Method for constructing mineralization alteration zoning of polymetallic deposit and determining prospecting direction

Abstract

The application provides a method for constructing mineralization alteration zoning of polymetallic deposit and determining prospecting direction, and relates to the technical field of mineral exploration; after a three-dimensional data matrix is constructed, a vertical sampling line is arranged along a main ore-controlling structure tendency, a change gradient of the abundance of potassium feldspar, silicified quartz and chlorite between sliding depth windows is calculated, a positive sharp increase of potassium feldspar abundance and a negative sharp decrease of low-temperature altered mineral abundance are coupled in space as a criterion for discrimination, an altered conversion boundary is accurately marked, and a main conversion boundary surface is generated by surface fitting. On this basis, a metallogenic center is locked in a medium-high temperature alteration domain through a potassium feldspar high-value area and a silver equivalent comprehensive mineralization intensity anomaly area, the deep prospecting target area is determined along the maximum inclination direction of the surface and according to the spatial distribution direction of the alteration core area, and the accuracy of positioning and prediction of the deep concealed ore body and the guidance of the exploration engineering deployment are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of mineral exploration technology, specifically a method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions. Background Technology

[0002] The Shuangjianzishan silver-lead-zinc deposit exhibits a vertical polymetallic zoning characteristic, with silver, lead, and zinc above and copper below. The shallow portion is dominated by low-to-medium temperature alteration such as silicification and chloritization, while the deeper portion gradually transitions to high-to-medium temperature alteration such as potassic alteration and skarnization associated with concealed granite porphyry intrusions. This alteration zoning is spatially continuous and gradual, lacking a clear mineralogical boundary, making it difficult to objectively define the transition interface from the shallow silver-lead-zinc mineralization zone to the deep copper mineralization zone. Accurately determining the vertical transition boundary of the alteration zoning within this gradual transition zone, and further locating the copper concentration center in the lower high-to-medium temperature alteration zone, is the core challenge guiding the delineation of deep prospecting target areas in the Shuangjianzishan mining section.

[0003] In existing technologies, alteration zoning studies mainly rely on drilling logging and visual identification, supplemented by empirical boundary delineation on two-dimensional profiles. Different personnel exhibit significant differences in their judgments of the same gradient zone boundary, and a continuous three-dimensional alteration zoning model cannot be formed. Some exploration projects use single geophysical methods such as CSAMT for deep inference, but geophysical anomalies are ambiguous, making it difficult to distinguish the combined properties of different alteration minerals, and even more difficult to establish a direct correlation with the concentration of ore-forming elements. For the Shuangjianzishan mining section, there is currently a lack of a systematic method that can collaboratively analyze quantitative data on alteration minerals and ore-forming element concentrations from borehole cores in three-dimensional space, first solving for alteration zoning transformation boundaries, and then using this to locate the ore-forming center and delineate prospecting target areas. This results in a lack of high-precision spatial vector guidance for the exploration deployment of deep copper ore bodies.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing mineralization alteration zoning of polymetallic deposits and determining prospecting directions, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for constructing mineralization alteration zoning of polymetallic deposits and determining prospecting directions, comprising the following steps: S1: Collect rock samples from the target exploration area, measure the relative abundance of alteration minerals and the content of ore-forming elements in each sample, perform standardization processing, and obtain standardized alteration mineral abundance values ​​and ore-forming element concentration values; at the same time, record the spatial three-dimensional coordinates of each sample. S2: Spatial grid interpolation is performed on the spatial three-dimensional coordinates of the sample, the abundance values ​​of altered minerals, and the concentration values ​​of ore-forming elements to construct a three-dimensional data matrix including an altered mineral assemblage data layer and a mineralization intensity distribution data layer. S3: In the three-dimensional data matrix, several vertical sampling lines are laid out, and the sequence of relative abundance of alteration minerals changing with depth is extracted one by one; for each vertical sampling line, the gradient of change of alteration mineral abundance values ​​between adjacent preset depth windows is calculated, and the depths where the gradient of change reaches an extreme value are marked as candidate boundary points for alteration transformation; three-dimensional surface fitting is performed on the candidate boundary points for alteration transformation on all vertical sampling lines to divide the main transformation boundary surface of the medium-high temperature alteration domain in three-dimensional space; S4: Within the medium-high temperature alteration domain, spatial overlay analysis is performed on the mineralization intensity distribution data layer and the alteration mineral assemblage data layer to delineate the medium-high temperature alteration core area; local high-value anomaly areas of mineralization intensity are extracted from the medium-high temperature alteration core area, and their spatial centroids are used as the medium-high temperature mineralization center. S5: Taking the medium-high temperature mineralization center as the benchmark, extend a preset distance outward into the deep unexplored control area along the maximum inclination direction of the main transformation boundary surface at the medium-high temperature mineralization center, and determine the long axis direction of the target area based on the spatial distribution direction of the medium-high temperature alteration core area, thereby delineating the deep mineral exploration target area and determining the mineral exploration direction.

[0007] Furthermore, the altered minerals include potassium feldspar, silicified quartz, and chlorite; the ore-forming elements include silver, lead, zinc, and copper.

[0008] Furthermore, the three-dimensional data matrix also includes a data layer for the distribution of ore-forming elements; a data layer for alteration mineral assemblages with the relative abundance of potassium feldspar, silicified quartz, and chlorite in each grid cell as a characteristic variable; a data layer for the distribution of ore-forming elements with the concentration values ​​of silver, lead, zinc, and copper in each grid cell as a characteristic variable; and a data layer for the distribution of mineralization intensity with the silver equivalent comprehensive mineralization intensity index as a characteristic variable. The silver equivalent comprehensive mineralization intensity index is determined based on the content of each ore-forming element in the data layer for the distribution of ore-forming elements, by multiplying the silver concentration value by the product of lead, zinc, and copper with their respective preset conversion coefficients. The relative abundance data of altered minerals in the altered mineral composition data layer is derived from the altered mineral abundance value, and the concentration values ​​of each ore-forming element in the ore-forming element distribution data layer are derived from the ore-forming element concentration value.

[0009] Furthermore, the ore resources controlled by each known ore-controlling structure within the target exploration area are statistically analyzed. The ore-controlling structure with the largest ore resource is identified as the main ore-controlling structure of the target exploration area, and its dip direction is measured. The extraction of the sequence of relative abundance of altered minerals with depth specifically includes: In the three-dimensional data matrix, the exploration line profile that passes through the target exploration area with the most boreholes is used as the reference profile. Along the reference profile and parallel to the dip direction of the main ore-controlling structures in the target exploration area, several parallel vertical sampling lines are laid out at preset intervals. Each vertical sampling line extends from the surface to the bottom boundary of the three-dimensional data matrix. The relative abundance values ​​of potassium feldspar, silicified quartz, and chlorite in the grid cells passed by each vertical sampling line are extracted one by one and arranged in ascending order of depth to form a sequence of the relative abundance of alteration minerals corresponding to each vertical sampling line as a function of depth. The bottom boundary of the three-dimensional data matrix refers to the depth of the last grid cell in the depth direction of the three-dimensional data matrix.

[0010] Furthermore, marking the candidate boundary points of the alteration transition specifically includes: On the sequence of relative abundance of altered minerals changing with depth along each vertical sampling line, the sequence is divided into several depth intervals from top to bottom with a preset depth window as compensation. The arithmetic mean of the relative abundance values ​​of the same altered mineral in each grid cell contained in each preset depth window within each depth interval is calculated as the average abundance value of the altered mineral in the preset depth window. The ratio of the difference between the average abundance values ​​of the same altered mineral in two adjacent preset depth windows to the window depth of the preset depth window is calculated as the abundance change gradient of the altered mineral between adjacent depth windows, where the window depth refers to the difference between the top and bottom depths of the preset depth window. When a positive maximum value is detected in the abundance change gradient of potassium feldspar and a negative maximum value is detected in the abundance change gradient of at least one altered mineral in silicified quartz or chlorite at a certain depth, the corresponding depth position is marked as a candidate boundary point for alteration transformation.

[0011] Furthermore, determining the principal transformation boundary surface specifically includes: All candidate boundary points of alteration transformation marked on all vertical sampling lines are projected into the three-dimensional data matrix. The spatial three-dimensional coordinates of each candidate boundary point of alteration transformation are used as known control points. Kriging interpolation is used to fit a three-dimensional surface to generate a continuous surface. The area above the surface is the low-temperature alteration domain, and the area below the surface is the medium-high temperature alteration domain. This surface is the main transformation boundary surface.

[0012] Furthermore, the delineation of the aforementioned medium-high temperature mineralization center specifically includes: Within the grid cell range corresponding to the medium-high temperature alteration domain, the relative abundance value of potassium feldspar of each grid cell is extracted from the alteration mineral assemblage data layer. Grid cells with potassium feldspar relative abundance values ​​higher than a preset threshold are marked as core candidate cells. The area formed by connecting spatially adjacent core candidate cells is delineated as the medium-high temperature alteration core region. Within the spatial range defined by the medium-high temperature alteration core area, the silver equivalent comprehensive mineralization intensity index of each grid cell is extracted from the mineralization intensity distribution data layer. An abnormal lower limit is set based on the average value of the silver equivalent comprehensive mineralization intensity index in the core area. Grid cells with a silver equivalent comprehensive mineralization intensity index higher than the abnormal lower limit are marked as local high-value abnormal areas of mineralization intensity. Calculate the arithmetic mean center of the spatial three-dimensional coordinates of all grid cells within the local high-value anomaly zone of mineralization intensity, and determine the three-dimensional coordinates of the arithmetic mean center as the medium-high temperature mineralization center.

[0013] Furthermore, delineating deep mineral exploration target areas specifically includes: In the three-dimensional data matrix, the maximum tilt direction of the main transformation boundary surface at the location of the medium-high temperature mineralization center is calculated, and the projection direction of the maximum tilt direction on the horizontal plane is determined as the dip direction of the main transformation boundary surface at that location; taking the medium-high temperature mineralization center as the starting point, a preset distance is extrapolated along the dip direction to the unexplored depth control area, and the extrapolation endpoint is taken as the center reference point of the depth prospecting target area; The boundary of the unexplored area at that depth is defined as follows: Horizontal boundary: An envelope formed by buffering outwards a preset control distance from the projection points of all existing boreholes on the horizontal plane; Vertical boundary: A horizontal plane defined by the deepest point in the vertical direction of the final hole depth of all existing boreholes, extending downwards by a predetermined extrapolation depth.

[0014] Furthermore, the projection of the medium-high temperature alteration core area onto the horizontal plane is extracted, the major axis direction of the projection is calculated, the major axis direction is taken as the major axis direction of the deep mineral exploration target area, and the horizontal direction perpendicular to the major axis direction is taken as the minor axis direction of the deep mineral exploration target area. Centered on the central reference point, and with the major and minor axes as the target area distribution directions, a three-dimensional spatial range outside the existing engineering control boundary is delineated as a deep mineral exploration target area. The extension length of this target area along the major axis is greater than the extension length along the minor axis, and the vertical range of the target area is based on the depth of the central reference point, extending upwards and downwards by a predetermined distance, thereby determining the mineral exploration direction.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a gradient coupling discrimination criterion for alteration mineral assemblages, using the depth marking of candidate alteration transformation boundaries based on the simultaneous spatial occurrence of positive maxima of potassium feldspar and negative maxima of chlorite or silicified quartz. Through three-dimensional surface fitting, the main transformation boundary surfaces of the medium-high temperature alteration domain and the low-temperature alteration domain are determined, enabling a quantitative characterization of the spatial structure of the alteration temperature field and significantly improving the objectivity and spatial accuracy of alteration zoning. Simultaneously, a comprehensive mineralization intensity index based on silver equivalent integrates the value equivalents or geological equivalents of the four ore-forming elements—silver, lead, zinc, and copper—into a unified mineralization intensity index. Combined with high-value areas of potassium feldspar abundance cumulative frequency, the core area of ​​medium-high temperature alteration is delineated, and mineralization is extracted within it. The intensity statistical anomaly zone is used as the spatial centroid of the anomaly zone as the medium-high temperature mineralization center, achieving precise positioning from the regional alteration zone to the mineralization enrichment center. Based on this, quantitative extrapolation is carried out to the depth beyond the existing engineering control boundary along the maximum inclination direction of the main transformation boundary surface, using the medium-high temperature mineralization center as the reference point. The main component direction of the spatial distribution of the medium-high temperature alteration core area is used to determine the long axis of the target area. Finally, the deep mineral exploration target area with clear three-dimensional spatial geometric parameters and extension direction is delineated, providing a directly quantifiable spatial basis for drilling engineering deployment, effectively reducing the ambiguity and blindness of deep mineral exploration, thereby guiding exploration engineering, reducing ineffective drilling, and lowering exploration costs. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram illustrating the gradient of alteration mineral abundance with depth in this invention. Detailed Implementation

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

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

[0019] Example: Please see Figures 1 to 2 The present invention provides a technical solution: A method for constructing mineralization alteration zoning of polymetallic deposits and determining prospecting directions, comprising the following steps: S1: Collect rock samples from the target exploration area, measure the relative abundance of alteration minerals and the content of ore-forming elements in each sample, perform standardization processing, and obtain standardized alteration mineral abundance values ​​and ore-forming element concentration values; at the same time, record the spatial three-dimensional coordinates of each sample. In this embodiment, the target exploration area is a prospective mineralization area determined after preliminary geological surveys, geophysical exploration, and geochemical exploration. Its scope generally covers the main alteration zone, known mineralization outcrops, and possible peripheral extension areas.

[0020] When collecting rock samples, a systematic grid sampling method or a profile sampling method is used. Sampling points are laid out at preset intervals along the strike and dip direction of the main ore-controlling structures in the exploration area to obtain spatially uniform sample data, ensuring the accuracy and reliability of subsequent three-dimensional interpolation. The preset sampling interval is determined according to the exploration stage and deposit type of the target exploration area: for the general exploration stage, the sampling interval is generally a planar grid of 50m×50m to 100m×100m; for the detailed exploration stage, the sampling interval is densified to a planar grid of 25m×25m to 50m×50m; for areas with known concentrated mineralization or strong alteration, it can be further densified to 10m×10m to 25m×25m. In the vertical direction, through a combination of surface outcrop sampling and borehole core sampling, the sampling interval is generally 2m to 10m, and densified to 0.5m to 2m in areas of drastic alteration or mineralization. The above sampling interval settings take into account the general engineering grid size of deposit exploration and can balance data continuity and sampling economy.

[0021] The rock samples include surface outcrop rock samples, fresh rock samples exposed in trenches or tunnels, and borehole core samples. Fresh or weakly weathered rocks are preferred for sampling, with each sample weighing no less than 500g to ensure sufficient sample quantity for subsequent alteration mineral identification and ore-forming element analysis. For borehole core samples, they are collected in segments according to lithological sections and alteration characteristics, with each sampling segment generally 0.5m to 2m in length to ensure that subtle vertical changes in alteration can be captured.

[0022] In this embodiment, the alteration minerals include potassium feldspar, silicified quartz, and chlorite. These three alteration minerals constitute a typical hydrothermal alteration mineral assemblage from high to low temperatures: potassium feldspar represents high-temperature potassic alteration, typically forming at temperatures between 300°C and 500°C; silicified quartz represents mid-temperature silicification alteration, typically forming at temperatures between 200°C and 350°C; and chlorite represents low-temperature stratitization or chloritization alteration, typically forming at temperatures between 150°C and 250°C. The reason for selecting these three alteration minerals as characteristic variables is that, in polymetallic hydrothermal deposits, the alteration zoning sequence of potassium feldspar-silicified quartz-chlorite has a good spatial correspondence with the ore-forming temperature field. Potassium feldspar enrichment areas typically indicate the center or high-temperature channel of hydrothermal activity, silicified quartz enrichment areas indicate mid-temperature mineralization enrichment areas, and chlorite enrichment areas indicate the periphery of low-temperature alteration. The spatial variation in the relative abundance of these three minerals can effectively invert the temperature gradient direction and evolution trajectory of the ore-forming fluid.

[0023] In this embodiment, the ore-forming elements include silver (Ag), lead (Pb), zinc (Zn), and copper (Cu). These four ore-forming elements are typical ore-forming element combinations in hydrothermal vein polymetallic deposits. They are often spatially zonal and exhibit an evolutionary trend of copper, zinc, lead, and silver from high temperature to low temperature.

[0024] The relative abundance of alteration minerals refers to the volume percentage or area percentage of each target alteration mineral in the same rock sample, obtained through mineral identification methods. Specific measurement methods include: grinding the collected rock sample into thin sections or optical thin sections, performing rock and mineral identification under a polarizing microscope, or using semi-quantitative X-ray diffraction (XRD), or scanning electron microscopy (SEM) combined with energy dispersive spectroscopy (EDS), or using Fourier transform infrared spectroscopy (FTIR) or shortwave infrared spectroscopy (SWIR) to determine the percentage content of each alteration mineral in the sample. In this embodiment, shortwave infrared spectroscopy is preferably used to measure the abundance of alteration minerals, as this method has the advantages of fast measurement speed, low sample pretreatment requirements, and the ability to identify fine-grained alteration minerals. During measurement, measurements are taken at least at three different locations for each sample, and the arithmetic mean is taken as the relative abundance value of each alteration mineral in that sample to reduce errors caused by sample inhomogeneity. In the measurement results, the relative abundance values ​​of the three alteration minerals, potassium feldspar, silicified quartz and chlorite, were recorded independently, ranging from 0% to 100%, and the sum of the abundance values ​​of the three alteration minerals at the same measurement point did not exceed 100%.

[0025] The measurement of ore-forming element content refers to the determination of the content of four ore-forming elements—silver (Ag), lead (Pb), zinc (Zn), and copper (Cu)—in the same rock sample using geochemical analysis methods. The specific measurement method is as follows: the sample is pulverized to below 200 mesh, digested using a four-acid method (hydrochloric acid-nitric acid-perchloric acid-hydrofluoric acid), and then the ore-forming element content is determined by inductively coupled plasma mass spectrometry (ICP-MS) or inductively coupled plasma atomic emission spectrometry (ICP-AES). For samples with high silver content, the fire assay method can be used for verification. In the measurement results, the unit for silver (Ag) content is grams per ton (g / t), and the units for lead (Pb), zinc (Zn), and copper (Cu) content are mass percentages or grams per ton. To ensure consistency in subsequent data processing, all results are uniformly converted to grams per ton.

[0026] The standardization process refers to mathematically transforming the measured relative abundance values ​​of altered minerals and the concentration values ​​of ore-forming elements to eliminate the influence of differences in dimensions, magnitudes, or data distribution characteristics between different variables on subsequent analysis. The specific methods for standardization are as follows: For the relative abundance values ​​of alteration minerals, the range normalization method is used to linearly map the original abundance values ​​to the [0,1] interval. For each alteration mineral j, the formula for calculating its normalized abundance value in all N samples is as follows: in, This represents the original relative abundance (%) of the j-th altered mineral in the i-th sample; This represents the minimum abundance value of the j-th altered mineral among all N samples; This represents the maximum abundance value of the j-th altered mineral among all N samples; denoted as the normalized abundance value of the j-th alteration mineral in the i-th sample, dimensionless, with a value range of [0,1]; j is the alteration mineral index, i is the sample index, i∈[1,N].

[0027] The reason for using range standardization is that the relative abundance data of alteration minerals usually have a 0% boundary value (i.e., the sample does not contain this alteration mineral). Range standardization can preserve the boundary characteristics of the data, and it is simple to calculate, which facilitates the mathematical processing in subsequent gradient analysis. At the same time, range standardization does not change the data distribution pattern and can preserve the structural characteristics of the original spatial variation of alteration abundance.

[0028] For the concentration values ​​of ore-forming elements, the logarithmic standardization method is used, and the calculation formula is as follows: in, This represents the original concentration (g / t) of the k-th ore-forming element in the i-th sample. It is a small positive constant, taking the value as one-tenth of the minimum detection limit of the ore-forming element in all samples. When the minimum detection limit is 1 g / t, The value is set to 0.1 g / t to avoid the undefined logarithmic operation when the original concentration value is 0; is the standardized concentration value of the k-th ore-forming element in the i-th sample, dimensionless; k is the index of the ore-forming element.

[0029] Logarithmic standardization is used because the distribution of ore-forming elements in geological bodies typically follows a log-normal distribution. However, the original data often exhibits high skewness and a long tail, meaning that a few high-value samples can severely impact spatial interpolation results. Logarithmic standardization effectively compresses high values ​​and stretches low values, making the data closer to a symmetrical distribution and improving the accuracy and stability of subsequent spatial interpolation. (Constant) The purpose of introducing zero is to mathematically justify the handling of zero values. Its value is set to one-tenth of the minimum detection limit of the element. This value is small enough that it will not significantly change the data distribution characteristics, while ensuring the feasibility of the operation.

[0030] The recording of the three-dimensional spatial coordinates refers to recording the spatial location information of each sample. For surface samples, the planar coordinates (X, Y) of the sample are measured using real-time dynamic differential GPS or a handheld GPS device, and the elevation (Z) of the sampling point is recorded. For underground samples (such as borehole core samples), the three-dimensional spatial coordinates of each sample are calculated based on the borehole opening coordinates (X, Y, Z), borehole azimuth, dip angle, and sampling depth, using borehole inclination data and borehole opening coordinates. All coordinates are uniformly based on the National Geodetic Coordinate System (such as CGCS2000) and the elevation datum (such as the 1985 National Elevation Datum) to ensure the uniformity and superposition of spatial data.

[0031] After standardization, the standardized alteration mineral abundance values ​​(including standardized potassium feldspar abundance values) for each sample were obtained. Standardized silica quartz abundance value Standardized chlorite abundance value ) and standardized mineralization element concentration values ​​(including standardized silver concentration values) Standardized lead concentration value Standardized zinc concentration value Standardized copper concentration value These standardized values ​​and the spatial three-dimensional coordinates of the sample. The data records that form a one-to-one correspondence serve as the basic input data for spatial grid interpolation in the subsequent step S2; among which... These represent the X, Y, and Z coordinates of the i-th sample, respectively.

[0032] S2: Spatial grid interpolation is performed on the spatial three-dimensional coordinates of the sample, the abundance values ​​of altered minerals, and the concentration values ​​of ore-forming elements to construct a three-dimensional data matrix including an altered mineral assemblage data layer and a mineralization intensity distribution data layer.

[0033] The three-dimensional data matrix refers to a digital model composed of several rectangular grid cells of equal size arranged regularly in three-dimensional space. Each grid cell is uniquely identified by its index number (a, b, c) in the three dimensions of X (east), Y (north), and Z (vertical), and stores various attribute values ​​corresponding to that spatial location. Here, a, b, and c are used to define the spatial coordinate integer numbers of the grid cells in the a-th row, b-th column, and c-th layer of three-dimensional space, respectively. The dimensions of the grid cells in the X, Y, and Z directions (i.e., the grid spacing) determine the spatial resolution of the three-dimensional data matrix.

[0034] In this embodiment, the grid cell size of the three-dimensional data matrix is ​​determined according to the following principles: grid cell size in the X and Y directions. Set to 1 / 4 to 1 / 2 of the grid spacing for the target exploration area; where This indicates the length of the mesh cell in the eastward direction (X-axis direction). This indicates the size and length of the grid cell in the north direction (Y-axis direction). For example, when the grid size of the target exploration area is 100m × 100m, and A grid spacing of 25m to 50m is acceptable, with 25m being preferred. This value is based on the sampling theorem, which states that the interpolation grid spacing should not exceed half the average spacing of the sampling points to effectively preserve the spatial variation characteristics of the original data. However, excessively large grid spacing leads to insufficient spatial resolution, loss of local details in alteration and mineralization, and negatively impacts the accuracy of subsequent gradient analysis. Conversely, excessively small grid spacing significantly increases computational complexity and can produce spurious interpolation details in areas lacking sufficient sample points for control. A grid spacing of 1 / 4 to 1 / 2 of the exploration grid spacing strikes a balance between interpolation accuracy and computational efficiency.

[0035] Z-direction (vertical) mesh cell size ( The sampling interval is determined based on the vertical sampling distance, and is generally set to 1 / 2 to 1 times the average vertical sampling interval. For example, when the borehole core sampling interval is 2m to 5m, The length can range from 1m to 5m, with 2m being the preferred length. The setting needs to consider the vertical sampling density and the degree of vertical change in alteration zoning: in the near-ore area where alteration zoning changes drastically, the vertical gradient is large, and a smaller gradient should be selected. To capture detailed vertical changes; in the more distant ore-bearing areas where alteration zoning is relatively stable, the size can be appropriately increased. To reduce the amount of computation. and , Different values ​​can be taken, generally It should be less than or equal to and This is because the vertical gradient of alteration zones in hydrothermal deposits is usually greater than that in the horizontal direction.

[0036] The specific algorithm used for spatial grid interpolation is ordinary kriging interpolation. Kriging interpolation is an optimal linear unbiased estimation method based on regional variable theory. Its core idea is that when performing unbiased optimal estimation of the values ​​of regional variables within a finite region, it not only considers the distance relationship between the estimated point and known sampling points, but also quantitatively describes the spatial autocorrelation structure of the regional variables (including range, nugget value, and sill value) through a variogram model, thereby assigning reasonable weight coefficients to different known points. Compared with purely geometric interpolation methods such as inverse distance weighting, kriging interpolation can better handle situations where geological variables have differences in spatial continuity in different directions (i.e., anisotropy), and can provide interpolation variance to assess the uncertainty of the interpolation results. This is particularly suitable for geological variables with obvious structural spatial variations, such as the abundance of alteration minerals and the concentration of ore-forming elements in hydrothermal deposits.

[0037] Before performing kriging interpolation, each attribute variable to be interpolated (normalized alteration mineral abundance value) is... , , Standardized mineralization element concentration values , , , ), and perform variogram analysis separately, specifically including: Calculate the variability function of anisotropic experiments: Calculate the experimental variability function values ​​along multiple directions in the horizontal plane (usually four directions are taken: 0°, 45°, 90°, and 135°, with due north as 0° and calculated clockwise) and the vertical direction respectively; Determine the main range direction: The direction with the smallest distance when the variogram value first reaches the sill value is determined as the main range direction (i.e. the direction with the best spatial continuity). This direction is usually consistent with the strike of the main ore-controlling structure or the overall extension direction of the alteration zone. Fitting the theoretical variogram model: The experimental variogram in each direction is fitted using a spherical model, exponential model, or Gaussian model to determine the three parameters: range, nugget value, and sill value. Based on the fitted theoretical variogram model parameters, ordinary kriging interpolation is performed to interpolate the values ​​of each attribute variable into each grid cell of the three-dimensional data matrix.

[0038] After spatial interpolation of all attribute variables, the resulting three-dimensional data matrix includes the following data layers: Alteration mineral assemblage data layer: contains normalized potassium feldspar abundance values ​​in each grid cell ( ), Standardized silica quartz abundance value ( ), Standardized chlorite abundance value ( Three characteristic variables; Ore-forming element distribution data layer: includes standardized silver concentration values ​​in each grid cell ( Standardized lead concentration value ( Standardized zinc concentration value ( Standardized copper concentration value ( Four characteristic variables; Mineralization intensity distribution data layer: contains the comprehensive mineralization intensity index in silver equivalent for each grid cell ( () as a characteristic variable.

[0039] In this embodiment, the three-dimensional data matrix further includes a data layer on the distribution of ore-forming elements; a data layer on the assemblage of altered minerals uses the relative abundance of potassium feldspar, silicified quartz, and chlorite in each grid cell as a characteristic variable; a data layer on the distribution of ore-forming elements uses the concentration values ​​of silver, lead, zinc, and copper in each grid cell as a characteristic variable; and a data layer on the distribution of mineralization intensity uses the silver equivalent comprehensive mineralization intensity index as a characteristic variable.

[0040] It should be noted that the relative abundance data of alteration minerals in the above-mentioned alteration mineral assemblage data layer are derived from the normalized alteration mineral abundance values ​​obtained in step S1 (i.e., , , The concentration values ​​of each ore-forming element in the ore-forming element distribution data layer are derived from the standardized ore-forming element concentration values ​​obtained in step S1 (i.e., , , , The silver equivalent comprehensive mineralization intensity index in the mineralization intensity distribution data layer needs to be calculated after the ore-forming element distribution data layer has been constructed.

[0041] The silver equivalent comprehensive mineralization intensity index (denoted as) The concentration of ore-forming elements (ORE) is determined by summing the standardized ORE concentration values ​​of each grid cell in the ORE distribution data layer, based on the product of the silver concentration value and the corresponding preset conversion coefficients for lead, zinc, and copper. Specifically, for a three-dimensional data matrix with coordinates... For any grid cell, its silver equivalent comprehensive mineralization intensity index The calculation process consists of two steps: The first step, for the i-th sample, is to calculate its comprehensive silver equivalent grade based on its original ore-forming element content and value equivalent. The calculation formula is as follows: in, These are the standardized concentration values ​​of silver, lead, zinc, and copper in the grid cell, respectively. These are the value equivalent conversion coefficients for lead, zinc, and copper, respectively, and are dimensionless.

[0042] The value equivalent conversion coefficient is determined based on the economic value ratio of each ore-forming element under current market conditions; with silver as the benchmark element, a preset conversion coefficient is established. The calculation formula is: in, , Let the market unit price be the kth ore-forming element and silver; , G is the conversion factor for the grade measurement unit of the kth ore-forming element and silver to ensure that the units of the numerator and denominator are consistent (usually all grades are expressed in grams per ton, so the value of G is 1); here silver is used as the base element, so k∈{Pb, Zn, Cu}.

[0043] For example, when the market reference prices are: silver (Ag) 5 yuan / gram, lead (Pb) 15,000 yuan / ton, zinc (Zn) 22,000 yuan / ton, and copper (Cu) 65,000 yuan / ton, since silver prices are usually expressed in yuan / gram, while lead, zinc, and copper prices are expressed in yuan / ton, to unify the units of measurement, the silver price needs to be converted to yuan / ton: 5 yuan / gram × Grams per ton = 5,000,000 yuan per ton. At this point, all elements are graded in grams per ton, so the conversion factor for each grade measurement unit is 1. The conversion coefficients are calculated according to the formula as follows: Lead value equivalent conversion coefficient. Zinc value equivalent conversion factor Copper value equivalent conversion factor .

[0044] As calculated above, under current market conditions, 1 gram / ton of lead grade is economically equivalent to 0.003 grams / ton of silver grade, and so on. By multiplying the original lead, zinc, and copper grades of a sample by the aforementioned coefficients, they can be uniformly converted into original silver equivalent grades, which have a clear unit of grams per ton and direct economic meaning. Furthermore, the preset conversion coefficient can also be a geological equivalent conversion coefficient based on the indicative significance of each ore-forming element for mineralization, such as based on the position of each element in the primary halo zoning sequence, differences in elemental activity, or the average enrichment multiple ratio of each element in a known ore body. The value of the geological equivalent conversion coefficient is usually obtained through statistical analysis of existing exploration data in the target exploration area, using the proportional relationship that best reflects the mineralization enrichment characteristics of the area.

[0045] The advantages of using the silver equivalent comprehensive mineralization intensity index as the characteristic variable of the mineralization intensity distribution data layer are: it integrates the mineralization information of multiple ore-forming elements into a comprehensive index, which can fully reflect the superposition and enrichment degree of polymetallic mineralization and avoid the defect that single element index may miss mineralization information; at the same time, it uses silver as the standard to unify the dimensions, which facilitates the comparison of mineralization intensity between different regions and different deposits.

[0046] S3: In the three-dimensional data matrix, several vertical sampling lines are set up, and the sequence of relative abundance of alteration minerals changing with depth is extracted one by one; for each vertical sampling line, the gradient of change of alteration mineral abundance values ​​between adjacent preset depth windows is calculated, and the depths where the gradient of change reaches an extreme value are marked as candidate boundary points for alteration transformation; three-dimensional surface fitting is performed on all candidate boundary points for alteration transformation on the vertical sampling lines to divide the main transformation boundary surface of the medium-high temperature alteration domain in three-dimensional space.

[0047] In this embodiment, the ore resources controlled by each known ore-controlling structure within the target exploration area are statistically analyzed. The ore-controlling structure with the largest ore resource is identified as the main ore-controlling structure of the target exploration area, and its dip direction is measured. The specific operation is as follows: Summarize the resource reserve estimation data of all known ore bodies in the target exploration area, classify and statistically analyze them according to the ore-controlling structures, and calculate the total resource quantity (in terms of ore quantity or metal quantity) of the ore body controlled by each ore-controlling structure; sort each ore-controlling structure from largest to smallest resource quantity, and determine the ore-controlling structure with the largest resource quantity as the main ore-controlling structure of the target exploration area; measure the occurrence elements of the main ore-controlling structure based on geological mapping or structural survey results, and extract its dip direction (i.e., the direction pointed to by the projection of the structural surface along the dip line onto the horizontal plane, expressed as an azimuth angle, such as NE45°).

[0048] The purpose of determining the main ore-controlling structures and their dip direction is to provide a control benchmark consistent with the spatial distribution direction of mineralization for the subsequent layout of vertical sampling lines, so that the vertical variation law of alteration zoning can be fully revealed along the main direction of ore-forming fluid migration.

[0049] Extracting the sequence of relative abundance of altered minerals as a function of depth specifically includes: In the three-dimensional data matrix, the exploration line profile with the most boreholes passing through the target exploration area is used as the benchmark profile. This exploration line profile is a vertical profile perpendicular to the strike of the main ore-controlling structure, and its selection criterion is that the largest number of boreholes are distributed on this profile, thus ensuring the richest geological information along this profile direction. When multiple exploration lines with the same number of boreholes exist, the exploration line profile located in the middle of the exploration area, with uniform borehole distribution and the largest controlled depth is preferentially selected as the benchmark profile.

[0050] Along the reference profile and parallel to the dip direction of the main ore-controlling structures in the target exploration area, several parallel vertical sampling lines are laid out at preset intervals. The preset interval is the horizontal distance between two adjacent vertical sampling lines, and is taken as 1 / 2 to 1 / 4 of the exploration grid spacing of the target exploration area. For example, when the exploration grid is 100m × 100m, the preset interval can be 25m to 50m, preferably 25m. This interval value ensures sufficient spatial density for detecting alteration transition boundaries on the reference profile, while avoiding the introduction of excessive random fluctuation noise due to overly dense vertical sampling lines. Each vertical sampling line extends from the surface into the depth to the bottom boundary of the three-dimensional data matrix; the bottom boundary of the three-dimensional data matrix refers to the depth of the last grid cell in the depth direction of the three-dimensional data matrix.

[0051] The relative abundance values ​​of potassium feldspar, silicified quartz, and chlorite in each grid cell traversed by the vertical sampling line are extracted one by one (i.e., the normalized alteration mineral abundance values ​​stored in the alteration mineral assemblage data layer in step S2). The samples are arranged in ascending order of depth (i.e., Z-value from largest to smallest or elevation from highest to lowest) to form a sequence of the relative abundance of alteration minerals corresponding to each vertical sampling line as a function of depth. This sequence is a one-dimensional array, and each element of the array contains the depth value (Z coordinate) and the abundance values ​​of the three alteration minerals at that depth.

[0052] In this embodiment, marking the candidate boundary points of the alteration transition specifically includes: A preset depth window is set; the preset depth window refers to a calculation interval with a fixed vertical length selected on each vertical sampling line. The window depth (denoted as W) of the preset depth window is determined based on the alteration zoning scale and vertical data resolution of the target exploration area. Generally, W is taken as the vertical grid cell size. The depth is 3 to 10 times greater than that of the average abundance. In this embodiment, W can be 10m to 30m, preferably 20m. If the window depth is too small, the number of grid cells contained in the window will be insufficient, the statistical representativeness of the average abundance value will be poor, and the abundance change gradient will be easily disturbed by local random fluctuations, generating a large number of false extreme points; if the window depth is too large, it will smooth out the alteration change information over a wider depth range, reduce the spatial resolution of the alteration transition boundary, and cause the true geological boundary to be missed. It is 3 to 10 times that of other methods, and can achieve a reasonable balance between statistical stability and spatial resolution.

[0053] On the sequence of relative abundance of altered minerals varying with depth along each vertical sampling line, the sequence is divided into several depth intervals from top to bottom, using a preset depth window as compensation. Specifically, a sliding window method can be used: the top of the first window is located at the shallowest depth of the vertical sampling line (i.e., the surface or the top boundary of the 3D data matrix), and the window is extended downwards by a depth W to form the first preset depth window; the window is then slid downwards along the depth direction by one step (the step size is usually equal to the vertical grid size). This process forms a second preset depth window; and so on, until the bottom of the window reaches or exceeds the deepest point of the vertical sampling line. Thus, each preset depth window corresponds to several consecutive grid cells within a depth range.

[0054] Calculate the arithmetic mean of the relative abundance values ​​of the same alteration mineral within each depth interval across all grid cells contained in each preset depth window, and use this as the average abundance value of the alteration mineral within that preset depth window. For the m-th preset depth window, the average abundance value of potassium feldspar is denoted as... The average abundance value of silica quartz is denoted as The average abundance value of chlorite is denoted as .

[0055] The ratio of the difference in the average abundance value of the same alteration mineral within two adjacent preset depth windows to the window depth of the preset depth window is calculated sequentially, and this ratio is used as the abundance variation gradient of the alteration mineral between the adjacent depth windows. The formula for calculating the abundance variation gradient between the m-th window and the (m+1)-th window is as follows: Where W is the window depth of the preset depth window, in meters (m). , , These represent the abundance gradients of potassium feldspar, silicified quartz, and chlorite between the m-th and m+1-th depth windows, in units of... ; These represent the average abundance values ​​of potassium feldspar, silicified quartz, and chlorite within the (m+1)th preset depth window, respectively. The sign of the abundance gradient has a clear geological meaning: a positive gradient indicates that the average abundance value of the altered mineral increases with depth (increasing downwards), while a negative gradient indicates that the average abundance value of the altered mineral decreases with depth (decreasing downwards). The midpoint depth between two adjacent windows (i.e., the average depth of the bottom surface of the m-th window and the top surface depth of the (m+1)-th window) is taken as the corresponding depth position of this abundance gradient.

[0056] On each vertical sampling line, extreme value detection is performed on the abundance variation gradient sequence to identify candidate boundary points for alteration transition; the detection criteria are as follows: At a certain depth, a location is marked as a candidate boundary point for alteration transformation when both of the following conditions are met: Condition 1: The gradient of potassium feldspar abundance changes reaches a positive maximum (i.e., the rate of increase in potassium feldspar abundance downwards reaches its maximum). Condition 2: When the abundance gradient of at least one alteration mineral in the silicified quartz abundance gradient or chlorite abundance gradient reaches a negative maximum (i.e., the rate of decrease in the abundance of silicified quartz or chlorite downwards reaches its maximum).

[0057] The above-mentioned positive maxima are defined as follows: the potassium feldspar abundance gradient at that depth is greater than the potassium feldspar abundance gradient at each of its two adjacent gradient sampling points (i.e., the gradient values ​​of the adjacent points above and below the depth), and is a positive value; the negative maxima are defined as follows: the silicified quartz abundance gradient or chlorite abundance gradient at that depth is less than the corresponding gradient values ​​of each of its two adjacent gradient sampling points, and is a negative value.

[0058] The geological significance of this discrimination criterion lies in the fact that during the transition from low-temperature alteration zones to medium- and high-temperature alteration zones, the typical change in alteration mineral assemblage is characterized by a relative decrease in the abundance of chlorite (low temperature) and an increase in the relative abundance of potassium feldspar (high temperature). In actual alteration sequences, silicified quartz, as a medium-temperature alteration mineral, may exhibit a trend of first increasing and then decreasing in abundance within the range of decreasing chlorite and increasing potassium feldspar, or it may decrease directly at the transition zone. Therefore, this discrimination criterion simultaneously captures the coupling position between the rapid increase in potassium feldspar (positive maximum) and the rapid decrease in low-temperature / medium-temperature alteration minerals (negative maximum), which, compared to relying solely on the abundance change of a single mineral, can more accurately locate the transition boundary of alteration zones.

[0059] When the absolute value of the difference between the positive maximum depth and the negative maximum depth detected under conditions one and two is less than a preset depth threshold, the midpoint of the two depths is taken as a candidate boundary point for alteration transformation. The preset depth threshold is 1 to 2 times the window depth W of the preset depth window. For example, when W is 20m, the preset depth threshold can be between 20m and 40m, preferably 20m. In actual geological processes, the transformation rates of different alteration minerals may have slight differences. The depth at which potassium feldspar rapidly increases and the depth at which chlorite (or silicified quartz) rapidly decreases may not completely coincide, but within the same alteration transformation zone, they should be spatially adjacent. By setting a depth threshold that matches the window depth W, reasonable depth deviations in the gradient extremes of different minerals are allowed, effectively merging two adjacent depth markers belonging to the same alteration transformation event and avoiding repeatedly marking the same transformation zone as multiple candidate boundary points. If the depth difference between the two exceeds the preset depth threshold, they are considered to indicate different alteration transformation events and are retained as their own independent candidate boundary points.

[0060] In this embodiment, taking a representative vertical sampling line within the target exploration area as an example, the abundance gradient data of three alteration minerals—potassium feldspar, silicified quartz, and chlorite—are extracted from the surface (0m) to the depth (270m). This vertical sampling line traverses a typical hydrothermal alteration zonation sequence, gradually transitioning from a shallow, low-temperature chloritization alteration domain to a deep, medium-to-high-temperature potassium feldspar-silicification alteration domain. The abundance gradient values ​​corresponding to each depth are shown in the table below, where a positive gradient value indicates that the average abundance of the alteration mineral increases with depth, and a negative value indicates that it decreases with depth, as detailed below: Table 1. Examples of alteration mineral abundance gradients with depth: Combining Table 1 above and Figure 2 As shown, in the shallow low-temperature alteration zone (approximately 0 m to 90 m depth), the absolute values ​​of the abundance gradients of the three alteration minerals are all small, and the curves fluctuate smoothly close to the zero axis, indicating that the alteration mineral assemblage in this section is relatively stable and no significant mineral phase transition has occurred. With increasing depth to the transition zone of approximately 110 m to 190 m, the three gradient curves begin to show significant changes: the potassium feldspar abundance gradient changes from a gentle slope to a rapid increase, reaching a positive maximum (+0.033) at a depth of 150 m. This indicates that the rate of increase in potassium feldspar abundance reaches its maximum at this depth; simultaneously, the gradient of chlorite abundance variation also exhibits a negative maximum (-0.052). The data in Table 1 indicate that the rate of decrease in chlorite abundance also reaches its peak. This phenomenon, where the positive and negative maxima are spatially zero in depth and completely coupled, precisely indicates the boundary between low-temperature chloritization and medium-to-high-temperature potassium feldspar alteration. Beyond this boundary (beyond 190 m), the gradient values ​​rapidly decline and flatten, signifying the entry into the stable region of the medium-to-high-temperature alteration domain. Figure 2 This demonstrates that the coupled discrimination criterion of positive maxima of potassium feldspar and negative maxima of chlorite / silicified quartz, adopted in this embodiment, can effectively overcome the deficiency of single mineral abundance threshold judgment being susceptible to local fluctuation interference. By capturing the gradient extreme value positions of the synergistic changes in alteration mineral assemblages, it achieves high sensitivity and high spatial resolution identification of alteration zonation transition boundaries. This gradient extreme value detection method based on the synergistic changes in mineral assemblages has stronger noise resistance and clearer geological indication significance than methods relying solely on changes in the absolute value of single mineral abundance.

[0061] In this embodiment, determining the principal transformation boundary surface specifically includes: All alteration transition candidate boundary points marked on all vertical sampling lines are projected into the three-dimensional data matrix. The unique position of each alteration transition candidate boundary point in three-dimensional space is determined by the planar coordinates (X,Y) of its vertical sampling line and the marking depth (Z).

[0062] Using the spatial 3D coordinates of each alteration transformation candidate boundary point as known control points, a 3D surface is fitted using Kriging interpolation to generate a continuous surface. The Kriging interpolation method used here is consistent with the ordinary Kriging interpolation method used in step S2; by analyzing the variogram structure of the control points in space, the optimal linear unbiased estimation of the position to be estimated is performed. The interpolated surface grid spacing is compared with the horizontal grid spacing of the 3D data matrix. Maintain consistency.

[0063] This surface divides the three-dimensional data matrix into two spatial domains: the area above the surface (i.e., the shallower, closer to the surface) is defined as the low-temperature alteration domain, and the area below the surface (i.e., the deeper, closer to the interior) is defined as the medium-high temperature alteration domain. The low-temperature alteration domain refers to the alteration space characterized by a relatively low-temperature environment and dominated by low-temperature alteration mineral assemblages such as chlorite during ore-forming hydrothermal activity; the medium-high temperature alteration domain refers to the alteration space characterized by a relatively medium-high temperature environment and dominated by medium-high temperature alteration mineral assemblages such as potassium feldspar and silicified quartz during ore-forming hydrothermal activity.

[0064] The principal transformation boundary surface is a spatial approximation of a gradually changing transition zone. In actual geological conditions, the boundary between the medium- and high-temperature alteration domains and the low-temperature alteration domains is not a strict mathematical boundary, but rather a transition zone with a certain width. This embodiment quantitatively expresses the spatial distribution of the transition zone in the form of a continuous surface by performing surface fitting in three-dimensional space, enabling the spatial structure of alteration zoning to be numerically described and utilized for subsequent analysis.

[0065] S4: Within the medium-high temperature alteration domain, spatial overlay analysis is performed on the mineralization intensity distribution data layer and the alteration mineral assemblage data layer to delineate the medium-high temperature alteration core area; local high-value anomaly areas of mineralization intensity are extracted from the medium-high temperature alteration core area, and their spatial centroids are used as the medium-high temperature mineralization center.

[0066] In this embodiment, delineating the medium-high temperature mineralization center specifically includes: Extract the range of grid cells corresponding to the medium-high temperature alteration domain; this range is defined by the main transformation boundary surface determined in step S3: in the three-dimensional solid data matrix, all grid cells located below the main transformation boundary surface (i.e., the side with greater depth) are marked as medium-high temperature alteration domain grid cells, forming a spatial subset of the medium-high temperature alteration domain.

[0067] From the alteration mineral assemblage data layer constructed in step S2, the relative abundance values ​​of potassium feldspar in each grid cell within the spatial subset of the medium-high temperature alteration domain are extracted. These values ​​are derived from the alteration mineral abundance values ​​after standardization in step S1 and are distributed in each grid cell after spatial grid interpolation in step S2.

[0068] Grid cells with a relative abundance of potassium feldspar higher than a preset threshold are marked as core candidate cells; the preset threshold is denoted as... This is the lower limit of potassium feldspar abundance for classifying a grid cell as belonging to the mid-to-high temperature alteration core region. The method for determining the abundance is as follows: The cumulative frequency distribution of the relative abundance values ​​of potassium feldspar in all grid cells within the high-temperature alteration domain is statistically analyzed, and the potassium feldspar abundance value corresponding to a certain preset percentile of the cumulative frequency is taken as... In this embodiment, the preset percentile is the 85th percentile (P85). In the statistical analysis of geochemistry and alteration mineral abundance, a cumulative frequency of 85% or higher is generally considered a strong anomaly or high-value area. The value of the 85th percentile can both screen out the strongly altered areas with the most developed potassium feldspar alteration and avoid the following: a threshold that is too high would result in an overly small core area, missing important information; a threshold that is too low would result in an overly large core area, losing its focusing significance. In addition, this preset percentile can also be adjusted according to the degree of alteration development in the target exploration area, and the value range can be between the 80th and 90th percentiles.

[0069] The region formed by connecting spatially adjacent core candidate units is defined as the medium-high temperature alteration core region. Spatially adjacent means that two core candidate units are considered connected and belong to the same connected region when they share a face, an edge, or a vertex in three-dimensional space (i.e., using the 26-neighborhood connectivity criterion). Using the 26-neighborhood connectivity criterion ensures the complete identification of the morphologically continuous core region in three-dimensional space, avoiding fragmentation due to mesh units only being connected by edges or vertices. All core candidate units with connectivity are merged into one or more connected regions, each of which is a medium-high temperature alteration core region. When multiple unconnected medium-high temperature alteration core regions exist, the one with the largest volume or the highest average abundance of potassium feldspar is selected as the primary target region for subsequent analysis.

[0070] After delineating the core area of ​​medium- and high-temperature alteration, local high-value anomalies in mineralization intensity are further extracted within the spatial range defined by this core area. The specific steps are as follows: From the mineralization intensity distribution data layer constructed in step S2, the silver equivalent comprehensive mineralization intensity index of each grid cell within the spatial range defined by the medium-high temperature alteration core area is extracted. An anomaly lower limit is set based on the average value of the silver equivalent comprehensive mineralization intensity index within the core area, and this anomaly lower limit is denoted as... , specifically, The method for determining the value is as follows: calculate the arithmetic mean of the comprehensive mineralization intensity index of silver equivalent of all grid cells within the high-temperature alteration core region (denoted as ). ) and standard deviation (denoted as ), the abnormal lower limit Set as the mean plus 1.5 times the standard deviation, i.e. .

[0071] In geochemical anomaly identification, the mean plus 1.5 (or 2) standard deviations is a commonly used method for determining the lower limit of statistical anomalies. When the data approximately follows a normal distribution, the probability of areas above the mean plus 1.5 standard deviations is about 6.7%, which can effectively screen out statistically significant high values. The reason for choosing 1.5 rather than 2 standard deviations is that the local high-value anomaly areas of mineralization intensity are located in the core area of ​​medium- and high-temperature alteration, and their background values ​​are already at a high level. If 2 standard deviations are used, it may lead to an excessively high threshold and overly fragmented anomaly areas, which is not conducive to the stability of subsequent spatial centroid calculations. 1.5 standard deviations can achieve a balance between highlighting the local enrichment center of mineralization and maintaining the spatial continuity of the anomaly area.

[0072] Grid cells with a silver equivalent comprehensive mineralization intensity index higher than the lower limit of the anomaly are marked as local high-value anomaly areas of mineralization intensity; similarly, spatial connectivity analysis can be used to connect adjacent high-value grid cells to form continuous high-value anomaly areas.

[0073] After delineating the locally high mineralization intensity anomaly zone, the spatial centroid of this anomaly zone is calculated to determine the medium-to-high temperature mineralization center; specifically: Calculate the arithmetic mean center of the spatial three-dimensional coordinates of all grid cells within the local high-value anomaly zone of mineralization intensity. Assume... A grid cell is marked as a local high-value anomaly in mineralization intensity, and the three-dimensional coordinates of the q-th grid cell are: Then the spatial barycenter coordinates of the anomaly region The calculation formula is: in, The X, Y, and Z coordinates represent the spatial centroid of the local high-value anomaly zone of mineralization intensity, respectively, and r represents the spatial centroid. Let X, Y, and Z represent the X, Y, and Z coordinates of the q-th grid within the local high-value anomaly zone of mineralization intensity, respectively; q is the index of the grid cell labeled as the local high-value anomaly zone of mineralization intensity, q∈[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ].

[0074] The three-dimensional coordinates of the arithmetic mean center It was determined to be the medium-high temperature mineralization center; the medium-high temperature mineralization center represents the spatial center of gravity of the core part with the most developed potassium feldspar alteration and the highest mineralization intensity in the medium-high temperature alteration domain, and is the optimal three-dimensional estimate of the location of the enrichment center of the multi-stage superposition of mineralization hydrothermal fluids.

[0075] In the specific methods provided above, the grid cells within the local high-value anomaly zone of mineralization intensity all satisfy... The selection criteria, each representing the most mineralized area, are weighted equally to avoid excessive influence of locally high mineralization intensity on the central location, thus making the medium-to-high temperature mineralization center more spatially representative. Alternatively, the selection criteria for each grid unit can also be used... Using the values ​​as weights, a weighted average method is used to calculate the spatial centroid to obtain the location of the mineralization center that is more biased towards the peak of mineralization intensity.

[0076] S5: Taking the medium-high temperature mineralization center as the benchmark, extend a preset distance outward into the deep unexplored control area along the maximum inclination direction of the main transformation boundary surface at the medium-high temperature mineralization center, and determine the long axis direction of the target area based on the spatial distribution direction of the medium-high temperature alteration core area, thereby delineating the deep mineral exploration target area and determining the mineral exploration direction.

[0077] In this embodiment, delineating the depth-based mineral exploration target area specifically includes: In the three-dimensional data matrix, the maximum tilt direction of the principal transformation boundary surface at the location of the medium-high temperature mineralization center is calculated. The principal transformation boundary surface is generated by fitting using the Kriging interpolation method in step S3. The normal vector and tilt direction of this surface can be calculated at each point in three-dimensional space. Specifically, for the three-dimensional coordinate point where the medium-high temperature mineralization center is located... The maximum tilt direction at a point on the surface (i.e., the opposite direction of the projection of the gradient direction onto the tangent plane of the surface, pointing in the direction of the steepest descent of the surface) is calculated using the partial derivative at that point. The formula is as follows: Let the principal transformation boundary surface be defined by the equation Implicitly, this surface is located in the mid-to-high temperature mineralization center. The gradient vector at is The partial derivatives and It is obtained through derivative estimation provided by numerical differentiation of the surface or Kriging interpolation model; where This represents the elevation value of the principal transformation boundary surface. It is the calculated output of function F at the plane coordinates (X,Y), representing the elevation of the main transformation interface at that point.

[0078] The gradient vector The projection vector on the horizontal plane is The direction of this projection vector is the projection of the surface's maximum tilt direction at that point onto the horizontal plane, which is also the tilt direction of the principal transformation boundary surface at that location. This tilt direction is represented by an azimuth angle, denoted as . The calculation formula is: Arctan2 is the four-quadrant arctangent function, which returns the angle value relative to true north (or the positive X-axis), ranging from 0° to 360°.

[0079] The projection direction of the maximum tilt direction onto the horizontal plane is determined as the dip direction of the principal transformation boundary surface at that location. The geological significance of this direction is that it indicates the direction of maximum horizontal variation of the alteration temperature field near the ore-forming center, which is usually consistent with the lateral migration direction of the ore-forming fluid or the gradient direction of the alteration zoning.

[0080] Starting from the aforementioned medium-high temperature mineralization center, a predetermined distance is extrapolated outward along the dip direction towards the unexplored depth control area. This extrapolated endpoint is taken as the center reference point of the depth prospecting target area. The predetermined distance is... The method for determining its value is as follows: The value is taken as the median of empirically statistically significant lengths of known ore bodies extending along the dip direction in the target exploration area, or as 1 to 3 times the grid spacing of the target exploration area. In this embodiment... Take twice the grid spacing. For example, when the grid spacing is 100m × 100m, A value of 200m is used. An excessively large extrapolation distance will exceed the reasonable extension range of the ore-forming system, significantly reducing the reliability of target area prediction; an excessively small extrapolation distance may only cover the edge of the existing engineering control area, failing to effectively perform the function of deep prediction. Using twice the exploration grid density as the extrapolation distance ensures that the target area extends beyond the existing engineering control area (i.e., the true unexplored control area) without excessive extrapolation that deviates from the reasonable spatial scale of the ore-forming system. Furthermore, if the target exploration area already has significant deep engineering control, and the known ore body has a large extension along the dip direction (e.g., exceeding 300m), this method is suitable. It can be appropriately increased to 300m to 500m, but not exceeding 1.5 times the maximum extension length of the known ore body along the dip.

[0081] The boundary of the unexplored area at that depth is defined as follows: Horizontal boundary: An envelope formed by buffering a predetermined control distance outwards from the projection points of all existing boreholes on the horizontal plane. This predetermined control distance is half the grid spacing of the target exploration area. For example, when the grid spacing is 100m × 100m, the predetermined control distance is 50m. In conventional mineral exploration, the area centered on the borehole and with a radius equal to half the grid spacing is usually considered the effective control range of that borehole. This boundary definition means that any location in the horizontal direction more than half the grid spacing from the nearest existing borehole is considered an unexplored control area.

[0082] Vertical boundary: A horizontal plane formed by the final depths of all existing boreholes (i.e., the lowest vertical limit of the envelope of the final depths of each borehole), extending downwards by a predetermined extrapolation depth. The area below this plane is the unexplored control area in the vertical direction. The predetermined extrapolation depth is a mid-section height or the spacing between exploration line profiles. In this embodiment, the predetermined extrapolation depth is taken as a mid-section height, generally 30m to 60m, preferably 50m. A mid-section height is the conventional engineering spacing for underground mining and also a reasonable inference depth step size in deep mineral exploration.

[0083] In this embodiment, the major axis and minor axis directions of the deep mineral exploration target area are determined by the following steps: Extract the projection of the medium-high temperature alteration core region onto the horizontal plane; the projection method is: ignore the Z coordinates of all grid cells in the medium-high temperature alteration core region, and only retain their (X,Y) coordinates to form a two-dimensional point set projection on the horizontal plane.

[0084] The major axis direction of the projection is calculated as follows: Principal component analysis (PCA) is used to analyze the direction of the planar coordinates of the projected point set, and the axis of the first principal component (i.e., the direction with the largest variance) is taken as the major axis direction; or the minimum bounding rectangle method is used to calculate the minimum bounding rectangle of the projected point set, and the direction of the long side of the rectangle is taken as the major axis direction. In this embodiment, PCA is preferred because it can more objectively reflect the main extension direction of the spatial distribution of the point set.

[0085] The long axis direction is taken as the long axis direction of the deep mineral exploration target area, and the horizontal direction perpendicular to the long axis direction is taken as the short axis direction of the deep mineral exploration target area.

[0086] The geological basis for using the long axis direction of the medium-high temperature alteration core area as the long axis direction of the deep mineral exploration target area is that the medium-high temperature alteration core area is the direct spatial manifestation of the high temperature channel of the ore-forming hydrothermal fluid. The long axis direction of its morphology usually reflects the strike direction of the ore-controlling structure (such as faults, fracture zones or interlayer fracture zones). The mineralization continuity is best along this direction, which is the most favorable extension direction for deep mineral exploration.

[0087] Centered on the aforementioned central reference point, and with the major and minor axes as the target area distribution directions, a three-dimensional spatial range outside the existing engineering control boundary is delineated as the deep mineral exploration target area; the specific boundary parameters are as follows: Length of the target area along its long axis The value is taken as 1.5 to 3 times the profile width of the high-temperature alteration core area at the central reference point, or 2 to 4 times the survey grid spacing. In this embodiment, The length should be between 200m and 400m, preferably 300m.

[0088] Length of the target area along the minor axis : Values One-third to two-thirds. In this embodiment, The length is selected from 100m to 200m, with 150m being preferred. The setting that the extension length along the major axis of the target area is greater than that along the minor axis reflects the general geological law that the continuity of mineralization along the strike direction of the ore-controlling structure is better than that along the dip direction.

[0089] The vertical range of the target area is defined as follows: based on the depth of the central reference point, it extends upwards and downwards by a predetermined distance. This predetermined distance represents a mid-section height, typically 30m to 60m. In this embodiment, the vertical extension is 50m upwards and downwards, resulting in a total vertical height of 100m for the target area. This vertical range is chosen because: in hydrothermal deposits, the mineralized enrichment zone typically extends vertically by tens to hundreds of meters, and an extension above and below a mid-section height can cover the main vertical distribution range of the mineralized enrichment zone.

[0090] The delineated deep mineral exploration target area is a three-dimensional spatial region located outside the existing engineering control boundary. Its geometric shape is a cuboid region with a definite location, orientation, and size in space. This target area is uniquely determined by the following parameters: coordinates of the central reference point. Major axis azimuth, major axis length, minor axis length, and vertical height; among which These represent the horizontal reference coordinates. This represents the vertical reference coordinates, i.e., the target depth / elevation.

[0091] Through the above steps, the delineated deep mineral exploration target area has clear three-dimensional spatial boundaries and geometric parameters, which can be directly used to guide the deployment of subsequent drilling projects. The drilling project should be laid out along the long axis of the target area, and the boreholes should preferably pass through the central reference point of the target area or be laid out at equal intervals along the long axis. This deep mineral exploration target area determines the mineral exploration direction referred to in this method: the deep space below the existing engineering control boundary along the dip direction of the main ore-controlling structure is the most favorable direction for finding medium- and high-temperature hydrothermal polymetallic concealed ore bodies.

[0092] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions, characterized in that, The specific steps include: S1: Collect rock samples from the target exploration area, measure the relative abundance of alteration minerals and the content of ore-forming elements in each sample, perform standardization processing, and obtain standardized alteration mineral abundance values ​​and ore-forming element concentration values; at the same time, record the spatial three-dimensional coordinates of each sample. S2: Spatial grid interpolation is performed on the spatial three-dimensional coordinates of the sample, the abundance values ​​of altered minerals, and the concentration values ​​of ore-forming elements to construct a three-dimensional data matrix including an altered mineral assemblage data layer and a mineralization intensity distribution data layer. S3: In the three-dimensional data matrix, several vertical sampling lines are laid out, and the sequence of relative abundance of alteration minerals changing with depth is extracted one by one; for each vertical sampling line, the gradient of change of alteration mineral abundance values ​​between adjacent preset depth windows is calculated, and the depths where the gradient of change reaches an extreme value are marked as candidate boundary points for alteration transformation; three-dimensional surface fitting is performed on the candidate boundary points for alteration transformation on all vertical sampling lines to divide the main transformation boundary surface of the medium-high temperature alteration domain in three-dimensional space; S4: Within the medium-high temperature alteration domain, spatial overlay analysis is performed on the mineralization intensity distribution data layer and the alteration mineral assemblage data layer to delineate the medium-high temperature alteration core area; local high-value anomaly areas of mineralization intensity are extracted from the medium-high temperature alteration core area, and their spatial centroids are used as the medium-high temperature mineralization center. S5: Taking the medium-high temperature mineralization center as the benchmark, extend a preset distance outward into the deep unexplored control area along the maximum inclination direction of the main transformation boundary surface at the medium-high temperature mineralization center, and determine the long axis direction of the target area based on the spatial distribution direction of the medium-high temperature alteration core area, thereby delineating the deep mineral exploration target area and determining the mineral exploration direction.

2. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 1, characterized in that, The altered minerals include potassium feldspar, silicified quartz, and chlorite; the ore-forming elements include silver, lead, zinc, and copper.

3. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 2, characterized in that, The three-dimensional data matrix also includes a data layer on the distribution of ore-forming elements; the alteration mineral assemblage data layer uses the relative abundance of potassium feldspar, silicified quartz, and chlorite in each grid cell as a characteristic variable. The ore-forming element distribution data layer uses the concentration values ​​of silver, lead, zinc, and copper in each grid cell as characteristic variables; the mineralization intensity distribution data layer uses the silver equivalent comprehensive mineralization intensity index as a characteristic variable. The silver equivalent comprehensive mineralization intensity index is determined based on the content of each ore-forming element in the ore-forming element distribution data layer, by multiplying the silver concentration value by the product of lead, zinc, and copper with their respective preset conversion coefficients. The relative abundance data of altered minerals in the altered mineral composition data layer is derived from the altered mineral abundance value, and the concentration values ​​of each ore-forming element in the ore-forming element distribution data layer are derived from the ore-forming element concentration value.

4. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 2, characterized in that, The mineral resources controlled by each known ore-controlling structure within the target exploration area are statistically analyzed. The ore-controlling structure with the largest mineral resources is identified as the main ore-controlling structure in the target exploration area, and its dip direction is measured. Extracting the sequence of relative abundance of altered minerals as a function of depth specifically includes: In the three-dimensional data matrix, the exploration line profile that passes through the target exploration area with the most boreholes is used as the reference profile. Along the reference profile and parallel to the dip direction of the main ore-controlling structures in the target exploration area, several parallel vertical sampling lines are laid out at preset intervals. Each vertical sampling line extends from the surface to the bottom boundary of the three-dimensional data matrix. The relative abundance values ​​of potassium feldspar, silicified quartz, and chlorite in the grid cells passed by each vertical sampling line are extracted one by one and arranged in ascending order of depth to form a sequence of the relative abundance of alteration minerals corresponding to each vertical sampling line as a function of depth. The bottom boundary of the three-dimensional data matrix refers to the depth of the last grid cell in the depth direction of the three-dimensional data matrix.

5. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 2, characterized in that, Marking the candidate boundary points of the alteration transition specifically includes: On the sequence of relative abundance of altered minerals changing with depth along each vertical sampling line, the sequence is divided into several depth intervals from top to bottom with a preset depth window as compensation. The arithmetic mean of the relative abundance values ​​of the same altered mineral in each grid cell contained in each preset depth window within each depth interval is calculated as the average abundance value of the altered mineral in the preset depth window. The ratio of the difference between the average abundance values ​​of the same altered mineral in two adjacent preset depth windows to the window depth of the preset depth window is calculated as the abundance change gradient of the altered mineral between adjacent depth windows, where the window depth refers to the difference between the top and bottom depths of the preset depth window. When a positive maximum value is detected in the abundance change gradient of potassium feldspar and a negative maximum value is detected in the abundance change gradient of at least one altered mineral in silicified quartz or chlorite at a certain depth, the corresponding depth position is marked as a candidate boundary point for alteration transformation.

6. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 1, characterized in that, Determining the principal transformation boundary surface specifically includes: All candidate boundary points of alteration transformation marked on all vertical sampling lines are projected into the three-dimensional data matrix. The spatial three-dimensional coordinates of each candidate boundary point of alteration transformation are used as known control points. Kriging interpolation is used to fit a three-dimensional surface to generate a continuous surface. The area above the surface is the low-temperature alteration domain, and the area below the surface is the medium-high temperature alteration domain. This surface is the main transformation boundary surface.

7. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 3, characterized in that, The delineation of the aforementioned medium-high temperature mineralization center specifically includes: Within the grid cell range corresponding to the medium-high temperature alteration domain, the relative abundance value of potassium feldspar of each grid cell is extracted from the alteration mineral assemblage data layer. Grid cells with potassium feldspar relative abundance values ​​higher than a preset threshold are marked as core candidate cells. The area formed by connecting spatially adjacent core candidate cells is delineated as the medium-high temperature alteration core region. Within the spatial range defined by the medium-high temperature alteration core area, the silver equivalent comprehensive mineralization intensity index of each grid cell is extracted from the mineralization intensity distribution data layer. An abnormal lower limit is set based on the average value of the silver equivalent comprehensive mineralization intensity index in the core area. Grid cells with a silver equivalent comprehensive mineralization intensity index higher than the abnormal lower limit are marked as local high-value abnormal areas of mineralization intensity. Calculate the arithmetic mean center of the spatial three-dimensional coordinates of all grid cells within the local high-value anomaly zone of mineralization intensity, and determine the three-dimensional coordinates of the arithmetic mean center as the medium-high temperature mineralization center.

8. The method for constructing mineralization alteration zoning and determining prospecting direction in polymetallic deposits according to claim 4, characterized in that, Delineating deep mineral exploration target areas specifically includes: In the three-dimensional data matrix, the maximum tilt direction of the main transformation boundary surface at the location of the medium-high temperature mineralization center is calculated, and the projection direction of the maximum tilt direction on the horizontal plane is determined as the dip direction of the main transformation boundary surface at that location; taking the medium-high temperature mineralization center as the starting point, a preset distance is extrapolated along the dip direction to the unexplored depth control area, and the extrapolation endpoint is taken as the center reference point of the depth prospecting target area; The boundary of the unexplored area at that depth is defined as follows: Horizontal boundary: An envelope formed by buffering outwards a preset control distance from the projection points of all existing boreholes on the horizontal plane; Vertical boundary: A horizontal plane defined by the deepest point in the vertical direction of the final hole depth of all existing boreholes, extending downwards by a predetermined extrapolation depth.

9. The method for constructing mineralization and alteration zoning of polymetallic deposits and determining prospecting directions according to claim 8, characterized in that, Extract the projection of the medium-high temperature alteration core area onto the horizontal plane, calculate the major axis direction of the projection, take the major axis direction as the major axis direction of the deep mineral exploration target area, and take the horizontal direction perpendicular to the major axis direction as the minor axis direction of the deep mineral exploration target area. Centered on the central reference point, and with the major and minor axes as the target area distribution directions, a three-dimensional spatial range outside the existing engineering control boundary is delineated as a deep mineral exploration target area. The extension length of this target area along the major axis is greater than the extension length along the minor axis, and the vertical range of the target area is based on the depth of the central reference point, extending upwards and downwards by a predetermined distance, thereby determining the mineral exploration direction.

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