Gold mine three-dimensional modeling and target prediction method based on multi-source data fusion

Abstract

The invention relates to a gold mine three-dimensional modeling and target prediction method based on multi-source data fusion. Aiming at the problems of low grade and large burial depth span of the layered hydrothermal gold deposit and two-dimensional limitation and distortion caused by single-source data existing in a traditional modeling method, a three-dimensional joint database is constructed by integrating multi-source data. According to the technical scheme, a three-dimensional joint database is utilized, earth surface, geological stratum, fault occurrence and trend information is determined, and a three-dimensional earth surface and structure model is constructed; based on an explicit and implicit combined modeling method, three-dimensional space distribution of two-long-spot rocks is determined, and a three-dimensional lithologic model is constructed; a structure-lithology coupling constraint modeling method is innovatively adopted, and a three-dimensional grade model is constructed by utilizing an implicit modeling method under the common constraint of a structure model and a lithology model; and finally, target area delineation is carried out according to the three-dimensional geologic model. Compared with a traditional method, the method has the advantages that the problem of distortion of model construction caused by a single data type is effectively solved, and accurate depiction of the ore body is realized. And the constraint of the cause mode on the three-dimensional model is highlighted, so that the target region delineation is more in line with geological knowledge.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional geological modeling of mineral deposits and geological exploration technology, and in particular to a three-dimensional gold mine modeling and target area prediction method based on multi-source data fusion. Background Art

[0002] With the breakthrough of mineral geological metallogenic theory and the innovation of exploration technology, the exploration system of surface and shallow easily identifiable mineral deposits has become increasingly mature, but the amount of recoverable resources has been decreasing sharply year by year. The focus of international mineral exploration has been fully shifted to hidden mines, deep mines and multi-stage transformation-type difficult-to-identify deposits. Such deposits are controlled by the multi-coupling effect of structure, magma and fluid, and the spatial distribution of ore bodies is highly heterogeneous. Taking layered hydrothermal gold deposits as an example, their mineralization process involves complex mechanisms such as porphyry alteration, cryptoexplosive breccia ore control, and micro-fracture percolation and replacement. Traditional exploration methods are difficult to achieve accurate positioning. The defects of existing technologies are concentrated in the following two aspects:

[0003] The dimensional limitations of two-dimensional geophysical inversion are reflected in the fact that mainstream technologies such as high-precision magnetics and transient electromagnetics rely on two-dimensional profile interpretation. These methods can only depict the distribution characteristics of shallow geological bodies and lack the ability to resolve the vertical zoning patterns of deep ore bodies in three dimensions. Statistics show that over 60% of deep verification drill holes fail to interrogate ore deposits due to model errors.

[0004] The boundary distortion problem in single-source data modeling is reflected in the fact that current 3D modeling methods often rely on a single data-driven approach. For example, CN114519662A discloses an orebody connection algorithm based on drillhole data. This invention fails to integrate multiple sources of information, such as geochemical anomalies and lithofacies zoning, leading to misjudgment of lithologic interfaces in fault-prone areas. A typical case study shows that a gold mine ignored the spatial coupling relationship between CSAMT resistivity and Au grade, resulting in a target area prediction deviation of 22.3% from the actual orebody boundary.

[0005] CN118674884A discloses a "method for accurately predicting volcanic uranium ore bodies based on a three-dimensional geological model." The invention describes the following steps: 1) correcting and processing drill hole exploration line profiles and geophysical interpretation profiles; 2) constructing a three-dimensional geological model of the study area based on the corrected and processed profiles; 3) determining the primary spatial enrichment locations of the uranium ore bodies based on the constructed three-dimensional geological model of the uranium deposit; and 4) analyzing the spatial distribution patterns of uranium mineralization based on the primary enrichment locations of the uranium ore bodies to infer favorable mineralization locations. This method is innovative for uranium deposit exploration and can improve the accuracy of uranium ore prediction and the probability of encountering ore deposits. However, it relies on a single data source. Furthermore, constructing a large-scale model requires a large number of profiles to accurately reflect the underground geology, often requiring tens of thousands or even hundreds of thousands of profiles. This extensive manual work is time-consuming and labor-intensive, hindering the development of three-dimensional geological research and three-dimensional mineral resource evaluation.

[0006] In summary, for the exploration of stratiform hydrothermal gold deposits, existing 2D exploration data cannot intuitively display the complex geological structure, while most 3D modeling methods lack the integration of multi-source data, resulting in an inability to fully display the actual geological conditions. These issues have resulted in blind spots in the exploration and prediction of deep mineral resources, necessitating a 3D modeling method that integrates multi-source data to achieve high-precision target prediction.

[0007] The method of the present invention aims to solve the problem of insufficient prediction accuracy of layered and quasi-layered hydrothermal gold deposits with low grade, large burial depth and high exploration difficulty. It innovatively proposes a multi-source data fusion modeling method based on the guidance of mineralization model. According to the existing mineralization model research, the gold mineralization of the target deposit has a significant genetic relationship with the layered intrusive diorite porphyry body. Based on this, a three-dimensional geological model containing lithology, structure and igneous rock distribution characteristics is constructed. Through the constraints of lithology-structure-magma multi-factors, the scientific delineation of the prospecting target area is achieved. Compared with traditional prediction methods, this technical solution significantly improves the geological rationality of target area prediction by strengthening the geological genetic mechanism and the three-dimensional visualization expression of multi-source data, and provides reliable technical support for the exploration deployment of the same type of hidden deposits. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for gold mine three-dimensional modeling and target area prediction by fusing multi-source data. The method combines multi-source data such as geological, topographic, geophysical, and geochemical tests to realize the refined construction of a three-dimensional geological model of layered hydrothermal gold deposits, and to delineate prospecting targets for layered hydrothermal gold deposits based on model constraints such as lithology and structure.

[0009] The method of the present invention comprises the following steps:

[0010] Step 1: Collect geological, geophysical and geochemical data of the target mining area, including but not limited to geological and topographic maps, drill hole data, core data, geochemical test data (gold grade), geophysical data (CSAMT, TEM), surface elevation data and exploration profile data;

[0011] Step 2: Preprocess all collected data, including unifying the coordinate system, removing outliers, digitizing, establishing a geological data database, a drilling exploration database, and a geophysical database, and visualizing them in three-dimensional space;

[0012] Step 3: Based on the elevation points, borehole information, and geological and topographic maps, a three-dimensional surface model is constructed using Kriging interpolation to constrain subsequent modeling;

[0013] Step 4: Using geological topographic maps, survey profiles, drill hole layers, and geophysical profile data, extract fault tracks, occurrence, and geological layering data to construct a three-dimensional structural model;

[0014] Step 5: Based on the lithologic preprocessing results, the stratigraphic correlation method of Resform software was used to compare the stages of the monzonite porphyry, draw the monzonite porphyry isopach map, clarify the distribution of the monzonite porphyry, and draw the pinch-out line of the monzonite porphyry in Petrel software;

[0015] Step 6: Based on the lithologic data and the pinch-out line of the monzonite porphyry, a three-dimensional lithologic model of the study area is constructed under the grid constraints of the structural model;

[0016] Step 7: Based on the gold grade test data and resource plan projection of each drill hole, identify the location and thickness of the ore body in each drill hole, establish a contour surface with a vertical interval of 1m, and depict the plane distribution range of the gold deposit in sections to delineate the ore body boundary;

[0017] Step 8: Based on the delineation results of the gold ore body boundary, a three-dimensional ore body model is constructed in the structural model grid using the explicit modeling method;

[0018] Step 9: Based on the gold grade test data, and under the constraints of the lithology model and structural model, construct a 3D gold grade model of the gold deposit;

[0019] Step 10: Delineate prospecting targets based on a three-dimensional gold grade model constrained by structural-lithologic coupling.

[0020] Effects of the Invention

[0021] The beneficial effects of the present invention are as follows: the present invention innovatively proposes a three-dimensional modeling and target prediction method for gold mines based on multi-source data fusion, and constructs a three-dimensional geological, exploration and geophysical joint database by integrating multi-source data such as geological cataloging, drilling, surface elevation, geophysics and geochemistry, breaking through the limitations of traditional single data modeling and achieving accurate description of the spatial morphology of the ore body; in view of the problem that the traditional Kriging interpolation method directly ignores the geological cause mechanism when simulating the grade model in the ore body model, the present invention pioneers the structural-lithological and other constraint modeling technology under the guidance of the mineralization model, and constructs a three-dimensional gold mine. Grade model, this method reflects the geological genesis mechanism; the target area delineation method based on the geological genesis constraint model not only complies with the regularity and scientific nature of the three-dimensional model data, but also reflects the geological genesis mechanism, making the target area prediction more accurate and the results more reasonable; the method of the present invention provides a more convenient, accurate and reasonable modeling method in areas with sparse exploration profile data and when the data is insufficient to characterize the ore body and lithology distribution, reducing the labor cost and time consumption caused by a large amount of profile work, and providing an efficient technical solution for the exploration of the same type of concealed deposits. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the process of a gold mine three-dimensional modeling and target area prediction method based on multi-source data fusion in one embodiment of the present invention;

[0023] Figure 2 Schematic diagram of the process of collecting, preprocessing and fusing multi-source data in one embodiment of the present invention;

[0024] Figure 3 Schematic diagram of drilling histogram information extraction results in one embodiment of the present invention;

[0025] Figure 4 Schematic diagram of surface elevation data processing results in one embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of the process of digitizing a picture format file in one embodiment of the present invention;

[0027] Figure 6 This is a result map of the geological-exploration-geophysical joint database in one embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the surface elevation point supplementary data results in one embodiment of the present invention;

[0029] Figure 8 A three-dimensional surface model result diagram in one embodiment of the present invention;

[0030] Figure 9 A schematic diagram of a three-dimensional structural model construction process in one embodiment of the present invention;

[0031] Figure 10 Schematic diagram of fault trajectory and occurrence extraction results in one embodiment of the present invention;

[0032] Figure 11 Schematic diagram of the result of the AddFaultpillars operation in one embodiment of the present invention;

[0033] Figure 12 A three-dimensional structural model and a cross-sectional view of an embodiment of the present invention;

[0034] Figure 13 A schematic diagram of the process of core data processing and drawing of isopach maps of monzonite porphyry in one embodiment of the present invention;

[0035] Figure 14 This is a schematic diagram of the results of drawing the isopach map of monzonite porphyry in one embodiment of the present invention;

[0036] Figure 15 The three-dimensional lithologic model, the monzonite porphyry lithologic model, and the cross-sectional profile result diagram in one embodiment of the present invention;

[0037] Figure 16 Schematic diagram of the process of delineating the boundary of an ore body in one embodiment of the present invention;

[0038] Figure 17 This is a window diagram for automatically calculating parameters for ore body thickness in an embodiment of the present invention;

[0039] Figure 18 This is a diagram showing the result of delineating the ore body boundary in one embodiment of the present invention;

[0040] Figure 19 A three-dimensional ore body model and cross-sectional diagram in one embodiment of the present invention;

[0041] Figure 20 This is a schematic diagram of the process of constructing a three-dimensional gold grade model in one embodiment of the present invention;

[0042] Figure 21 A three-dimensional gold grade model and cross-sectional results diagram in one embodiment of the present invention;

[0043] Figure 22 This is a schematic diagram of the results of prospecting target area delineation in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described 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.

[0045] Taking the layered and quasi-layered hydrothermal gold deposits in a certain area as an example, the specific details and originality of the present invention are explained. Figure 1 As shown, the embodiment of the present invention provides a gold mine three-dimensional modeling and target area prediction method based on multi-source data fusion, which mainly includes the following steps:

[0046] Step 1: Collect geological, geophysical and geochemical data of the target mining area, including but not limited to geological and topographic maps, drill hole data, core data, geochemical test data (gold grade), geophysical data (CSAMT, TEM), surface elevation data and exploration profile data;

[0047] Specifically, if Figure 2FIG2 is a flow chart illustrating the collection, preprocessing, and fusion of multi-source data according to an embodiment of the present invention. Step 1 specifically involves the collection of surface geological data, including geological topographic maps, geological sequences, fault information, and surface elevation data obtained by a team of geologists after field surveys. Drilling data acquisition primarily includes borehole information (drillhole number, X-coordinate, Y-coordinate, hole depth, etc.), cores, borehole layering data, borehole trajectory, and geological profiles of the exploration line. Geophysical data acquisition primarily includes geophysical inversion resistivity cross-sections. Geochemical data acquisition primarily includes test data (gold grade test data) for drill core samples. Horizontal projection maps of ore resources obtained after a mining area survey are also included.

[0048] Step 2: Preprocess all collected data, including unifying the coordinate system, removing outliers, digitizing, establishing a geological data database, a drilling exploration database, and a geophysical database, and visualizing them in three-dimensional space;

[0049] Specifically, if Figure 2 The figure shows a schematic diagram of the process of collecting, preprocessing and fusing multi-source data according to an embodiment of the present invention; step 2 is specifically divided into the following steps:

[0050] Step 1: Digitally process the information collected in the drill hole histogram (drill hole information, lithology data, drill hole trajectory, etc.) and unify the units;

[0051] Specifically, the digital processing process of the borehole histogram is to extract the information from the image format file, including borehole information (extracted in the format of borehole number, X, Y, GL, hole depth), lithology information (extracted in the format of depth, lithology number, lithology English name, lithology), borehole layer (extracted in the format of borehole number, geological layer, bottom depth), and borehole trajectory (extracted in the format of depth, inclination, and azimuth). Figure 3 )'s *.xlsx format file is converted to *.txt format;

[0052] Step 2: Determine the scope of the modeling area based on the collected geological and topographic maps, and remove useless information outside the modeling area from the collected elevation data according to the scope of the modeling area ( Figure 4 ), and standardize the data units and coordinate systems, and convert the *.xlsx format file into *.txt format after removing useless data;

[0053] Step 3: Digitally process the collected geological topographic maps, exploration line geological profiles, inversion resistivity cross-sections, and ore body resource plane projections to achieve precise positioning on the Petrel platform.

[0054] Specifically, the picture digitization process is as follows: Figure 5As shown, the detailed process is:

[0055] (1) Load the image in the Petrel platform in bitmap image format;

[0056] (2) Set the coordinates of the image according to the pixels of the image as (X A ,Y A ),(X B ,Y B ),(X C ,Y C ), in one example, the image has a pixel size of 945×715, then the three-point coordinates (X A ,Y A ),(X B ,Y B ),(X C ,Y C ) should be set to (0,0), (0,715), (945,0);

[0057] (3) Open a 2D window to display the image, open the point editing tool, and use the add points to pointset tool to add the three known points in the image ((X 00 ,Y 00 ),(X 11 ,Y 11 ),(X 22 ,Y 22 ))Click three dots in sequence;

[0058] (4) Copy the X and Y coordinates of the three points found in the previous step ((X0, Y0), (X1, Y1), (X2, Y2)) into Excel, and edit the X and Y coordinates of the three points with known coordinates corresponding to these three points into the file. Fit the data points of the X coordinate group and the Y coordinate group respectively, and you can see that they all have a linear relationship.

[0059] (5) According to the requirements of the image to be enlarged, calculate the slope and intercept according to the following formula:

[0060] k=(X 11 -X 00 ) / (X1-X0)

[0061] A=X 11 -k×X1

[0062] B=Y 11 -k×Y1

[0063] Where k is the slope, A is the horizontal intercept, and B is the vertical intercept X 00 、X 11、Y 11 are the X and Y coordinates of the known points in (3), and X0, X1, and Y1 are the X and Y coordinates of the points in (4). In one example, the coordinates of three points in the study area ((X 00 ,Y 00 ),(X 11 ,Y 11 ),(X 22 ,Y 22 )) is (231500,8565800),(233038,8562475),(230238,8558274), (X0,Y0),(X1,Y1),(X2,Y2) in (4) are (316.42,640),(428.47,396),(223.91,88), and it can be calculated that k is 13.7, A is 227156.8143, and B is 8557049.8;

[0064] (6) Then, based on the slope and intercept calculated in the previous step, use the following formula to calculate the final coordinates (X a ,Y a ),(X b ,Y b ),(X c ,Y c ):

[0065] X a =X A ×k+A

[0066] Y a =Y A ×k+B

[0067] Where X a 、Y a The final X and Y coordinates of the image, X A 、Y A are the initial X and Y coordinates of the image, k is the slope, A is the intercept, and B is the intercept. In the above example, the final coordinates of the image should be (227156.8143,8557049.8), (240103.3143,8557049.8), (227156.8143,8566845.3).

[0068] (7) Finally, double-click the image in Petrel and go to settings>settings to set the coordinates of the three points;

[0069] Step 4: Organize the gold grade test data into a *.xlsx file containing the drill hole number, depth, and gold grade (in g / t), and convert the file into a *.txt file.

[0070] Step 5: Import the pre-processed geological topographic map, surface elevation, drilling information, lithology data, drilling trajectory, drilling layering, geological profile of exploration line, inversion resistivity cross section and gold grade data into Petrel software to construct a geological-exploration-geophysical joint database, such as Figure 6 As shown;

[0071] Step 3: Based on the elevation points, borehole information, and geological and topographic maps, a three-dimensional surface model is constructed using Kriging interpolation to constrain subsequent modeling;

[0072] Specifically, if Figure 7 The figure shows the result of the surface elevation point supplementary data of this embodiment. Figure 8 The figure shows the result of the three-dimensional surface model of this embodiment. In the specific implementation process of step 3 of the embodiment of the present invention, since there are areas with missing elevation point data in the modeling area, it is necessary to extract elevation point data from the geological topographic map to convert the two-dimensional coordinates into three-dimensional coordinates, and to extract and convert the borehole coordinates into three-dimensional coordinates to make full preparations for the detailed construction of the surface model ( Figure 7 ); Use the surface model construction function of Structural Modeling→Make surface in Petrel software, input surface elevation data points and modeling area boundaries, set the plane grid accuracy to 10m×10m, and use the Kriging interpolation algorithm to construct a three-dimensional surface model ( Figure 8 );

[0073] Step 4: Using geological topographic maps, survey profiles, drill hole layers, and geophysical profile data, extract fault tracks, occurrence, and geological layering data to construct a three-dimensional structural model;

[0074] Specifically, if Figure 9 The figure shows a schematic diagram of the process of constructing a three-dimensional structural model in this embodiment; step 4 includes:

[0075] Step 1: Use the "Structural Modeling → Make surface" function in Petrel software to input the layer data and modeling area boundaries of the imported drill hole layer data. Set the plane grid accuracy to 10m×10m, and use the "Structural Modeling → Make surface → Pre proc → Trend surface" function to input the surface model to construct a 3D stratigraphic model of the Quaternary, Paleozoic, and Archean strata.

[0076] It should be noted that the surface model is input using the "Structural Modeling→Make surface→Pre proc→Trendsurface" function because there is little drill hole layer data. Directly constructing the stratum model without using the surface model constraints will cause geological layer penetration, which is inconsistent with geological laws. Therefore, it is necessary to input the surface model to constrain the construction of the 3D stratum model.

[0077] Step 2: Draw a fault distribution diagram based on the geological topographic map, and use the fault distribution diagram, geological profile of the exploration line and inversion resistivity profile to extract the fault trajectory and occurrence information. The extraction results are as follows: Figure 10 As shown; in Petrel software, use the "Structural Modeling→Define model" function to create a new model, combine the fault distribution map with the surface model, and use the "Home→Tool Palette→Fault model→Add pillar by 1 point" function to point out all faults in the surface model in the 3D window with the "view from above→orthographic camera" perspective ( Figure 11 ), further based on the geological profile of the exploration line, the inversion resistivity cross section and Figure 10 In the 3D window, use the "Home→ToolPalette→Fault model→Manipulate pillars" function to adjust the bottom of the fault to ensure that the dip angle is the extracted fault strike result. Then, according to the fault combination relationship shown in the fault distribution diagram, combine the faults to complete the construction of the 3D fault model.

[0078] Step 3: In the Petrel software, right-click the modeling area boundary → Convert to grid boundary to set the grid boundary, use the "Structural Modeling → Pillar gridding" function, set the plane grid accuracy to 10m×10m, and perform grid processing;

[0079] Step 4: Use the "Structural Modeling → Make Horizons" function to input the 3D surface model and the three-layer model and its corresponding drill hole layering, and integrate the surface and stratum model into the structural grid; according to the thickness between the strata, use the "Structural Modeling → Layering" function to set the vertical grid with an accuracy of about 1m; visualize the fused surface, stratum, fault model and vertical grid in the 3D window, and the result is a 3D structural model, as shown in the figure below. Figure 12 As shown;

[0080] Step 5: Based on the lithologic preprocessing results, the stratigraphic correlation method of Resform software was used to compare the stages of the monzonite porphyry, draw the monzonite porphyry isopach map, clarify the distribution of the monzonite porphyry, and draw the pinch-out line of the monzonite porphyry in Petrel software;

[0081] Specifically, if Figure 13 FIG. 1 is a schematic diagram of the process of processing core data and drawing isopach maps of monzonite porphyry in this embodiment; Step 5 includes:

[0082] Step 1: Configure the Data Service Manager in the Resform software, create a new work area, and import the drill hole information, lithology data, and drill hole layer data digitized in step 2 into the new work area;

[0083] Step 2: In the Resform software, right-click the stratigraphic comparison map, select Add stratigraphic comparison map, select all boreholes, and create a new stratigraphic comparison map. Use the "Section Wizard" function to create a new comparison skeleton section. Flatten the boreholes in the comparison section according to the basement lithology. Based on the distance between the monzonite porphyry and the basement and the thickness of the monzonite porphyry as the comparison criteria, connect the monzonite porphyries within the same distance range as the same period.

[0084] Step 3: Export the results of the comparison of the monzonite porphyry stages in the format of drill hole number, stage number, top depth, bottom depth, and layer thickness. Classify the exported files according to the monzonite porphyry stages and edit them into *.csv files with X-coordinate, Y-coordinate, and layer thickness headers according to the X and Y coordinates of the drill hole number.

[0085] Step 4: Use the "contour line drawing → minimum tension method drawing" function of the Shuanghu mapping software platform to input the statistical data, add scale, boundary, borehole coordinates, legend, and rectangular grid, and draw the equal thickness map of the monzonite porphyry of each period. The results are as follows: Figure 14 As shown;

[0086] Step 5: After digitizing the isopach map, import it into Petrel software and use the "Home→ToolPalette→Polygon editing→Add points to polygon" function to draw the pinch-out line in the monzonite porphyry isopach map;

[0087] Step 6: Based on the lithologic data and the pinch-out line of the monzonite porphyry, a three-dimensional lithologic model of the study area is constructed under the grid constraints of the structural model;

[0088] Specifically, the three-dimensional lithologic model construction process in step 6 is as follows:

[0089] Step 1: Import the comparison results of the monzonite porphyry periods into the Petrel software according to the geological stratification format and classify them into different periods;

[0090] Step 2: Use the imported data to construct the stratigraphic model in step 4, and construct the top and bottom interfaces constraining the distribution of the monzonite porphyry within the pinch-out lines of each phase; and integrate all constrained interfaces into the structural grid by using the Make Horizons operation in step 4.

[0091] Step 3: In the Petrel software, the lithologic data imported in step 2 were discretized into a structural grid using the "Property Modeling → Welllog upscaling" function. A variogram analysis was performed on the discretized lithologic data using the "Property Modeling → Data analysis" function to adjust the sill value, major range, minor range, and vertical range of each lithologic type in each formation.

[0092] Step 4: Use the "Property Modeling→Facies" function to construct a three-dimensional lithologic model using sequential indicator simulation and envelope surface modeling methods. The results are as follows: Figure 15 As shown;

[0093] It should be noted that the envelope modeling method uses the top and bottom surfaces as constraints, and constructs a specific lithology within the envelope. In this example, the monzonite porphyry lithology model is constructed within the top and bottom interfaces of the monzonite porphyry constructed in the second step of step 6;

[0094] Step 7: Based on the gold grade test data and resource plan projection of each drill hole, identify the location and thickness of the ore body in each drill hole, establish a contour surface with a vertical interval of 1m, and depict the plane distribution range of the gold deposit in sections to delineate the ore body boundary;

[0095] Specifically, if Figure 16 Schematic diagram of the ore body boundary delineation process in this embodiment; Step 7 includes:

[0096] Step 1: In Petrel, create a contour surface at 1m intervals vertically across the mining area. To more accurately delineate the boundaries of the ore body, create a new set of geological layers in Petrel (one layer every 1m).

[0097] Step 2: Based on the general industrial indicators of oxide ores in rock gold deposits in Appendix D of the "Geological Exploration Specifications for Rock Gold Deposits" (DZ / T0205-2020), the cut-off grade of the ore body is set at 0.5g / t. Ore bodies that meet the cut-off grade condition are considered gold ore bodies, and the ore body distribution of each drill hole is identified;

[0098] Step 3: To more conveniently delineate the ore body boundary, create a new geological layer group. In the new geological layer group, right-click Attributes, select Continuous, and set the parameters in Attribute operations ( Figure 17 ), calculate the thickness of the ore body in each 1m layer;

[0099] Step 4: In the 2D window, the ore body thickness value between each layer of each drill hole is displayed. Combined with the ore body resource plane projection map, the ore body boundary is delineated according to the ore body extrapolation rule under the engineering spacing constraint in the "General Rules for Geological Exploration of Solid Minerals" (GB / T 13908-2020). The results are as follows: Figure 18 As shown;

[0100] Step 8: Based on the delineation results of the gold ore body boundary, a three-dimensional ore body model is constructed in the structural model grid using the explicit modeling method;

[0101] Specifically, the three-dimensional ore body model construction process in this example includes:

[0102] Step 1: Based on the results of the ore body boundary delineation, delineate the distribution of the ore body in each contour surface and make a distribution map of the ore body;

[0103] Step 2: In the Petrel software, use the "Property Modeling→Facies" function to create a new attribute model. In each 1m segment of the mining area, use the Surface of Assign values ​​to input the ore body distribution map, specify the ore body within the ore body boundary, and construct a three-dimensional ore body model using the explicit modeling method. The result is as follows: Figure 19 As shown;

[0104] Step 9: Based on the gold grade test data, and under the constraints of the lithology model and structural model, construct a 3D gold grade model of the gold deposit;

[0105] Specifically, if Figure 20 The figure shows a schematic diagram of the process of constructing a three-dimensional gold grade model in this embodiment; step 9 specifically includes:

[0106] Step 1: Import the grade data into the Petrel software and use the "Property Modeling → Welllog upscaling" function. Select the 3D lithologic model as the constraint and discretize the grade values ​​into a 3D structural grid.

[0107] Step 2: Use the "Property Modeling→Data analysis" function to perform detrending and variogram analysis on the grade data discretized into the grid, and set its nugget value, sill value, major range, minor range, and vertical range;

[0108] Step 3: Using the "Property Modeling→Petrophysical" function, the grade data after data analysis is combined with Gaussian random function simulation and explicit modeling methods. Under the constraints of the 3D lithologic model, a 3D gold grade model is constructed. The results are as follows: Figure 21 As shown;

[0109] Step 10: Delineate prospecting targets based on a three-dimensional gold grade model constrained by structural-lithologic coupling.

[0110] Specifically, if Figure 22 The figure shows a schematic diagram of the results of the prospecting target area delineation in this embodiment; the specific process of the prospecting target area delineation is: according to the three-dimensional grade model results constructed under the guidance of the mineralization model and the constraints of the three-dimensional structural model and the three-dimensional lithology model, the three-dimensional visualization displays the three-dimensional gold grade model with a boundary grade of 0.5g / t and above based on the general industrial indicators of oxide ores of rock gold mines in Appendix D of the "Geological Exploration Specifications for Rock Gold Deposits" (DZ / T0205-2020), and delineates the prospecting target area. In this embodiment, the method of delineating the prospecting target area using a fine three-dimensional geological model constructed by a multi-source data fusion method complies with the regularity of the data and reflects the geological genesis of lithology-structure-igneous rocks.

[0111] The gold mine three-dimensional modeling and target area prediction method based on multi-source data fusion provided in this embodiment can effectively express the complex geological structure of layered hydrothermal gold deposits. The generated high-precision three-dimensional geological model of the gold deposit conforms to the actual geological conditions and reflects the geological causes.

[0112] In the absence of conflict, the above embodiments and features in the embodiments may be combined and adjusted with each other.

[0113] The above description is only a preferred embodiment of the present invention and does not limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A gold mine three-dimensional modeling and target area prediction method based on multi-source data fusion, characterized in that: The following steps are involved: Step (1): Collect geological, geophysical and geochemical data of the target mining area, including but not limited to geological and topographic maps, drill hole data, core data, geochemical test data, geophysical data, surface elevation data and exploration profile data; Step (2): pre-process the data in step 1 and construct a three-dimensional visualization database; Step (3): Construct a three-dimensional surface model based on elevation points, borehole information, and geological and topographic maps; Step (4): Extract fault trajectory, occurrence and geological stratification data to construct a three-dimensional structural model; Step (5): Compare the stages of the monzonite porphyry according to the lithologic data, draw the monzonite porphyry isopach map, clarify the distribution of the monzonite porphyry, and draw the monzonite porphyry pinch-out line in the 3D modeling software; Step (6): Based on the lithologic data and the pinch-out line, a three-dimensional lithologic model is constructed under the grid constraints of the structural model; Step (7): Based on the drill hole gold grade data and the resource plan projection map, the ore body boundary is segmented according to the vertical spacing; Step (8): Based on the ore body boundary delineation results, a three-dimensional ore body model is constructed in the structural model grid; Step (9): Under the constraints of the lithologic model and the structural model, the gold grade data are integrated to construct a three-dimensional gold grade model; Step (10): Based on the three-dimensional gold grade model of structure-lithology coupling, the prospecting target area is delineated.

2. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: The geochemical test data in step (1) include gold grade values, the geophysical data include CSAMT, TEM inversion resistivity data and inversion resistivity cross-section diagrams, and the exploration profile data include exploration line geological cross-section diagrams.

3. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: The data preprocessing in step (2) specifically includes: (1) Digitally process the information collected in the borehole histogram (drilling information, lithology data, drilling trajectory, etc.) and unify the units; (2) Determine the modeling scope based on the geological and topographic maps, eliminate the elevation data outside the modeling scope, and unify the units and coordinate systems; (3) The collected geological topographic maps, exploration line geological profiles, inversion resistivity cross-sections, and ore body resource plane projections are digitized to achieve three-dimensional visualization.

4. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: In step (3): a three-dimensional surface model is constructed using the Kriging interpolation algorithm with a plane grid accuracy of 10m×10m, and the surface model is used to constrain subsequent modeling.

5. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: In step (4): by integrating the geological topographic map, the geological profile of the exploration line and the inversion resistivity cross-section map, the fault information, including the extension length, extension direction, occurrence and geological stratification data of the fault, is extracted, and the fault and stratum model is constructed in the 3D modeling software. The plane grid accuracy is 10m×10m, and the vertical grid is divided with an accuracy of 1m to construct the 3D structural model.

6. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: Step (6) specifically includes the following steps: (1) Import the comparison results of the monzonite porphyry stages into the 3D modeling software according to the geological stratification format and classify them into different stages; (2) The imported data is constructed according to the stratigraphic model construction method in step (4) to construct the top and bottom interfaces constraining the distribution of the monzonite porphyry within the pinch-out line of the monzonite porphyry, and the grid processing is performed with a plane grid accuracy of 10m×10m; (3) Discretize the lithologic data into a structural grid and perform variogram analysis to adjust the sill value, major range, minor range, and vertical range; (4) A three-dimensional lithologic model was constructed using sequential indicator simulation and envelope surface modeling methods.

7. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: Step (7) specifically includes the following steps: (1) Construct an isoelevation surface vertically in the mining area at a spacing of 1m; (2) Identify the distribution location and thickness of the ore body in each drill hole according to the cut-off grade threshold specified in the geological exploration specifications; (3) Combined with the identification of ore body thickness and the plan projection map of resource volume, the ore body boundary is delineated according to the extrapolation rules of geological exploration specifications.

8. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1 is characterized in that: In step (8), the method for constructing a three-dimensional ore body model is specifically as follows: making an ore body distribution map according to the result of delineating the ore body boundary; and constructing a three-dimensional ore body model by an explicit modeling method.

9. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1, characterized in that: Step (9) specifically includes the following steps: (1) Discretize the gold grade data into a structural grid under the constraints of the 3D lithologic model; (2) Perform detrending and variogram analysis on the discretized gold grade data, and set its nugget value, base value, major range, minor range, and vertical range; (3) A three-dimensional gold grade model was constructed under the constraints of the three-dimensional lithologic model by combining Gaussian random function simulation and explicit modeling methods.

10. The method for gold mine three-dimensional modeling and target area prediction based on multi-source data fusion according to claim 1, characterized in that: In step (10): the spatial distribution of the three-dimensional gold grade model and the geological genesis constraints are used as the basis for target area delineation, and the high-grade areas are identified as prospecting targets in combination with the mineralization model.

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