A method for processing primary halo data of a structure-controlled hydrothermal deposit

Abstract

The application discloses a kind of primary halo data processing methods of tectonically controlled hydrothermal deposit, comprising the following steps: determining the primary halo combination element of analysis test;Measure the hole depth data of sampling point, the occurrence data of fault structure, ore-controlling structure, the spatial coordinate information of sampling point and the occurrence data of fault;The main fault structure area on exploration line profile is divided into different subareas, the same grid in a subarea will use the same search ellipsoid parameter, and interpolation of grid data is carried out;Calculate the interpolation data of each grid;Draw primary halo geochemical anomaly map;According to primary halo geochemical anomaly map, analyze deep ore body prospecting potential and prospecting direction.The grid data processing method according to the oblique direction interpolation of ore-controlling structure proposed in the application fully considers the influence of ore-controlling structure on primary halo geochemical anomaly, that is, the interpolation search ellipsoid adjusts with the occurrence change of ore-controlling structure.Improves the effect of using primary halo geochemical anomaly prospecting.

Description

Technical Field

[0001] This invention relates to the field of solid mineral exploration technology, specifically to a method for processing primary halo data of tectonic-controlled hydrothermal deposits. Background Technology

[0002] Ore-forming hydrothermal fluids migrate along fault structures. With changes in physicochemical conditions, ore-forming materials precipitate and accumulate in the fractured zones of these faults, forming metallic hydrothermal deposits. Primary halo exploration geochemical methods are a prospecting approach for finding concealed hydrothermal metallic deposits, widely used in the exploration of hydrothermal deposits for gold, antimony, copper, lead, zinc, tungsten, and tin. Faults and fissures developed in mining areas serve as channels for ore-forming material migration and favorable spaces for mineral precipitation and storage. Due to differences in elemental geochemical properties and the physicochemical conditions of the hydrothermal fluids, different elements precipitate sequentially during the migration of ore-forming hydrothermal fluids along faults, exhibiting elemental geochemical zoning structures in the ore body and surrounding rocks, known as primary halo zoning. Primary halos are generally divided into vertical zonation sequences consisting of a leading halo, a near-ore halo, and a tail halo. The leading halo typically contains low-temperature elemental combinations, distributed in the leading section of the direction of ore-bearing solution movement or at the top (or in front) of the ore body; the near-ore halo typically contains medium- and low-temperature elements, distributed in the ore body and surrounding areas. Tail halos are generally composed of medium- to high-temperature elemental combinations and are distributed in the direction of the ore-bearing solution source or in areas below the tail of the ore body. Therefore, geochemical analysis and interpretation of primary halos can provide valuable clues for predicting deep blind ore bodies and probing their extension direction. However, hydrothermal deposits are influenced to varying degrees by ore-forming geological conditions such as magmatic rocks, structures, strata, and lithology. Therefore, for hydrothermal deposits with different ore-forming geological conditions, targeted data processing methods must be adopted when applying primary halo exploration geochemical methods.

[0003] Primary halo exploration geochemical methods involve collecting rock samples from boreholes or adits below the surface along the exploration profile. These samples are then analyzed for their mineral assemblage and trace element content to obtain geochemical elemental data and metallic mineral assemblage information at different depths along the exploration profile. Further data analysis and primary halo geochemical elemental mapping are then performed. Traditional primary halo exploration geochemical data processing methods suffer from the following problems:

[0004] (1) Ore-forming fluids migrate along fault structures, causing mineral deposits to precipitate and accumulate, forming ore bodies. Therefore, the distribution characteristics and extension direction of ore bodies are mainly controlled by fault structures. Traditional methods for primary halo sample collection and primary halo profile mapping, such as Kriging interpolation, distance-weighted interpolation, mixed geometric curvature interpolation, and dynamic ellipsoid search, are all spatial data processing methods and do not fully consider the important influence of the key geological element of "ore-controlling structures" on primary halo data processing and mapping.

[0005] (2) Ore-forming fluids are hydrothermal fluids enriched with multiple metallic elements. Taking scheelite deposits as an example, each precipitation of hydrothermal fluids forms major ore minerals such as scheelite, while also precipitating minor minerals such as pyrite, galena, sphalerite, chalcopyrite, arsenopyrite, and molybdenite. Therefore, the more types of elements enriched in the ore-controlling structure where the main ore body is located, and the higher the element enrichment intensity, the more times hydrothermal fluids have been injected into the area, and the greater the ore-forming potential, rather than simply the "high content of ore-forming elements" that previous researchers have focused on.

[0006] In view of this, the present invention provides a method for processing primary halo data of tectonic-controlled hydrothermal deposits. Summary of the Invention

[0007] To address the aforementioned shortcomings of existing technologies, this invention provides a method for processing primary halo data in tectonic-controlled hydrothermal deposits. This method solves the problems of neglecting the influence of fault structure occurrence on the primary halo and the indicative significance of multi-element enrichment intensity on the main ore body in traditional exploration line profile primary halo data processing.

[0008] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0009] A method for processing primary halo data of tectonically controlled hydrothermal deposits, comprising the following steps:

[0010] S1: Select exploration profiles based on geological data of the mineral deposits, and set the sampling point depth for each borehole sample based on the distribution and depth of existing boreholes; determine the primary halo assemblage elements for analysis and testing based on the ore mineral assemblage characteristics of hydrothermal deposits.

[0011] S2: Collect rock and mineral samples at the sampling points and analyze the content of primary halo assemblage elements in the rock and mineral samples to obtain primary halo element geochemical data at the sampling points; measure the borehole depth data, fault structure, ore-controlling structure occurrence data, spatial coordinate information of the sampling points, and fault occurrence data at the sampling points.

[0012] S3: Based on occurrence data and geological logging data, the main fault structure areas on the exploration line profile are divided into different sub-regions. The same search ellipse parameters will be used for interpolation of the grid data within the same sub-region.

[0013] S4: Within each sub-region's grid, select known sampling point data for interpolation using the diagonal data as the search direction, and calculate the interpolation data for each grid.

[0014] S5: Based on the correlation between ore mineral assemblage and elemental geochemical data, analyze the types of elements in the primary halo assemblage, calculate the profile background value and anomaly lower limit; based on the interpolated grid data, calculate the element enrichment intensity E value and halo element enrichment index N value for each grid, and draw the primary halo geochemical anomaly map.

[0015] S6: Analyze the mineral exploration potential and direction of deep ore bodies based on the primary halo geochemical anomaly map.

[0016] Further, step S1 includes:

[0017] S11: Based on the geological characteristics of the stratigraphy, ore body distribution and fault structure of the hydrothermal deposit, select existing exploration projects with more than 3 boreholes and adits as primary halo profiles.

[0018] S12: On the exploration line profile, set the sampling point spacing and density according to the distribution and depth of existing boreholes, and determine the point coordinates of the core sample sampling points in all boreholes.

[0019] S13: Based on the mineral assemblage characteristics of hydrothermal deposits, determine the ore type and identify the primary halo assemblage elements that need to be analyzed and tested.

[0020] Furthermore, the methods for collecting rock and ore samples and measuring occurrence data in step S2 include:

[0021] S21: Based on the exploration line profile and the mid-section geological map, and using the location and coordinates of the sampling points, collect 0.5 to 1 kg of rock and mineral samples at the sampling points of the borehole or adit project.

[0022] S22: Rock and mineral samples are ground to 200 mesh and sent to the laboratory for analysis and testing to obtain the primary halo geochemical data of the rock and mineral samples at each sampling point;

[0023] S23: After collecting rock and mineral samples at each sampling point, the coordinate data of the actual sampling point is recorded simultaneously. The coordinate data includes the horizontal axis, vertical axis, and borehole depth data. The fault structure and ore-controlling structure occurrence data of the sampling point are measured. The occurrence data of the ore-controlling structure includes dip, dip angle, and oblique direction.

[0024] S24: Based on the coordinate data of sampling points, fault structures, occurrence data of ore-controlling structures, and primary halo geochemical data of rock and mineral samples, non-uniformly distributed discrete spatial data are formed on the exploration line profile.

[0025] Further, step S3 includes:

[0026] S31: Based on the occurrence data of the ore-controlling structures and the geological logging data of the exploration line profile, sub-regions are divided within the ore-controlling structure area on the exploration line profile. Areas with an oblique value of less than or equal to 15° of the ore-controlling structure are divided into the same sub-region, while areas with an oblique value greater than 15° are divided into different sub-regions.

[0027] S32: Each sub-region is divided into grids with a spacing of 2m×2m or 4m×4m, and the same interpolation is used for the grids within the same sub-region; the discrete spatial data is gridded, and the interpolation data of each grid is selected from known sampling points that participate in the interpolation according to the oblique direction of the ore-controlling structure of that grid.

[0028] Further, step S4 includes:

[0029] S41: Within each sub-region, the known sampling point data for interpolation are selected using the oblique data of the ore-controlling structure as the search direction; the oblique data of the ore-controlling structure is the oblique data obtained by field sampling.

[0030] The oblique search for interpolation points uses the elliptic method:

[0031] The major axis radius 'a' and minor axis radius 'b' of the ellipse are the search distances in the major axis X and minor axis Y directions, expressed in data units, respectively.

[0032] The inclination angle is the angle between the positive X-axis and the radius 'a' of the major axis of the ellipse, ranging from -360° to 360°. Data points within the search ellipse are included in the interpolation, while data points outside the ellipse are not interpolated.

[0033] S42: Calculate the interpolated data value for each grid, which is the content value of different primary halo geochemical elements.

[0034] Further, step S5 includes:

[0035] S51: Based on the geochemical data of primary halo elements and the mineral assemblage in the ore, cluster analysis is used to calculate the correlation of other elements related to the ore-forming elements, and the halo-forming elements of the deposit are determined. The halo-forming elements include the leading halo, the near-ore halo, and the tail halo.

[0036] S52: Calculate the lower limit of anomalies and background values ​​for each element in the exploration line profile;

[0037] S53: Calculate the element enrichment intensity E value based on the interpolated grid data; the element enrichment intensity E value for each grid = grid element content value / lower limit of profile element anomaly; the elements with enrichment intensity are all elements in the primary halo geochemical data obtained from the test and analysis of rock and mineral samples;

[0038] S54: Based on the type of halo element, count the number of halo elements with enrichment intensity E value > 1 for each grid element, which is the halo element enrichment index N value of that grid.

[0039] S55: Draw a geochemical element anomaly map of the primary halo based on the content of primary halo geochemical elements corresponding to each grid; draw a primary halo element enrichment intensity map based on the element enrichment intensity E value; draw a halo element enrichment index map based on the halo element enrichment index N.

[0040] The beneficial effects of this invention are as follows: This method proposes a primary halo data interpolation based on the occurrence of ore-controlling fault structures, and proposes a method for processing primary halo data using element enrichment intensity E value and halo element enrichment index N value, thereby uncovering more abundant mineralization information, improving the reliability of primary halo geochemical anomaly analysis, and avoiding or reducing the influence of factors such as spatial distribution of sampling points, sampling point density, and the primary halo data itself on primary halo geochemical anomalies.

[0041] The gridded data processing method proposed in this invention, which interpolates according to the oblique direction of the ore-controlling structure, fully considers the influence of the ore-controlling structure on the geochemical anomalies of the primary halo. Specifically, the interpolation search ellipsoid is adjusted according to the occurrence of the ore-controlling structure. The interpolation data for each grid are selected from known sampling points chosen according to the oblique direction of the ore-controlling structure within that grid, rather than using a single fixed direction (or directional derivative), considering the minimum distance polygon principle, or taking into account the amount of data involved in the interpolation, as is the traditional method. Primary halo data analysis based on structural direction better reflects the close relationship between the primary halo and the ore-forming structure, improving the rationality and reliability of primary halo anomaly data selection.

[0042] This invention changes the previous method of drawing geochemical element anomaly maps based solely on the elemental content or element-pair ratio of the primary halo profile. It proposes indicators such as "elemental enrichment intensity" and "halo elemental enrichment index," and then uses the elemental enrichment intensity and halo elemental enrichment index to draw geochemical element anomaly maps, analyze deep mineral exploration potential and exploration direction, avoid or reduce the influence of factors such as the spatial distribution density of sampling points and the difference in the content of geochemical elemental data of samples on the primary halo geochemical anomaly, and improve the effectiveness of mineral exploration using the primary halo geochemical anomaly. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for processing primary halo data in tectonically controlled hydrothermal deposits.

[0044] Figure 2 This is a schematic diagram of the sampling points for the exploration line profile.

[0045] Figure 3This is a sub-region of the exploration line profile.

[0046] Figure 4 This is a schematic diagram of grid division.

[0047] Figure 5 This is a comparison chart of gridded data processing of Cu elements in the exploration line profile.

[0048] Figure 6 This is a comparison chart of the geochemical map and the enrichment intensity map of element W.

[0049] Figure 7 This is a diagram showing the enrichment index of elements forming a halo in the exploration line profile. Detailed Implementation

[0050] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0051] like Figure 1 As shown, a method for processing primary halo data of tectonic-controlled hydrothermal deposits is presented. This embodiment takes a copper polymetallic deposit in Yunnan as an example to further illustrate the method for processing primary halo profile data of tectonic-controlled hydrothermal deposits provided by the present invention.

[0052] Includes the following steps:

[0053] S1: Select exploration line profiles based on geological data of the mineral deposits, and set the sampling point depth for each borehole sample based on the distribution and depth of existing boreholes; determine the primary halo assemblage elements for analysis and testing based on the ore mineral assemblage characteristics of hydrothermal deposits.

[0054] Step S1 includes:

[0055] S11: Based on the geological characteristics of the stratigraphy, ore body distribution and fault structure of the hydrothermal deposit, select existing exploration projects with more than 3 boreholes and adits as primary halo profiles.

[0056] S12: On the exploration line profile, set the sampling point spacing and density according to the distribution and depth of existing boreholes, and determine the point coordinates of the core sample sampling points in all boreholes.

[0057] S13: Based on the mineral assemblage characteristics of hydrothermal deposits, determine the ore type and identify the primary halo assemblage elements that need to be analyzed and tested.

[0058] Taking a hydrothermal vein-type copper polymetallic deposit in Yunnan as an example, the main ore bodies discovered through exploration occur in vein-like, stratiform, and lenticular forms. A small number of ore bodies occur at the contact zone between granite porphyry and calcareous host rock strata, but mainly occur in calcareous host rock strata. In tectonically controlled hydrothermal vein-type copper polymetallic deposits, the ore bodies often appear in bands, showing clear tectonic control.

[0059] The No. 0 exploration line of the ore deposit has completed the construction of medium-deep boreholes ZK001, ZK003, ZK005, and ZK007, with the deepest point reaching approximately 900 meters, and has discovered more than four ore veins. For primary halo profile sampling, the No. 0 exploration line profile, which has undergone numerous drilling operations, has a relatively deep drilling control depth, and exhibits significant mineralization and alteration, was selected as a representative exploration line profile.

[0060] On the No. 0 exploration line profile, four boreholes (ZK001, ZK003, ZK005, and ZK007) and the PD001 adit were selected, and sampling locations were pre-set from the surface to depth. Following the principle of "denseer sampling points near the mineralization zone and sparser sampling points further away," one sample was pre-set every 1 meter within the structural alteration zone; one sample was pre-set every 2 meters within a 20-10 meter radius on either side of the structural alteration zone; and one sample was pre-set every 20-40 meters in areas with relatively fresh rocks further away from the structural alteration zone. Figure 2 As shown.

[0061] The ore body exhibits distinct mineralization zoning from shallow to deep. The shallow portion is dominated by hydrothermal infilling of lead-zinc-copper mineralization, with veins of chalcopyrite, galena, and sphalerite. The deep portion shows contact metasomatic copper-tungsten mineralization, including veins of chalcopyrite, scheelite, and wolframite quartz. Based on the ore assemblage, the primary halo assemblage elements designed for analysis and testing include 22 elements: Cu, Pb, Zn, Ag, W, Sn, Mo, Bi, Au, As, Sb, Hg, Co, Ni, Cd, Cr, Ba, Mn, Se, Sr, Ti, and V.

[0062] S2: Collect rock and mineral samples at the sampling points and analyze the content of primary halo elements in the rock and mineral samples to obtain primary halo element geochemical data at the sampling points; measure the borehole depth data, fault structure, ore-controlling structure occurrence data, spatial coordinate information of the sampling points, and fault occurrence data at the sampling points.

[0063] The methods for collecting rock and ore samples and measuring occurrence data in step S2 include:

[0064] S21: Based on the exploration line profile and the mid-section geological map, and using the location and coordinates of the sampling points, collect 0.5 to 1 kg of rock and mineral samples at the sampling points of the borehole or adit project.

[0065] S22: Rock and mineral samples are ground to 200 mesh and sent to the laboratory for analysis and testing to obtain the primary halo geochemical data of the rock and mineral samples at each sampling point;

[0066] S23: After collecting rock and mineral samples at each sampling point, the coordinate data of the actual sampling point is recorded simultaneously. The coordinate data includes the horizontal axis, vertical axis, and borehole depth data. The fault structure and ore-controlling structure occurrence data of the sampling point are measured. The occurrence data of the ore-controlling structure includes dip, dip angle, and oblique direction.

[0067] S24: Based on the coordinate data of sampling points, fault structures, occurrence data of ore-controlling structures, and primary halo geochemical data of rock and mineral samples, non-uniformly distributed discrete spatial data are formed on the exploration line profile.

[0068] During the collection of samples from four boreholes (ZK001, ZK003, ZK005, and ZK007) and the PD001 adit along the No. 0 exploration line profile, approximately 0.5 kg of rock and mineral samples were collected based on the designed sampling point locations and coordinates. These samples were then sent to the laboratory for analysis and testing, resulting in a set of primary halo geochemical data for each sampling point.

[0069] While collecting samples at each sampling point, geological compasses and other tools are used simultaneously to measure the dip, dip angle, and oblique orientation of ore-controlling structures such as fault structures and ore-bearing veins at the designed sampling point locations. For borehole cores, the dip, dip angle, and oblique orientation of ore-controlling structures are determined by combining borehole zenith angle and fault plane axis angle.

[0070] Based on the primary halo geochemical data, ore-controlling structural occurrence data near the sampling points, and the spatial XYZ coordinates of the sampling points obtained from sample analysis at each sampling point, discrete spatial data with uneven distribution on the exploration line profile are compiled. The spatial data attributes of each sampling point include primary halo geochemical data and ore-controlling structural occurrence data.

[0071] S3: Based on occurrence data and geological logging data, the main fault structure areas on the exploration line profile are divided into different sub-regions. The same search ellipse parameters will be used for interpolation of the grid data within the same sub-region.

[0072] Step S3 includes:

[0073] S31: Based on the occurrence data of the ore-controlling structures and the geological logging data of the exploration line profile, sub-regions are divided within the ore-controlling structure area on the exploration line profile. Areas with an oblique value of less than or equal to 15° of the ore-controlling structure are divided into the same sub-region, while areas with an oblique value greater than 15° are divided into different sub-regions.

[0074] S32: Each sub-region is divided into grids with a spacing of 2m×2m or 4m×4m, and the same interpolation is used for the grids within the same sub-region; the discrete spatial data is gridded, and the interpolation data of each grid is selected from known sampling points that participate in the interpolation according to the oblique direction of the ore-controlling structure of that grid.

[0075] Based on the oblique data of the main ore-controlling structures collected from the No. 0 exploration line profile, and considering factors such as stratigraphy, lithology, hydrothermal alteration, and ore-controlling structures on the exploration line profile, areas with oblique values ​​less than 15° on the exploration line profile were divided into one sub-region; areas with oblique values ​​greater than 15° were further divided into different sub-regions, resulting in a total of three sub-regions: A, B, and C. Figure 3 and Figure 4 As shown, the ore body in area A dips steeply, at an angle of over 75°, while the ore body in area B dips at approximately 60°. Area C is located on the footwall of a fault, and the ore-controlling structures in this area differ from those in areas A and B.

[0076] Each sub-region, such as A, B, and C, is divided into grids with a spacing of 4 meters by 4 meters, as shown below. Figure 4 As shown, grids within the same sub-region are all assigned oblique data representing the occurrence of the ore-controlling structures within that sub-region. In subsequent grid data interpolation, this oblique data will be used to define the relevant parameters of the search ellipse.

[0077] S4: Within each sub-region's grid, select known sampling point data for interpolation using the diagonal data as the search direction, and calculate the interpolation data for each grid.

[0078] Step S4 includes:

[0079] S41: Within each sub-region, the known sampling point data for interpolation are selected using the oblique data of the ore-controlling structure as the search direction; the oblique data of the ore-controlling structure is the oblique data obtained by field sampling.

[0080] The oblique search for interpolation points uses the elliptic method:

[0081] The major axis radius 'a' and minor axis radius 'b' of the ellipse are the search distances in the major axis X and minor axis Y directions, expressed in data units, respectively.

[0082] The inclination angle is the angle between the positive X-axis and the radius 'a' of the major axis of the ellipse, ranging from -360° to 360°. Data points within the search ellipse are included in the interpolation, while data points outside the ellipse are not interpolated.

[0083] S42: Calculate the interpolated data value for each grid, which is the content value of different primary halo geochemical elements.

[0084] Within each sub-region, the known sampling points for interpolation are selected using the oblique direction data of the ore-controlling structure as the search direction. The oblique direction of the ore-controlling structure is the oblique data obtained during field sampling, not based on a uniformly oriented gridded interpolation method used by the computer. The implementation of the oblique search interpolation point method adopts the previously proposed and widely used ellipse search method, whose relevant parameters are:

[0085] (1) The major axis radius a and minor axis radius b of the ellipse are the search distances in the X and Y directions of the major axis, respectively, expressed in data units.

[0086] (2) Inclined angle: The angle of inclination between the positive X-axis and the radius a of the major axis of the ellipse (-360° to +360°).

[0087] (3) All data within the search ellipse range are included in the interpolation.

[0088] Data points outside the search ellipse are not considered during interpolation. After selecting the interpolation data, the well-established inverse distance method, which has been proposed by predecessors, is used to calculate the interpolated values ​​of the primary halo geochemical elements for each grid.

[0089] In this embodiment, the above steps are implemented using Sufer7 software. The specific process is as follows: First, the Scattered Data Interpolation dialog box is opened using the "Grid" → "Data" function in Sufer7 software. The X and Y coordinate values ​​are edited. The gridding method is selected for interpolation calculation using the "Scattered Data Interpolation" → "Genaral" function. Gridding methods such as inverse distance ratio, Shepard's method, and Kriging can be selected. The "Scattered Data Interpolation" → "Search" function determines whether to select the Search Ellipse when interpolating grid nodes.

[0090] The parameters for searching the ellipse include the radius and the angle. Radius 1 and Radius 2 are the search distances in the X and Y directions, respectively, expressed in data units. Here, the X value can be set to twice the Y value. The angle is the tilt angle (-360° to +360°) between the positive X-axis direction and Radius 1. The angle is set to the tilt data that is already built into the grid.

[0091] Grid data within the ellipse's range will be used in the interpolation calculation; data points outside the ellipse will not be considered during interpolation. The grid function dialog box is opened via "Grid" → "Function," and a grid file is generated using function relationships, yielding the original halo geochemical data for the interpolated grid. Based on the geochemical element content of each grid calculated using the above steps, an original halo geochemical element anomaly map can be plotted.

[0092] In this embodiment, taking the main ore-forming element Cu as an example, the geochemical element anomaly map obtained using the traditional uniform interpolation gridded data processing method is shown below. Figure 5 As shown in Figure A, the extension direction of the Cu element concentration center is related to the distribution of borehole sampling points, but differs significantly from the actual ore body extension direction, which does not conform to geological facts. Using the gridded data processing method proposed in this paper, which interpolates the oblique direction of the ore-controlling structure, a more reasonable geochemical element anomaly map is obtained, as shown in Figure A. Figure 5 As shown in Figure B, the Cu concentration centers (red areas) in this figure are generally distributed along the direction of the existing ore body, which is consistent with geological reality, rather than being controlled by the distribution direction of the borehole sampling points.

[0093] S5: Based on the correlation between ore mineral assemblage and elemental geochemical data, analyze the types of elements in the primary halo assemblage, calculate the profile background value and anomaly lower limit; based on the interpolated grid data, calculate the element enrichment intensity E value and halo element enrichment index N value for each grid, and draw the primary halo geochemical anomaly map.

[0094] Step S5 includes:

[0095] S51: Based on the geochemical data of primary halo elements and the mineral assemblage in the ore, cluster analysis is used to calculate the correlation of other elements related to the ore-forming elements, and the halo-forming elements of the deposit are determined. The halo-forming elements include the leading halo, the near-ore halo, and the tail halo.

[0096] S52: Calculate the lower limit of anomalies and background values ​​for each element in the exploration line profile;

[0097] S53: Calculate the element enrichment intensity E value based on the interpolated grid data; the element enrichment intensity E value for each grid = grid element content value / lower limit of profile element anomaly; the elements with enrichment intensity are all elements in the primary halo geochemical data obtained from the test and analysis of rock and mineral samples;

[0098] S54: Based on the type of halo element, count the number of halo elements with enrichment intensity E value > 1 for each grid element, which is the halo element enrichment index N value of that grid.

[0099] S55: Draw a geochemical element anomaly map of the primary halo based on the content of primary halo geochemical elements corresponding to each grid; draw a primary halo element enrichment intensity map based on the element enrichment intensity E value; draw a halo element enrichment index map based on the halo element enrichment index N.

[0100] In this embodiment, halo-forming elements refer to elements closely related to the mineralization process that have undergone enrichment or depletion, thus indicating mineralization. Unrelated elements that have not undergone significant migration or enrichment during the mineralization process are not considered halo-forming elements. The determination of halo-forming elements is based on the correlation coefficients with the main ore-forming elements on the profile and cluster analysis.

[0101] (1) Elements closely related to Cu mineralization include Au, Ag, Co, Mo, W, Sn, and Mn. Among them, Cu and Ag have the highest correlation coefficient of 0.84, and may exist in the mining area as chalcopyrite and chalcopyrite in a symbiotic relationship. Mo, W, Sn, and Bi are all typical high-temperature elements with correlation coefficients >0.5. Cu, Au, Ag, Co, Ni, Mo, W, Sn, and Bi belong to the same group at the similarity level of correlation coefficient >0.3, indicating that these elements are closely related and belong to the same set of hydrothermal fluid sources, but have different enrichment levels.

[0102] (2) Elements such as Zn, Cd, As, Sb, and Hg, which are associated with the mineralization of minor metallic minerals (sphalerite, galena, stibnite, etc.) in the deposit, can be used as indicator elements. Among them, Zn and Cd have a strong correlation (>0.9). As, Sb, and Hg are typical combinations of elements at medium and low temperatures, with a correlation (>0.2).

[0103] (3) Elements not closely related to mineralization activities, such as Mn, Sr, Cr, Ni, Ba, Ti, V, and Se, did not undergo significant migration and enrichment during hydrothermal activity and were not considered as halo-forming elements, nor were they included in relevant parameter calculations or primary halo mapping. Ultimately, the elements identified for the front halo of this deposit were Zn, Cd, As, Sb, and Hg; for the near-ore halo, Cu, Ag, Au, Co, and Ni; and for the tail halo, W, Mo, Sn, and Bi, totaling 14 halo-forming elements.

[0104] In this embodiment, the background values ​​and anomaly lower limits of exploration line profile elements are calculated using methods such as the EDA method (exploration data analysis technique) and traditional iterative methods. The anomaly lower limit is equal to the mean of all sampling points in the profile plus twice the standard deviation. The iterative method first calculates the mean X0 and standard deviation S0 of each primary halo element data in the profile; the mean... variance

[0105] High-value data are removed based on the condition X0+3S0, forming a new dataset. The mean X1 and standard deviation S1 of the new dataset are then calculated. This step is repeated until there are no higher values ​​and the dataset conforms to a normal distribution. The final mean X and standard deviation S are then calculated. In this implementation case, the background value = mean X; the lower limit of anomalies = mean X + 2 × standard deviation S. The relevant parameters calculated in this implementation case are shown in Table 1 below.

[0106] Table 1. Calculation results of background values ​​and lower limit parameters of halo elements in the profile.

[0107] element Zn Cd As Sb Hg average value 120.6783 0.8584 9.8755 0.6897 0.0181 Standard deviation 50.0280 0.3914 5.5473 0.3173 0.0110 Abnormal lower limit 220.7343 1.6412 20.9702 1.3244 0.0401 Background value 120.6783 0.8584 9.8755 0.6897 0.0181 element Cu Au Ag Co Ni average value 127.3643 0.0018 0.0009 3.3817 9.3134 Standard deviation 76.1881 0.0013 0.0005 1.9141 4.0937 Abnormal lower limit 279.7405 0.0045 0.0019 7.2099 17.5009 Background value 127.3643 0.0018 0.0009 3.3817 9.3134 element W Mo Sn Bi average value 3.2579 1.0453 1.0000 1.3512 Standard deviation 1.9273 0.4776 0.0100 0.5992 Abnormal lower limit 7.1125 2.0006 1.0200 2.5496 Background value 3.2579 1.0453 1.0000 1.3512

[0108] The calculation of element enrichment intensity involves dividing the element content value of each grid cell by the lower limit of the element's anomaly in the profile. The formula is: Element enrichment intensity E value = Grid element content value / Lower limit of element anomaly in the profile. The element in the enrichment intensity can be any element obtained from sample testing and analysis. Based on the element enrichment intensity E value calculated for each grid cell using the above steps, a primary halo element enrichment intensity map can be drawn.

[0109] In this implementation case, taking element W as an example, the traditional method of mapping using absolute element abundance is affected by factors such as the distribution of sampling points and the test data itself. The resulting geochemical map of element W is as follows: Figure 6 As shown in Figure A, the main features are continuous, low-lying, and weak primary halo anomalies, with a few high-value points (red) distributed near the ore body, which are insufficient to reflect the potential deep extension characteristics of the copper ore body. As a comparative example, the element enrichment intensity index (grid element content value / lower limit of profile element anomaly) proposed in this embodiment is used. Its essence is an element ratio, which can eliminate interference from factors such as errors in the data's own content levels. The resulting element enrichment intensity map is shown below. Figure 6 As shown in B, the geochemical anomaly of element W is significantly enhanced, its concentration center is more prominent, and the regularity of the element enrichment region is more obvious.

[0110] The calculation of the halo element enrichment index N value involves counting the number of halo elements with an enrichment intensity E value > 1 in each grid, which is the halo element enrichment index N value for that grid. Elements that are not halo elements are not included in the statistics. In this embodiment, 14 halo elements, including the front halo, near-ore halo, and tail halo, are identified. Therefore, their maximum N value is E value 14. Based on the halo element enrichment index N value calculated for each grid using the above steps, a halo element enrichment index map can be drawn. On the exploration line profile, areas with higher element enrichment intensity are potential ore-forming hydrothermal centers. Areas with higher halo element enrichment indices are favorable areas for further mineral exploration.

[0111] In this embodiment, based on the calculated enrichment index N value of halo elements in each grid on the exploration line profile, a halo element enrichment index map is drawn, such as... Figure 7 As shown, the exploration profile exhibits a regular enrichment pattern of primary halo-forming elements. This enrichment is not only present in the -200m to -600m region of the existing ore body, but also extends eastward to the footwall of the -800m to -1000m fault, indicating further mineral exploration potential in this area.

[0112] S6: Analyze the mineral exploration potential and direction of deep ore bodies based on the primary halo geochemical anomaly map.

[0113] This method proposes a primary halo data interpolation based on the occurrence of ore-controlling fault structures. It also proposes a method for processing primary halo data using element enrichment intensity E value and halo element enrichment index N value, which uncovers more abundant mineralization information, improves the reliability of primary halo geochemical anomaly analysis, and avoids or reduces the influence of factors such as spatial distribution of sampling points, sampling point density, and the primary halo data itself on primary halo geochemical anomalies.

[0114] The gridded data processing method proposed in this invention, which interpolates according to the oblique direction of the ore-controlling structure, fully considers the influence of the ore-controlling structure on the geochemical anomalies of the primary halo. Specifically, the interpolation search ellipsoid is adjusted according to the occurrence of the ore-controlling structure. The interpolation data for each grid are selected from known sampling points chosen according to the oblique direction of the ore-controlling structure within that grid, rather than using a single fixed direction (or directional derivative), considering the minimum distance polygon principle, or taking into account the amount of data involved in the interpolation, as is the traditional method. Primary halo data analysis based on structural direction better reflects the close relationship between the primary halo and the ore-forming structure, improving the rationality and reliability of primary halo anomaly data selection.

[0115] This invention changes the previous method of drawing geochemical element anomaly maps based solely on the elemental content or element-pair ratio of the primary halo profile. It proposes indicators such as "elemental enrichment intensity" and "halo elemental enrichment index," and then uses the elemental enrichment intensity and halo elemental enrichment index to draw geochemical element anomaly maps, analyze deep mineral exploration potential and exploration direction, avoid or reduce the influence of factors such as the spatial distribution density of sampling points and the difference in the content of geochemical elemental data of samples on the primary halo geochemical anomaly, and improve the effectiveness of mineral exploration using the primary halo geochemical anomaly.

Claims

1. A method for processing primary halo data of tectonic-controlled hydrothermal deposits, characterized in that, Includes the following steps: S1: Select exploration profiles based on geological data of the mineral deposits, and set the sampling point depth for each borehole sample based on the distribution and depth of existing boreholes; determine the primary halo assemblage elements for analysis and testing based on the ore mineral assemblage characteristics of hydrothermal deposits. S2: Collect rock and mineral samples at the sampling points and analyze the content of primary halo assemblage elements in the rock and mineral samples to obtain primary halo element geochemical data at the sampling points; measure the borehole depth data, fault structure, ore-controlling structure occurrence data, spatial coordinate information of the sampling points, and fault occurrence data at the sampling points. S3: Based on occurrence data and geological logging data, the main fault structure areas on the exploration line profile are divided into different sub-regions. The same search ellipse parameters will be used for interpolation of the grid data within the same sub-region. S4: Within each sub-region's grid, select known sampling point data for interpolation using the diagonal data as the search direction, and calculate the interpolation data for each grid. S5: Based on the correlation between ore mineral assemblage and elemental geochemical data, analyze the types of elements in the primary halo assemblage, calculate the profile background value and anomaly lower limit; based on the interpolated grid data, calculate the element enrichment intensity E value and halo element enrichment index N value for each grid, and draw the primary halo geochemical anomaly map. S6: Analyze the mineral exploration potential and direction of deep ore bodies based on the primary halo geochemical anomaly map.

2. The method for processing primary halo data of tectonic-controlled hydrothermal deposits according to claim 1, characterized in that, Step S1 includes: S11: Based on the geological characteristics of the stratigraphy, ore body distribution and fault structure of the hydrothermal deposit, select existing exploration projects with more than 3 boreholes and adits as primary halo profiles. S12: On the exploration line profile, set the sampling point spacing and density according to the distribution and depth of existing boreholes, and determine the point coordinates of the core sample sampling points in all boreholes. S13: Based on the mineral assemblage characteristics of hydrothermal deposits, determine the ore type and identify the primary halo assemblage elements that need to be analyzed and tested.

3. The method for processing primary halo data of tectonic-controlled hydrothermal deposits according to claim 1, characterized in that, The methods for collecting rock and mineral samples and measuring occurrence data in step S2 include: S21: Based on the exploration line profile and the mid-section geological map, and using the location and coordinates of the sampling points, collect 0.5 to 1 kg of rock and mineral samples at the sampling points of the borehole or adit project. S22: Rock and mineral samples are ground to 200 mesh and sent to the laboratory for analysis and testing to obtain the primary halo geochemical data of the rock and mineral samples at each sampling point; S23: After collecting rock and mineral samples at each sampling point, the coordinate data of the actual sampling point is recorded simultaneously. The coordinate data includes the horizontal axis, vertical axis, and borehole depth data. The fault structure and ore-controlling structure occurrence data of the sampling point are measured. The occurrence data of the ore-controlling structure includes dip, dip angle, and oblique direction. S24: Based on the coordinate data of sampling points, fault structures, occurrence data of ore-controlling structures, and primary halo geochemical data of rock and mineral samples, non-uniformly distributed discrete spatial data are formed on the exploration line profile.

4. The method for processing primary halo data of tectonic-controlled hydrothermal deposits according to claim 1, characterized in that, Step S3 includes: S31: Based on the occurrence data of the ore-controlling structures and the geological logging data of the exploration line profile, sub-regions are divided within the ore-controlling structure area on the exploration line profile. Areas with an oblique value of less than or equal to 15° of the ore-controlling structure are divided into the same sub-region, while areas with an oblique value greater than 15° are divided into different sub-regions. S32: Each sub-region is divided into grids with a spacing of 2m×2m or 4m×4m, and the same interpolation is used for the grids within the same sub-region; the discrete spatial data is gridded, and the interpolation data of each grid is selected from known sampling points that participate in the interpolation according to the oblique direction of the ore-controlling structure of that grid.

5. The method for processing primary halo data of tectonic-controlled hydrothermal deposits according to claim 4, characterized in that, Step S4 includes: S41: Within each sub-region, the known sampling point data for interpolation are selected using the oblique data of the ore-controlling structure as the search direction; the oblique data of the ore-controlling structure is the oblique data obtained by field sampling. The oblique search for interpolation points uses the elliptic method: The major axis radius 'a' and minor axis radius 'b' of the ellipse are the search distances in the major axis X and minor axis Y directions, expressed in data units, respectively. The inclination angle is the angle between the positive X-axis and the radius 'a' of the major axis of the ellipse, ranging from -360° to 360°. Data points within the search ellipse are included in the interpolation, while data points outside the ellipse are not interpolated. S42: Calculate the interpolated data value for each grid, which is the content value of different primary halo geochemical elements.

6. The method for processing primary halo data of tectonic-controlled hydrothermal deposits according to claim 1, characterized in that, Step S5 includes: S51: Based on the geochemical data of primary halo elements and the mineral assemblage in the ore, cluster analysis is used to calculate the correlation of other elements related to the ore-forming elements, and the halo-forming elements of the deposit are determined. The halo-forming elements include the leading halo, the near-ore halo, and the tail halo. S52: Calculate the lower limit of anomalies and background values ​​for each element in the exploration line profile; S53: Calculate the element enrichment intensity E value based on the interpolated grid data; the element enrichment intensity E value for each grid = grid element content value / lower limit of profile element anomaly; the elements with enrichment intensity are all elements in the primary halo geochemical data obtained from the test and analysis of rock and mineral samples; S54: Based on the type of halo element, count the number of halo elements with enrichment intensity E value > 1 for each grid element, which is the halo element enrichment index N value of that grid. S55: Draw a geochemical element anomaly map of the primary halo based on the content of primary halo geochemical elements corresponding to each grid; draw a primary halo element enrichment intensity map based on the element enrichment intensity E value; draw a halo element enrichment index map based on the halo element enrichment index N.

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