficient of variation of the stability data of the spatial distribution morphology of the mineralized structure is less than a set threshold and the trend of change is stable, the first reliability correction coefficient is a value close to one; if the coefficient of variation is greater than the set threshold or the trend of change is drastic, the first reliability correction coefficient is less than one. The specific value is determined according to the coefficient of variation and the trend slope. The larger the coefficient of variation and the more drastic the trend, the smaller the correction coefficient.

[0176] Step S1494: Based on the stability data and variation gradient data of the physical and mechanical properties of the strata lithology, assess the degree of influence of the strata lithology on the reliability of the vertical depth range data after the first adjustment, and generate a second reliability correction coefficient.

[0177] In this embodiment, the first adjusted longitudinal depth range data is equal to the original longitudinal depth range data multiplied by the first reliability correction coefficient. The evaluation method for the stability data of stratigraphic lithological physical and mechanical properties is similar to that for mineralized structures. A second reliability correction coefficient is generated based on its coefficient of variation and gradient, and the first adjusted longitudinal depth range data is then corrected again.

[0178] Step S1495: Based on the stability data of the magmatic activity heat conduction path and the migration direction data, assess the degree of influence of magmatic activity on the reliability of the second adjusted longitudinal depth range data, and generate a third reliability correction coefficient.

[0179] In this embodiment, the longitudinal depth range data after the second adjustment is equal to the data after the first adjustment multiplied by the second reliability correction coefficient. The stability data of the magmatic activity heat conduction path generates a third reliability correction coefficient based on its coefficient of variation and the angle between the migration direction and the longitudinal depth direction. The smaller the angle, the greater the impact, and the larger the adjustment range of the correction coefficient.

[0180] Step S1496: Based on the first reliability correction coefficient, the second reliability correction coefficient and the third reliability correction coefficient, the original longitudinal depth range data is comprehensively corrected to generate the verified longitudinal burial depth range data of the main distribution zone.

[0181] In this embodiment, the comprehensive correction method is that the verified longitudinal depth range data is equal to the original longitudinal depth range data multiplied by the first reliability correction coefficient, the second reliability correction coefficient, and the third reliability correction coefficient. Simultaneously, based on the comprehensive evaluation of the synergistic stability data, if the overall stability is high, the correction magnitude is small; if the overall stability is low, the correction magnitude is large, ensuring that the verified depth range data better reflects the actual geological conditions.

[0182] Step S1497: Extract the verified burial depth data of the top and bottom of the main distribution zone from the verified main distribution zone longitudinal burial depth range data, and calculate the difference between the verified burial depth data of the top and bottom of the main distribution zone as the longitudinal extension depth data of the mineralization window.

[0183] In this embodiment, the verified top burial depth data is the original top burial depth data multiplied by a comprehensive correction factor, and the verified bottom burial depth data is the original bottom burial depth data multiplied by a comprehensive correction factor. The longitudinal extension depth data is equal to the verified bottom burial depth data minus the verified top burial depth data, thus obtaining the vertical extension length of the ore-forming window.

[0184] Step S1410: Integrate the spatial center coordinates, lateral extension length and vertical extension depth data of the mineralization window, and combine them with the mineral feature dataset of the candidate area and the ore-controlling feature dataset of the candidate area to form the spatial location and extension range data of the lead-zinc mineralization window. This spatial location and extension range data includes the three-dimensional spatial parameters and associated geological feature data of the area with the highest mineralization potential.

[0185] In this embodiment, the spatial center coordinates, lateral extension length, and longitudinal extension depth data of the mineralization window are integrated to form a three-dimensional spatial range. The spatial location is represented by the center coordinates, and the extension range is determined by the lateral extension length and longitudinal extension depth, forming a cuboid region extending laterally and longitudinally with the center coordinates as the midpoint. Simultaneously, mineral assemblage and element enrichment data from the candidate region mineral feature dataset are correlated with the synergistic response intensity and stability data from the candidate region ore-controlling feature dataset, forming complete mineralization window data containing spatial parameters and geological features.

[0186] Step S150: Based on the spatial location, extension range, and element enrichment status in the regional alteration mineral mapping results of the mineralization window, the spatial range of the target area is delineated and the boundary is optimized to generate the lead-zinc ore target area delineation results. The lead-zinc ore target area delineation results include the three-dimensional spatial boundary of the target area, the corresponding alteration mineral zoning characteristics, and the synergistic response parameters of the ore-controlling conditions.

[0187] In this embodiment, after determining the spatial location and extension range of the mineralization window, the target area range is further optimized by combining the element enrichment state to generate the final lead-zinc ore target area delineation result.

[0188] Step S151: Extract the spatial center coordinates, lateral extension length and longitudinal extension depth data of the mineralization window to construct a three-dimensional spatial framework of the initial range of the target area.

[0189] In this embodiment, a cuboid three-dimensional spatial frame is constructed with the spatial center coordinates of the ore-forming window as the center, extending horizontally as the diameter and vertically as the height, to serve as the initial range of the target area. The vertex coordinates of this frame are calculated by adding and subtracting half of the horizontal extension length and half of the vertical extension depth from the center coordinates.

[0190] Step S152: Extract the element enrichment status data from the regional alteration mineral mapping results, screen areas where the content of lead and zinc elements is higher than the background value, and form element enrichment area data.

[0191] In this embodiment, the elemental enrichment data in the regional alteration mineral mapping results includes the lead and zinc content of each sampling point. The background value is determined by adding three times the standard deviation to the average elemental content of the non-mineralized area within the statistical region. Sampling points with lead content higher than the lead background value or zinc content higher than the zinc background value are selected, and polygonal boundaries of the elemental enrichment region are generated using interpolation methods to form the elemental enrichment region data.

[0192] Step S153: Spatial overlay the three-dimensional spatial framework of the initial target area with the element-rich region data, retain the spatial coordinate data of the overlapping area, and form the target area pre-selection range data.

[0193] In this embodiment, a three-dimensional spatial overlay tool is used to perform an intersection operation between the cuboid frame of the initial target area and the three-dimensional spatial area of ​​the element-rich region to obtain the overlapping area. The spatial coordinate data of the overlapping area, including the horizontal plane coordinates and the vertical depth range, is extracted to form the target area pre-selection range data. If multiple discontinuous overlapping areas exist, the coordinate data of each area is recorded separately.

[0194] Step S154: Extract the alteration mineral zoning evolution sequence data corresponding to the pre-selected target area data, analyze the distribution continuity data and complete area proportion data of the main distribution zones of alteration minerals within the pre-selected target area, and form the distribution evaluation data of the main distribution zones.

[0195] In this embodiment, the main distribution zone data for each time stage within the pre-selected target area are extracted from the alteration mineral zoning evolution sequence dataset. Distribution continuity data is calculated by measuring the ratio of the length of continuous line segments of the main distribution zone within the target area to the total length; the percentage of intact area is the ratio of the area of ​​the unbroken portion of the main distribution zone within the target area to the total area of ​​the target area. The above data are then organized by time stage to form the main distribution zone distribution evaluation data.

[0196] Step S155: Based on the distribution evaluation data of the main distribution zone, expand the area where the main distribution zone is complete and not covered by the target area pre-selection range, shrink the area where the main distribution zone is missing or fragmented, adjust the boundary coordinates of the target area pre-selection range, and form the preliminary optimized range of the target area.

[0197] In this embodiment, for regions with high continuity and a large proportion of complete areas in the main distribution zone distribution evaluation data, if part of them is located outside the pre-selected target area, the target area boundary is expanded outward to include that part of the region. For regions within the pre-selected target area where the main distribution zone is missing or severely fragmented, the target area boundary is shrunk inward to exclude that part of the region. Boundary adjustment is achieved by moving the coordinates of the boundary points, and the distance of expansion or contraction is determined according to the distribution characteristics of the main distribution zone, ensuring that the adjusted target area includes as many complete main distribution zones as possible.

[0198] Step S156: Extract the collaborative response intensity data within the initial optimization range of the target area, retain the areas where the collaborative response intensity values ​​are higher than the preset standard, form the secondary optimization range of the target area, and use a three-dimensional spatial boundary smoothing algorithm to process the boundary coordinates of the secondary optimization range of the target area to eliminate jagged undulations and make the boundary conform to the natural distribution shape of the geological body, thus forming the final range data of the target area.

[0199] In this embodiment, the preset standard is the lower limit of the strong response range of the cooperative response intensity. All regions within the initial optimization range of the target area whose cooperative response intensity exceeds this standard are extracted to form the secondary optimization range of the target area. A three-dimensional spatial boundary smoothing algorithm is used to smooth the boundary coordinates of the secondary optimization range. The algorithm moves boundary points to conform to the curvature trend of adjacent points, eliminating sharp corners and jagged undulations. The smoothed boundary more closely resembles the natural shape of geological bodies, such as the smooth boundaries controlled by folds and faults.

[0200] Step S157: Extract the three-dimensional spatial boundary coordinate data of the final range data of the target area. The three-dimensional spatial boundary coordinate data includes the coordinates of all boundary points of the target area in the directions of the first horizontal axis, the second horizontal axis, and the vertical axis.

[0201] In this embodiment, the final target area data is a smoothed 3D polygon. The coordinates of all vertices of this polygon are extracted, and each vertex contains three coordinate components: a first horizontal axis, a second horizontal axis, and a vertical axis. These coordinate data are arranged sequentially to form a boundary coordinate list, ensuring that connecting the coordinate points in a clockwise or counter-clockwise order correctly constitutes the 3D boundary of the target area.

[0202] Step S158: Associate the three-dimensional spatial boundary coordinate data with the corresponding alteration mineral zoning characteristic data and ore-controlling condition co-response parameter data to form a comprehensive dataset of the target area.

[0203] In this embodiment, the alteration mineral zoning characteristic data includes the zoning structure, mineral assemblage, and element enrichment degree at each time stage within the target area; the ore-controlling condition synergistic response parameter data includes the response coefficients and synergistic response intensity of mineralization structures, stratigraphic lithology, and magmatic activity. Through spatial location correlation, the above data is correlated with the three-dimensional spatial boundary coordinate data of the target area to form a comprehensive target area dataset containing spatial and attribute information.

[0204] Step S159: Integrate the target area comprehensive dataset to generate lead-zinc mine target area delineation results. The lead-zinc mine target area delineation results include two presentation formats: a three-dimensional spatial model and a data table, which include the spatial location, geological features and mineralization potential data of the target area.

[0205] In this embodiment, the three-dimensional spatial model is constructed using three-dimensional modeling software. It uses the target area boundary coordinates as a framework, overlaying the three-dimensional distribution of alteration mineral zoning characteristics and synergistic response intensity. The data table includes basic parameters of the target area (spatial location, extent), alteration mineral characteristics (zoning evolution, mineral assemblage), ore-controlling condition parameters (response coefficient, synergistic intensity), and mineralization potential evaluation indicators (elemental enrichment degree, zoning integrity).

[0206] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of a lead-zinc ore target area delineation system 100 based on alteration mineral mapping results analysis provided in this application embodiment. The lead-zinc ore target area delineation system 100 based on alteration mineral mapping results analysis may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0207] In this embodiment, the machine-readable storage medium 120 can also be integrated into the processor 130 and can communicate and interact with external systems through the communication unit 110. The machine-readable storage medium 120 stores machine-executable instructions for executing the scheme of this application, and the processor 130 executes the machine-executable instructions stored in the machine-readable storage medium 120 to implement the lead-zinc ore target area delineation method based on alteration mineral mapping results analysis provided in the aforementioned method embodiments.

[0208] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results, characterized in that, The method includes: The results of regional alteration mineral mapping and regional ore-controlling conditions are obtained. The results of regional alteration mineral mapping include the lateral distribution span, vertical burial depth, mineral assemblage composition and element enrichment state of various alteration minerals in the region. The regional ore-controlling conditions include the spatial distribution morphology of metallogenic structures, the physical and mechanical properties of stratigraphic lithology, the amount of material ejected from magmatic activity and the heat conduction path. By combining the lateral distribution span, vertical burial depth and mineral assemblage in the regional alteration mineral mapping results with regional geological evolution history data, an alteration mineral zoning evolution sequence is constructed. This alteration mineral zoning evolution sequence includes the evolution process, spatial characteristics and stage correlation data of alteration mineral zoning in different periods. Based on the spatial distribution of metallogenic structures, the physical and mechanical properties of stratigraphy and magmatic activity heat conduction paths in the information of regional ore-controlling conditions, response coefficients are calculated. Weighted summation of each response coefficient is performed to generate the synergistic response intensity of various ore-controlling conditions on the formation of altered minerals, and the synergistic response intensity of various ore-controlling conditions on the formation of altered minerals is quantified. By combining the alteration mineral zoning evolution sequence and the co-response intensity, the spatial location and extension range of the lead-zinc ore mineralization window can be determined. Based on the spatial location, extension range, and element enrichment status of the regional alteration mineral mapping results of the mineralization window, the spatial range of the target area is delineated and the boundary is optimized to generate the lead-zinc ore target area delineation results. The lead-zinc ore target area delineation results include the three-dimensional spatial boundary of the target area, the corresponding alteration mineral zoning characteristics, and the synergistic response parameters of the ore-controlling conditions.

2. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 1, characterized in that, The alteration mineral zoning evolution sequence is constructed by combining the lateral distribution span, vertical burial depth, and mineral assemblage composition in the regional alteration mineral mapping results with regional geological evolution history data, including: Geological profile analysis tools were used to extract the lateral distribution span data of each type of alteration mineral in the regional alteration mineral mapping results. This lateral distribution span data was obtained by measuring the maximum horizontal distance of mineral distribution at the same burial depth, and the measurement was corrected by referring to the regional topographic and geomorphological maps. The vertical burial depth data of each type of alteration mineral is extracted by interpreting borehole core data. This vertical burial depth data is calculated by the vertical distance between the mineral occurrence layer and the surface reference level, and the influence value of topographic relief is deducted during the calculation. The mineral assemblage composition data of each type of altered mineral was extracted using a mineral assemblage microanalysis system. This mineral assemblage composition data includes the types of associated minerals, the ratio of the number of main minerals to accessory minerals, and the spatial arrangement of mineral grains. Collect regional geological evolution history data, which includes the tectonic movement periods, stratigraphic sedimentary sequences, magmatic intrusion times, and paleogeographic environmental changes in the region; Based on the tectonic movement periods in the regional geological evolution history data, the time stages of alteration mineral formation are divided, with each time stage corresponding to a specific tectonic movement intensity and influence range; The horizontal distribution span data, vertical burial depth data, and mineral assemblage data of each type of alteration mineral are classified and collected according to the corresponding formation time stage to form a staged mineral characteristic dataset. For each formation time stage, the spatial overlap relationship of the lateral distribution span data and vertical burial depth data of various alteration minerals in the phased mineral characteristic dataset is analyzed. The distribution overlap area data and center-to-center distance data of different minerals in the same time stage are derived to form a spatial distribution association dataset. Based on the spatial distribution association dataset, the main distribution zone, transitional distribution zone and peripheral distribution zone of alteration minerals in each time period are divided, the spatial boundary data of each zone are determined, and the alteration mineral zoning structure is formed. According to the chronological order of formation time stages, the alteration mineral zoning structure of each stage is linked together. By comparing the boundary overlap data and mineral assemblage composition data of the zoning structure of the previous stage and the next stage, the inherited area data and the altered area data are derived to form zoning evolution correlation data. Supplement each zonal structure with data on changes in mineral assemblage, elemental enrichment, and formation environment parameters. Combine this with zonal evolution correlation data to form an alteration mineral zonal evolution sequence. This alteration mineral zonal evolution sequence includes the evolutionary history, spatial characteristics, and stage correlation data of alteration mineral zoning at different periods.

3. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 1, characterized in that, The spatial distribution of metallogenic structures, the physical and mechanical properties of stratigraphy and magmatic activity, and the heat transfer pathways of magmatic activity, based on regional ore-controlling conditions, quantify the synergistic response intensity of various ore-controlling conditions to the formation of alteration minerals, including: The spatial distribution morphology data of mineralized structures in the regional ore-controlling conditions information is extracted by constructing a 3D modeling tool. This spatial distribution morphology data includes the strike orientation, dip angle, dip angle and width of the structural fracture zone. The physical and mechanical properties of the formation lithology were extracted using a rock mechanics test data analysis tool. This physical and mechanical properties data included the rock's compressive strength, tensile strength, porosity, and permeability. The heat conduction path data of magmatic activity was extracted using a numerical simulation tool for heat conduction. This heat conduction path data includes the direction, rate, heat source temperature and influence range of heat conduction. At the same time, the material ejection data of magmatic activity was also extracted. Collect physicochemical condition data for the formation of altered minerals, including the temperature range, pressure range, and chemical fluid composition data required for mineral formation. Establish correlation mapping rules between ore-controlling conditions and alteration mineral formation; establish matching standards between spatial distribution morphology data of ore-forming structures and fluid channel conditions; match standards between stratigraphic lithological physical and mechanical properties data and fluid permeability conditions; and match standards between magmatic activity parameters and thermodynamic conditions. Based on the aforementioned correlation mapping rules, the matching rate between the width and dip angle of the mineralized fracture zone and the fluid channel conditions is calculated, and the first response coefficient is derived. Calculate the matching rate of rock porosity and permeability with fluid permeability conditions, and derive the second response coefficient; The matching rate of magmatic activity heat source temperature, heat conduction rate and thermodynamic conditions is calculated. The correction factor for the thermal influence range is combined with the material ejection data to derive the third response coefficient. Data on the correspondence between the three types of response coefficients and the degree of development of alteration minerals in known lead-zinc ore-forming areas were collected, and correlation analysis was used to determine the weight allocation data of the first, second, and third response coefficients. Based on the weighted data, the first response coefficient, the second response coefficient, and the third response coefficient are weighted and summed to generate the synergistic response intensity of various ore-controlling conditions to the formation of altered minerals. This synergistic response intensity is presented using a predefined numerical range.

4. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 1, characterized in that, The method of combining the alteration mineral zoning evolution sequence and the co-response intensity to lock the spatial location and extension range of the lead-zinc ore mineralization window includes: The spatial boundary data of the main distribution zone of each stage of the zoning structure is extracted from the altered mineral zoning evolution sequence. The spatial boundary data of the main distribution zone includes the lateral distribution coordinates, longitudinal burial depth range and planar projected area data of the main distribution zone. From the collaborative response intensity, regions with values ​​higher than a preset threshold are selected, and the spatial coordinate range data of mineralized structures, stratigraphic lithology and magmatic activity in these regions are extracted to form a high-value mineral control region dataset. Spatial overlay analysis tools were used to overlay the spatial boundary data of the main distribution zones of each stage with the dataset of high-value ore-controlling areas, and the spatial coordinate data and temporal duration data of the overlapping areas were extracted to form a dataset of overlapping areas of each stage. Regions in the overlapping area dataset of the screening stage with more than the preset number of existing stages are identified as candidate regions for mineralization potential. Spatial range data, corresponding alteration mineral zoning evolution sequence data, and synergistic response intensity data of the candidate regions for mineralization potential are extracted. From the alteration mineral zoning evolution sequence data corresponding to the mineralization potential candidate areas, we extract the data on the combination composition changes of alteration minerals, element enrichment trend data, and zoning structure integrity data to form a mineral characteristic dataset for the candidate areas. From the synergistic response intensity data corresponding to the candidate mineralization potential areas, extract the synergistic stability data and parameter change amplitude data of various mineralization control conditions to form a mineralization control feature dataset for the candidate areas; Based on the element enrichment trend data in the mineral feature dataset of the candidate region, the mineral enrichment center coordinate data of the mineralization potential candidate region is corrected; combined with the zonal structure integrity data, the calculation weight of the geometric center coordinate is adjusted, and the spatial center coordinate of the mineralization window is derived. By referring to the combination composition variation data in the candidate region mineral feature dataset, the effective distribution span of the main distribution zone of alteration minerals is determined, and the lateral extension length of the mineralization window is calculated by combining the spatial center coordinate data. Based on the synergistic stability data in the candidate region ore-controlling feature dataset, the reliability of the longitudinal burial depth of the main distribution zone is verified. Combined with the longitudinal burial depth range data, the longitudinal extension depth of the ore-forming window is calculated. By integrating the spatial center coordinates, lateral extension length, and longitudinal extension depth data of the mineralization window, and combining them with the mineral characteristic dataset and ore-controlling characteristic dataset of the candidate region, spatial location and extension range data of the lead-zinc mineralization window are formed. This spatial location and extension range data includes the three-dimensional spatial parameters and associated geological feature data of the region with the highest mineralization potential.

5. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 2, characterized in that, For each formation time stage, the spatial overlap relationships of the lateral distribution span data and vertical burial depth data of various alteration minerals in the stage-specific mineral characteristic dataset are analyzed. This leads to the derivation of overlapping distribution areas and center-to-center distance data for different minerals within the same time stage, forming a spatial distribution correlation dataset, including: Extract the lateral distribution span data and vertical burial depth data of various alteration minerals from the stage mineral feature dataset of each formation time stage; Based on the horizontal distribution span data and the vertical burial depth data, a three-dimensional spatial coordinate system for this time period is constructed. The first horizontal axis and the second horizontal axis of the coordinate system correspond to the horizontal plane coordinates, and the vertical axis corresponds to the vertical burial depth. The distribution range of various alteration minerals is converted into a spatial geometry in this three-dimensional spatial coordinate system. The boundary of the geometry is determined by the maximum and minimum values ​​of the lateral distribution and the maximum and minimum values ​​of the longitudinal burial depth. Using a spatial geometry intersection calculation tool, the intersection of spatial geometries corresponding to any two alteration minerals is calculated, and the three-dimensional spatial coordinate range data, volume data and center position data of the intersection are recorded to form a dataset of overlapping distribution areas. Calculate the straight-line distance between the centers of the spatial geometry corresponding to any two altered minerals, record the straight-line distance data between the centers of each pair of altered minerals, and form a mineral center spacing dataset. The proportional relationship between the volume data in the overlapping region dataset and the total volume of each mineral's spatial geometry is analyzed, and the proportional relationship between the corresponding distance data in the mineral center spacing dataset and the preset reference distance is analyzed to comprehensively deduce the spatial correlation tightness data. For combinations of three or more altered minerals, data on the overlapping distribution areas and the straight-line distance between the centers of all pairs are collected, and the average correlation density of minerals within the combination is calculated to form a combination aggregation state dataset. By integrating datasets of overlapping distribution areas, mineral center spacing, spatial correlation density, and combined clustering states, a spatial distribution correlation dataset is formed within the same time period.

6. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 3, characterized in that, The method involves collecting data on the correspondence between the three types of response coefficients and the degree of alteration mineral development in known lead-zinc ore-forming areas, and using correlation analysis to determine the weight allocation data for the first, second, and third response coefficients, including: We collected a large amount of metallogenic tectonic response coefficient data, stratigraphic lithological response coefficient data, magmatic activity response coefficient data, and corresponding alteration mineral development data for known lead-zinc ore-forming areas. The known areas cover different geological backgrounds and ore deposit types. The collected data on metallogenic structural response coefficients, stratigraphic lithology response coefficients, magmatic activity response coefficients, and alteration mineral development were standardized to form a standardized dataset. The correlation analysis algorithm was used to calculate the first correlation coefficient between the metallogenic tectonic response coefficient and the degree of alteration mineral development, the second correlation coefficient between the stratigraphic lithology response coefficient and the degree of alteration mineral development, and the third correlation coefficient between the magmatic activity response coefficient and the degree of alteration mineral development. The first correlation coefficient, the second correlation coefficient, and the third correlation coefficient are normalized to obtain the first weight value, the second weight value, and the third weight value, and the sum of the three values ​​meets the preset ratio standard. Verify the consistency between the weight values ​​and the actual contribution of ore-controlling conditions in known ore-forming areas, adjust the weight value deviations, and form weight allocation data; Record the first weight value, the second weight value, the third weight value and the basis for adjustment to form a weight allocation scheme. This weight allocation scheme includes data on the contribution of various ore-controlling conditions to the formation of alteration minerals.

7. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 4, characterized in that, The mineral enrichment center coordinates of the mineralization potential candidate region are corrected based on the element enrichment trend data in the mineral feature dataset of the candidate region. By combining the zonal structure integrity data and adjusting the calculation weights of the geometric center coordinates, the spatial center coordinates of the ore-forming window are derived, including: Extract element enrichment trend data from the mineral feature dataset of the candidate region. This element enrichment trend data includes the content gradient change data of lead, zinc and associated beneficial elements, and the distribution data of high content areas. Based on the elemental content gradient variation data, the target enrichment region with the highest elemental enrichment degree is determined, the three-dimensional spatial coordinate range of the target enrichment region is extracted, and the geometric center of the target enrichment region is calculated as the initial mineral enrichment center coordinates. Extract the zonal structure integrity data from the mineral feature dataset of the candidate region. This zonal structure integrity data includes the continuous distribution length data of the main distribution zones and the proportion data of the broken areas. If the continuous distribution length of the main distribution zone exceeds the preset value and the proportion of the broken area is lower than the preset value, the zonal structure is determined to be complete, and the weight of the mineral enrichment center coordinates and the weight of the geometric center coordinates of the candidate area of ​​mineralization potential are set to a set ratio. If the continuous distribution length data of the main distribution zone does not reach the preset value or the proportion of the broken area exceeds the preset value, the zoning structure is determined to be incomplete, and the weight of the mineral enrichment center coordinates and the weight of the geometric center coordinates of the candidate area of ​​mineralization potential are set to another set ratio. The geometric center coordinates of the candidate regions for mineralization potential are calculated. The geometric center coordinates of the first horizontal axis are the average of the maximum and minimum coordinate values ​​of the first horizontal axis. The geometric center coordinates of the second horizontal axis are the average of the maximum and minimum coordinate values ​​of the second horizontal axis. The geometric center coordinates of the vertical axis are the average of the maximum and minimum coordinate values ​​of the vertical axis. A weighted average algorithm is used to calculate the spatial center coordinates of the mineral enrichment center and the geometric center coordinates of the mineralization potential candidate area according to a set weight, so as to obtain the spatial center coordinates of the mineralization window. To verify whether the area corresponding to the spatial center coordinates is located within the candidate area of ​​mineralization potential, the coordinate deviation is corrected by combining the synergistic stability data in the mineralization control feature dataset of the candidate area, and finally the spatial center coordinates of the mineralization window are determined.

8. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 1, characterized in that, Based on the spatial location, extension range, and elemental enrichment status of the mineralization window and the regional alteration mineral mapping results, the spatial range of the target area is delineated and the boundaries are optimized to generate the lead-zinc ore target area delineation results, including: Extract the spatial center coordinates, lateral extension length, and longitudinal extension depth data of the mineralization window to construct a three-dimensional spatial framework of the initial range of the target area; Extract element enrichment data from the regional alteration mineral mapping results, screen areas where lead and zinc content is higher than the background value, and form element enrichment area data; The three-dimensional spatial framework of the initial target area is spatially overlaid with the element-rich region data, and the spatial coordinate data of the overlapping area is retained to form the target area pre-selection range data. Extract alteration mineral zoning evolution sequence data corresponding to the pre-selected target area data, analyze the distribution continuity data and complete area proportion data of the main distribution zones of alteration minerals within the pre-selected target area, and form distribution evaluation data of the main distribution zones; Based on the distribution evaluation data of the main distribution zone, the area where the main distribution zone is complete and not covered by the target area pre-selection range is expanded, the area where the main distribution zone is missing or fragmented is shrunken, and the boundary coordinates of the target area pre-selection range are adjusted to form the preliminary optimized range of the target area. Extract the co-response intensity data within the initial optimization range of the target area, retain the areas where the co-response intensity values ​​are higher than the preset standard, and form the secondary optimization range of the target area. Use a three-dimensional spatial boundary smoothing algorithm to process the boundary coordinates of the secondary optimization range of the target area, eliminate jagged undulations, and make the boundary conform to the natural distribution shape of the geological body, thus forming the final range data of the target area. Extract the three-dimensional spatial boundary coordinate data of the final range of the target area. This three-dimensional spatial boundary coordinate data includes the coordinates of all boundary points of the target area in the directions of the first horizontal axis, the second horizontal axis, and the vertical axis. By associating the three-dimensional spatial boundary coordinate data with the corresponding alteration mineral zoning characteristic data and the ore-controlling condition co-response parameter data, a comprehensive dataset of the target area is formed. By integrating the comprehensive dataset of the target area, the delineation results of lead-zinc mine target areas are generated. These results include two presentation formats: a three-dimensional spatial model and a data table, which contain data on the spatial location, geological features, and mineralization potential of the target area.

9. The method for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results according to claim 2, characterized in that, The process involves sequentially linking the alteration mineral zoning structures of each formation stage, comparing the boundary overlap data and mineral assemblage data between the previous and subsequent stages, and deriving inherited and altered region data to form zoning evolution correlation data, including: The alteration mineral zoning structures of each stage are sorted according to their chronological order of formation time to form a time series of zoning structures. Extract spatial boundary coordinates and mineral assemblage data of the main distribution zone, transitional distribution zone and peripheral distribution zone of the zonal structure in the previous stage of the zonal structure time series. Extract spatial boundary coordinates and mineral assemblage data of the main distribution zone, transitional distribution zone and peripheral distribution zone of the zonal structure in the later stage of the zonal structure time series. A spatial overlay comparison tool is used to overlay the boundary coordinate data of the previous stage and the next stage of the zonal structure, calculate the percentage of the overlapping area, and determine the spatial coordinate data of the overlapping area. By comparing the mineral assemblage composition data of the zonal structure in the previous and subsequent stages, regions with consistent mineral types and quantities are selected to determine the data of the mineral assemblage inheritance regions. The spatial coordinate data of overlapping areas are merged with the mineral assemblage inherited area data to form zone inherited area data, and the area data and spatial location data of the zone inherited area data are recorded. Identify the regions in the zonation structure of the next stage that do not overlap with the previous stage, analyze the boundary change direction and magnitude data of the region, and compare the differences in mineral assemblage composition data. By combining data on tectonic movements and magmatic activity events in regional geological history data, we can deduce the data on the geodynamic factors that lead to boundary changes and differences in mineral assemblages. Based on geological dynamic factor data, the types of alteration are classified, and the regional scope and degree of alteration data for each type of alteration are determined to form zonal alteration regional data. Data on zonal inheritance areas, zonal transformation areas, geological dynamic factors, boundary changes, and mineral assemblage differences are integrated to form zonal evolution correlation data.

10. A target area delineation system for lead-zinc ore deposits based on the analysis of alteration mineral mapping results, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the lead-zinc ore target delineation method based on alteration mineral mapping results analysis as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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