Blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory

Abstract

The invention relates to a blind mine prediction method based on a three-dimensional geological modeling and structural superposition halo theory, and the method comprises the steps: collecting the three-dimensional geographic information of a mining area, constructing a three-dimensional geological model based on the three-dimensional geographic information, and determining a favorable metallogenic part region of the mining area according to the three-dimensional geological model; acquiring sampling point element data, migrating the sampling point element data to a structural plane of the three-dimensional geologic model or cubic network areas in preset ranges on two sides of the structural plane, and diffusing the sampling point element data to the remaining areas; and extracting a three-dimensional element abnormal region in the diffused three-dimensional geological model, and determining an ore-forming target region in the favorable ore-forming space based on the three-dimensional element abnormal region. According to the method, the advantages of the three-dimensional geologic modeling technology can be fully utilized, the accuracy and reliability of prediction of the structural superposition halo-blind mine are improved, and more powerful technical support is provided for prospecting of the deep part and the periphery of the mine.

Description

Technical Field

[0001] The present invention relates to the field of mine digital technology, and in particular to a blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory. Background Art

[0002] With socioeconomic development and the increasing demand for mineral resources, prospecting deep within and around mines is becoming increasingly important. Traditional mineral resource prediction and evaluation relies primarily on two-dimensional information, including geological, geophysical, and geochemical information. However, as prospecting and exploration become increasingly difficult, relying on two-dimensional representations is no longer sufficient to meet the demands of modern times, let alone the demands of three-dimensional display and analysis.

[0003] After over 30 years of practical application, the effectiveness of the structural overlay halo technology for blind ore prospecting has been widely recognized. This technology studies the characteristics of primary overlay halos within tectonic zones, establishes a structural overlay halo model for mineral deposits, and identifies blind ore prediction markers, thereby predicting deep blind deposits. However, traditional structural overlay halo prospecting methods primarily rely on two-dimensional data processing and analysis, which has certain limitations, such as difficulty in visually displaying the spatial morphology and elemental distribution characteristics of geological bodies, and insufficient adaptability to complex geological conditions.

[0004] In recent years, with the rapid development of computer graphics and the continuous improvement of 3D spatial data processing capabilities, 3D visualization technology has gradually gained recognition and mastery. 3D geological modeling technology can visualize the geometry and attribute information of geological bodies in three dimensions, providing geologists and clients with more intuitive and accurate spatial data. In the field of deep blind deposit prediction, combining 3D geological modeling technology with the structural overlay halo method for blind deposit detection can better leverage their respective strengths and improve the accuracy and efficiency of blind deposit prediction.

[0005] Currently, research in 3D geological modeling has yielded considerable results both domestically and internationally. Examples include the concept of 3D geological modeling proposed by Canadian scholar Houlding and the kriging method developed by South African mining engineer Danie Krige. These technologies provide a theoretical foundation and methodological support for 3D geological modeling. However, research on the application of 3D visualization to the structural overlay halo method for blind ore prospecting is relatively limited. In particular, there remains significant research and development potential in integrating 3D geological modeling with the structural overlay halo method. Summary of the Invention

[0006] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory. By constructing a three-dimensional geological model, the element data of the sampling point is migrated to the structural surface of the three-dimensional geological model or the cubic grid area within a preset range on both sides of the structural surface, and then diffused to the remaining area, and the three-dimensional element anomaly area in the diffused three-dimensional geological model is extracted, thereby determining the mineralization target area in the favorable mineralization space. This method can fully utilize the advantages of three-dimensional geological modeling technology, improve the accuracy and reliability of blind mine prediction, and provide more powerful technical support for prospecting deep in mines and in the periphery.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A blind mine prediction method based on 3D geological modeling and structural superposition halo theory, including:

[0009] Collecting three-dimensional geographic information of the mining area, constructing a three-dimensional geological model based on the three-dimensional geographic information, and determining favorable mineralization space in the mining area based on the three-dimensional geological model;

[0010] Acquiring element data of sampling points, migrating the element data of the sampling points to a structural surface of a three-dimensional geological model or to a cubic grid area within a preset range on both sides of the structural surface, and spreading the data to the remaining area;

[0011] The three-dimensional element abnormal area in the three-dimensional geological model after diffusion is extracted, and the mineralization target area in the favorable mineralization space is determined based on the three-dimensional element abnormal area.

[0012] Optionally, constructing the three-dimensional geological model includes:

[0013] Collecting three-dimensional geographic information of the mining area, wherein the three-dimensional geographic information of the mining area includes: lithology data, basic drilling information, and stratification results of various geological layers;

[0014] The three-dimensional geographic information is coupled with the sampling points, boreholes and tunnel layout to construct the three-dimensional geological model.

[0015] Optionally, determining the favorable mineralization area of ​​the mining area includes:

[0016] Analyze each geological boundary according to the three-dimensional geological model to obtain the existence of hidden faults near the exploration line and the intrusion contact interfaces on both sides of the faults;

[0017] Based on the spatial relationship between the intrusive contact interface and the tectonic alteration zone, and further combined with the waveform fitting, the favorable mineralization space is determined.

[0018] Optionally, obtaining the sampling point element data includes:

[0019] Sampling points are arranged in the mining area, and elemental measurement is performed on samples collected on site using a fractional prospecting method according to the sampling points to obtain elemental data of the sampling points.

[0020] Optionally, migrating the sampling point element data to a structural surface of a three-dimensional geological model or a cubic grid area within a preset range on both sides of the structural surface includes:

[0021] The top and bottom surfaces of the ore body structural belt in the three-dimensional geological model are used as structural surfaces, and the three-dimensional geological model is partitioned according to the structural surfaces to obtain the structural surface area and the cubic grid area within a preset range on both sides of the structural surface;

[0022] The data interpolation method is used to migrate the element data of the sampling point to the construction surface area or the cubic network area within a preset range on both sides of the construction surface.

[0023] Optionally, diffusing to the remaining area includes:

[0024] Dividing a plurality of leading edge halo elements in the sampling point element data into outer zone, middle zone and inner zone according to the outer, middle and inner zone standard of the structural superposition halo, and assigning values;

[0025] The corresponding weight of the element is set, the assigned value is multiplied by the corresponding weight, and combined with the element content, a comprehensive abnormal attribute index of the element data of the sampling point between the outer zone, the middle zone and the inner zone is obtained.

[0026] Optionally, setting the corresponding weight of the element includes:

[0027] The vertical data of each sampling point is acquired, and the vertical data is fitted to form a concentration fitting function that varies along the depth, and the concentration fitting function is used as the corresponding weight of the element.

[0028] Optionally, obtaining the comprehensive abnormal attribute indicator includes:

[0029] Obtain the element content between the outer zone, the middle zone and the inner zone, multiply the assigned value by the corresponding weight as the final weight, multiply the content of each element by the final weight, and further add them to obtain the comprehensive abnormal attribute index that changes along the depth.

[0030] The beneficial effects of the present invention are:

[0031] By constructing a three-dimensional geological model, the present invention can more intuitively and comprehensively display the geological structure and element abnormal distribution of the mining area, enabling geological experts to more accurately identify and analyze favorable mineralization areas, thereby improving the accuracy of blind mine prediction.

[0032] The present invention not only takes into account the three-dimensional geographic information of the mining area, but also combines the element data of the sampling points, and performs data migration, diffusion and calculation of comprehensive abnormal attribute indicators through specific algorithms, fully integrating multiple data resources, avoiding the limitations of a single data source, and further improving the reliability of the prediction results.

[0033] The present invention realizes the operations such as rapid import, migration, interpolation and diffusion of sampling point data through customized software modules, reduces the workload and time cost of manual data processing, and improves the efficiency of data processing.

[0034] The three-dimensional visualization results display of the present invention enables geological experts to understand and analyze data more quickly, reducing the conversion time from two-dimensional data to three-dimensional spatial imagination, thereby enabling faster decision-making and improving the efficiency of the entire prospecting work.

[0035] The present invention's construction of superimposed halo technology for blind ore exploration can itself reduce the workload of sample collection, processing, and analysis. On this basis, the present invention further optimizes the sample collection and analysis strategy through three-dimensional geological modeling and data diffusion, avoids unnecessary sample collection, and thus reduces exploration costs.

[0036] The present invention can more accurately determine the mineral target area, reduce the scope of blind exploration, improve the pertinence of exploration work, avoid ineffective investment in non-mineralizing areas, and thus reduce exploration costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flow chart of a blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to an embodiment of the present invention;

[0039] Figure 2 This is a topographical diagram of the alteration zone No. 1 in the Huanaote mining area according to an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the main geological interfaces of the No. 1 alteration zone in the Huanaote mining area according to an embodiment of the present invention;

[0041] Figure 4 This is a three-dimensional closed geological body display diagram of the No. 1 alteration zone in the Huanaote mining area according to an embodiment of the present invention;

[0042] Figure 5This is a comprehensive three-dimensional anomaly model diagram of the leading edge halo elements in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 As shown, this embodiment discloses a blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory, including: collecting three-dimensional geographic information of the mining area, constructing a three-dimensional geological model based on the three-dimensional geographic information, and determining the favorable mineralization area of ​​the mining area according to the three-dimensional geological model; obtaining sampling point element data, migrating the sampling point element data to the structural surface of the three-dimensional geological model or the cubic network area within a preset range on both sides of the structural surface, and diffusing it to the remaining area; extracting the three-dimensional element anomaly area in the three-dimensional geological model after diffusion, and determining the mineralization target area in the favorable mineralization space based on the three-dimensional element anomaly area.

[0046] This embodiment discloses a blind ore prediction method based on three-dimensional geological modeling and structural superposition halo theory. The method includes the following: The Huanaote silver polymetallic deposit is located in the accretionary zone of the southeastern continental margin of the Siberian plate. The exposed strata mainly include the Middle Ordovician and Upper Silurian of the Lower Paleozoic, the Devonian and Lower Permian of the Upper Paleozoic, the Jurassic and Upper Cretaceous of the Mesozoic, and the Holocene of the Quaternary of the Cenozoic. The mineralized alteration zone has been affected by multiple phases of tectonic movement, resulting in intense rock alteration and fragmentation. Alteration Zone I is the main mineralization zone in the mining area, located within the No. 1 fracture zone in the northeast of the mining area. It is controlled for 2,300 meters in length, with an overall strike of 117°, a dip to the northeast, and a dip angle of 57 to 88°.

[0047] Data collection includes the collection of 3D geographic information for the mining area, including lithologic data, basic drillhole information, and stratification results for each geological layer. This drillhole data is crucial as a foundation for precise constraints. This data serves as the foundation for 3D geological modeling and the development of a drillhole geological database for the Huanaote silver polymetallic deposit.

[0048] Furthermore, constructing a three-dimensional geological model includes: collecting three-dimensional geographic information of the mining area, which includes: lithology data, basic drilling information and stratification results of each geological layer; coupling the three-dimensional geographic information with sampling points, drilling holes and tunnel layout to construct a three-dimensional geological model.

[0049] Specifically, the CnGIM-ma software was used to construct a three-dimensional geological model using borehole data combined with the exploration line profile method. First, the exploration data was imported, including exploration results such as boreholes and adits as precise constraints, as well as various boundaries reasonably inferred based on the exploration results as fuzzy constraints. Then, the basic form of the model was created, and global encryption and fitting calculations were used alternately to ensure that the created geological surface accurately passed through the precise constraint points while gradually approaching the fuzzy constraints. Finally, verification and correction were carried out, and reference surfaces were introduced to correct and adjust areas lacking exploration control points to ensure the correctness of the geological relationship between geological interfaces and the rationality of the relative spatial geometric relationship:

[0050] The study area is approximately 4 km 2 The model base elevation is designed to be -150m. The main modeling data include 23 1:1000 exploration line profiles; 1:1000 topographic and geological Figure 1 The data includes seven 1:1000 scale plans of the middle section, with elevations ranging from 450m to 100m and intervals of 50m, including sampling points in the middle section; and histograms of 104 drill holes, including sampling information from the drill holes. Before importing the geological maps into the CnGIM-ma software, they must be scaled and geometrically corrected to eliminate 2D graphic errors and errors, thereby improving consistency after importing the data into 3D space. Drill hole data, as the foundation for precise constraints, is particularly important. This data serves as the basis for 3D geological modeling and establishes a drill hole geological database for the Huanaote Silver Polymetallic Mine. Data from 104 drill holes located along 23 exploration lines were collected and entered. Initially, stratigraphic lithology tables must be imported, followed by batch import of drill hole information data, which primarily includes basic drill hole information and stratification results.

[0051] First, use the import point set function to import the coordinates of the control points within the modeling range and expand the control points into three-dimensional space; then use MAPGIS or AutoCAD software to vectorize the contour lines in the topographic geological map and attach elevation attributes. The contour line range can be appropriately larger than the modeling range to facilitate later cropping. Convert it into DXF data format and import it into CnGIM-ma software. Utilize the geological surface modeling in process modeling, select the control points imported in the early stage as "precise constraints" conditions to ensure that the constructed topographic surface passes through these points 100%; select the contour lines with elevation attributes as "fuzzy constraints" conditions to control the shape of the topographic surface. The discrete smooth interpolation (DSI) function in CnGIM-ma software is applied in the modeling process. Finally, import the various geological information required in the topographic geological map into the three-dimensional map, and project this information into the constructed topographic surface. In this way, a topographic surface with geological attributes is obtained, such as Figure 2 shown.

[0052] The geological interfaces mainly include stratum interface, fractured alteration zone interface, fault surface, etc. The modeling process is roughly divided into 6 steps:

[0053] Import exploration data: Data involved in geological surface modeling are divided into 100% reliable exploration data (precise constraints) and reference data with reasonable trends (fuzzy constraints) according to their reliability. Exploration data include exploration results such as drilling and horizontal tunnels; reference data are mainly various boundaries that are reasonably inferred based on exploration results; (2) Import or define model range: For most geological objects, a unified model range can be used. For objects that have been pinched out in this range, the model range can be defined separately, or a unified range can be used to generate a larger interface, which can then be cut later. (3) Create model: After selecting the target object, use global encryption and fitting calculations alternately to ensure that the created geological surface accurately passes through the precise constraint points while gradually approaching the fuzzy constraint conditions. (4) Verification and correction: Introduce reference surfaces to correct and adjust areas that lack exploration control points to ensure the correctness of the geological relationship between geological interfaces and the rationality of the spatial relative geometric relationship (thickness). For example, if the later created geological surface partially passes through the upper geological surface created earlier, it is necessary to use the earlier created geological surface as a reference surface to make local adjustments to the later created surface so that the spatial relationship between the two conforms to the actual situation. (5) Geophysical Correction (Adjustment of Relief): Use the geophysical interpretation results as a reference surface to further adjust the model's relief. (6) Optimization and Output: Crop the created surface that exceeds the model's range.

[0054] After the creation of each geological surface is completed, the previous layer is cut with the later layer according to the cutting relationship between the new and old strata, rock intrusion, etc. After removing the redundant part, the target geological interface in the study area is obtained, and then the corresponding geological color attribute is given to it to obtain the surface model of each geological unit (such as Figure 3 ).

[0055] Based on the cubic mesh used for the modeling scope, a bounding box is created to define the complete modeling scope surface. Based on the intersection of the modeling scope surface and the topographic surface (the two intersect), the "Single Closed Volume" function in the surface tool is used to clip the topographic surface and the modeling scope surface together, forming a cube with the topographic surface. This serves as the initial building block. The Quaternary base, the top and bottom surfaces of the alteration zone, the fault plane, and the rock intrusion contact interface are then clipped against the building block to form separate closed small geological volumes. During this process, the intersection of each geological volume is carefully considered. For example, the top and bottom surfaces of the alteration zone and the fault plane cannot cut through the Quaternary cover layer. Faults disrupt the rock intrusion contact interface but do not destroy the alteration zone. Finally, a 3D geological volume model is formed, consisting of independent and closed small geological units. In this case, a total of 11 closed units are formed, including the Quaternary cover layer, the alteration zone, the slate east of the alteration zone hanging wall fault, and the granite east of the alteration zone hanging wall fault.

[0056] For ore bodies in alteration zones, when importing geological boundaries, you can import the ore body boundaries into 3D space along with the exploration line profiles. Then, use the "Create Surface from Multiple Lines" command in the surface collection to connect the ore body boundaries in each exploration line to form a closed 3D ore body. In this case, several small ore bodies were removed and merged, resulting in a total of five ore body units.

[0057] After all the production is completed, a single closed unit can be displayed in three dimensions, or multiple units can be displayed as needed. When displaying multiple units at the same time, the transparency of the external unit can be adjusted to simultaneously display the internal and external units. Figure 4 shown.

[0058] Furthermore, determining the favorable mineralization areas in the mining area includes: analyzing each geological boundary according to the three-dimensional geological model to obtain the hidden faults near the exploration line and the intrusion contact interfaces on both sides of the faults; determining the favorable mineralization space based on the spatial relationship between the intrusion contact interface and the tectonic alteration zone, and further determining the favorable mineralization areas in the favorable mineralization space in combination with the waveform fitting.

[0059] Furthermore, obtaining elemental data of sampling points includes: arranging sampling points in the mining area, and performing elemental measurement on samples collected on-site using a fractional prospecting method according to the sampling points to obtain elemental data of the sampling points.

[0060] Specifically, sampling point arrangement and data acquisition: Sampling points are arranged in the mining area, and elemental measurement of samples collected on site is carried out using fractional prospecting methods to obtain elemental data of the sampling points.

[0061] Furthermore, migrating the element data of the sampling points to the structural surface of the three-dimensional geological model or the cubic network area within a preset range on both sides of the structural surface includes: taking the top and bottom surfaces of the ore body structural belt in the three-dimensional geological model as the structural surface, partitioning the three-dimensional geological model according to the structural surface, and obtaining the structural surface area and the cubic network area within the preset range on both sides of the structural surface; using the data interpolation method to migrate the element data of the sampling points to the structural surface area or the cubic network area within the preset range on both sides of the structural surface.

[0062] Furthermore, the diffusion to the remaining area includes: dividing several leading halo elements in the element data of the sampling point into outer zones, middle zones and inner zones according to the outer, middle and inner zones of the structural superposition halo, and assigning values; setting the corresponding weights of the elements, multiplying the assigned values ​​by the corresponding weights, and combining them with the element content to obtain the comprehensive abnormal attribute indicators of the element data of the sampling point between the outer zones, middle zones and inner zones.

[0063] Specifically, data migration and diffusion include: taking the top and bottom surfaces of the ore body structural belt in the three-dimensional geological model as the structural surface, partitioning the three-dimensional geological model according to the structural surface, and obtaining the structural surface area and the cubic network area within the preset range on both sides of the structural surface. The data interpolation method is used to migrate the sampling point element data to the structural surface area or the cubic network area within the preset range on both sides of the structural surface, and diffuse it to the remaining area. Several leading edge halo elements in the sampling point element data are divided into outer zones, middle zones and inner zones according to the outer, middle and inner zone standards of the structural superposition halo, and assigned values. The corresponding weight of the element is set, the assigned value is multiplied by the corresponding weight, and combined with the element content, the comprehensive abnormal attribute index of the sampling point element data between the outer zone, middle zone and inner zone is obtained:

[0064] In this embodiment, the leading halo elements are Hg, B, Sb, and As. How to form a comprehensive index of the four elements in practical applications is a complex process. First, the content of different elements varies greatly, usually by 2-3 orders of magnitude, and cannot be directly allocated according to the weight ratio. For example, the highest As content is 142556×10 -6 , the minimum is 18.9×10 -6 , with an average of 14274×10 -6 ; The maximum B content is 150×10 -6 , the lowest is 0.84×10 -6 , with an average of 38.37×10 -6 ; The content of Hg is three orders of magnitude lower than that of the other three elements; secondly, the significance of different front halo elements to ore bodies varies greatly in different mines, such as Figure 4 As described above, the Hg element in the present invention has a good indication effect on the ore body. The different strengths of the indication significance have different weights in the comprehensive index.

[0065] In response to the above problems, the "normalization method" is used. First, the four leading halo elements are divided into three zones according to the outer, middle and inner zoning standards of the structural superposition halo, and the values ​​are assigned as 1, 2 and 3 respectively. The vertical data of each sampling point is obtained, and the vertical data is fitted to form a concentration fitting function that varies along the depth, and the element content between the outer zone, middle zone and inner zone is obtained. In this case, the weight coefficients corresponding to each leading halo element are: Hg-0.6, B-0.2, Sb-0.1, As-0.1. The assigned value is multiplied by the corresponding weight as the final weight, and the content of each element is multiplied by the final weight, and further added to obtain a comprehensive abnormal attribute index that varies along the depth. The comprehensive index is then imported into three-dimensional space, and the alteration zone area is assigned a value to obtain a comprehensive anomaly model of the leading halo, such as Figure 5 shown.

[0066] Furthermore, setting the corresponding weight of the element includes: acquiring vertical data of each sampling point, fitting the vertical data to form a concentration fitting function that varies along the depth, and using the concentration fitting function as the corresponding weight of the element.

[0067] Furthermore, obtaining comprehensive abnormal attribute indicators includes: obtaining the element content between the outer zone, the middle zone and the inner zone, multiplying the assigned value by the corresponding weight as the final weight, multiplying each element content by the final weight, and further adding them to obtain the comprehensive abnormal attribute indicators that change along the depth.

[0068] Specifically, the 3D elemental anomaly regions were extracted from the diffused 3D geological model. The spatial distribution of the elemental anomalies was visually displayed using the 3D geological model, allowing the location and extent of the anomaly regions to be determined. Based on these 3D elemental anomaly regions, combined with the geological background and mineralization patterns, mineralization targets within favorable mineralization spaces were identified. Two favorable mineralization targets were identified in the lower portion of Alteration Zone I.

[0069] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory, characterized in that: include: Collecting three-dimensional geographic information of the mining area, constructing a three-dimensional geological model based on the three-dimensional geographic information, and determining favorable mineralization space in the mining area based on the three-dimensional geological model; Acquiring element data of sampling points, migrating the element data of the sampling points to a structural surface of a three-dimensional geological model or to a cubic grid area within a preset range on both sides of the structural surface, and spreading the data to the remaining area; The three-dimensional element abnormal area in the three-dimensional geological model after diffusion is extracted, and the mineralization target area in the favorable mineralization space is determined based on the three-dimensional element abnormal area.

2. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 1 is characterized in that: Constructing the three-dimensional geological model includes: Collecting three-dimensional geographic information of the mining area, wherein the three-dimensional geographic information of the mining area includes: lithology data, basic drilling information, and stratification results of various geological layers; The three-dimensional geographic information is coupled with the sampling points, boreholes and tunnel layout to construct the three-dimensional geological model.

3. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 1 is characterized in that: The favorable mineralization areas of the mining area include: Analyze each geological boundary according to the three-dimensional geological model to obtain the existence of hidden faults near the exploration line and the intrusion contact interfaces on both sides of the faults; Based on the spatial relationship between the intrusive contact interface and the tectonic alteration zone, and further combined with the waveform fitting, the favorable mineralization space is determined.

4. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 1 is characterized in that: Acquiring the sampling point element data includes: Sampling points are arranged in the mining area, and elemental measurement is performed on samples collected on site using a fractional prospecting method according to the sampling points to obtain elemental data of the sampling points.

5. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 1 is characterized in that: Migrating the sampling point element data to the structural surface of the three-dimensional geological model or to a cubic grid area within a preset range on both sides of the structural surface includes: The top and bottom surfaces of the ore body structural belt in the three-dimensional geological model are used as structural surfaces, and the three-dimensional geological model is partitioned according to the structural surfaces to obtain the structural surface area and the cubic grid area within a preset range on both sides of the structural surface; The data interpolation method is used to migrate the element data of the sampling point to the construction surface area or the cubic network area within a preset range on both sides of the construction surface.

6. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 3 is characterized in that: Diffusion to the remaining area includes: Dividing a plurality of leading edge halo elements in the sampling point element data into outer zone, middle zone and inner zone according to the outer, middle and inner zone standard of the structural superposition halo, and assigning values; The corresponding weight of the element is set, the assigned value is multiplied by the corresponding weight, and combined with the element content, a comprehensive abnormal attribute index of the element data of the sampling point between the outer zone, the middle zone and the inner zone is obtained.

7. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 6 is characterized in that: Setting the corresponding weights of the elements includes: The vertical data of each sampling point is acquired, and the vertical data is fitted to form a concentration fitting function that varies along the depth, and the concentration fitting function is used as the corresponding weight of the element.

8. The blind mine prediction method based on three-dimensional geological modeling and structural superposition halo theory according to claim 6 is characterized in that: Obtaining the comprehensive abnormal attribute indicator includes: Obtain the element content between the outer zone, the middle zone and the inner zone, multiply the assigned value by the corresponding weight as the final weight, multiply the content of each element by the final weight, and further add them to obtain the comprehensive abnormal attribute index that changes along the depth.

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