Three-dimensional prediction method and system for gold mineralization based on structural information fusion

Abstract

The present invention relates to the field of geological exploration technology, and proposes a three-dimensional prediction method and system for gold mineralization based on structural information fusion. The fusion processing of multi-source data such as geological structure, ore body evolution process and geochemical anomalies breaks through the limitation of traditional three-dimensional modeling methods that stop at the geometric description level. For the first time, the ore-controlling structural information is integrated with geochemical trends and spatial structures to realize a collaborative prediction mechanism of structure-geochemistry-ore body morphology, effectively enhancing the blind mine identification and peripheral prospecting capabilities. At the same time, a target area division mechanism based on the overlap of multiple source layers is constructed, which significantly improves the credibility and accuracy of the prediction results and meets the prospecting needs of high-intensity gold mining areas. The combination of engineering modeling and mid-section map modeling effectively avoids the spatial errors caused by traditional interpolation modeling in areas with sparse data or complex morphology, and improves the authenticity and continuity of the ore body structure.

Description

Technical Field

[0001] The present invention relates to the technical field related to geological exploration, and in particular to a three-dimensional prediction method and system for gold mineralization based on structural information fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As mining continues to increase in depth and intensity, shallow, easily ore-mining bodies are gradually becoming depleted, forcing prospecting efforts to increasingly focus on deeper, concealed ore bodies. Against this backdrop, improving the accuracy and efficiency of mineralization prediction has become a key issue in mineral exploration. Three-dimensional mineralization prediction technology has become a crucial tool for deep prospecting and demonstrates promising application prospects in numerous mining areas.

[0004] In highly-worked gold mining areas or mines already in production, extensive drilling, tunneling, and other engineering operations have typically been completed, accumulating a wealth of original geological information on structure, mineralization, and alteration. With such a high-density, high-value data resource, how to fully utilize it to conduct more targeted and accurate prospecting and prediction, directly supporting mine production and exploration, has become a key focus of current technological development. Traditional gold mineralization prediction methods have largely focused on indirect inference based on geophysical and geochemical anomalies. However, in mining areas with high levels of work and dense engineering coverage, the applicability and accuracy of these methods have been increasingly challenged.

[0005] When conducting prospecting in highly worked or already-producing gold mining areas, existing three-dimensional mineralization prediction methods still have significant limitations and are unable to fully support target area identification and resource assessment in complex mineralization settings. On the one hand, interpolation algorithms (such as Kriging interpolation and inverse distance weighting) are commonly used in the three-dimensional modeling process to construct ore body models. These algorithms rely on the integrity and uniformity of pre-processed data and are prone to spatial errors in areas with sparse data or complex morphology, resulting in insufficient authenticity of the ore body structure. In particular, in areas with sudden changes in ore body morphology or severe fractures, interpolation models have difficulty capturing key structural features. On the other hand, existing methods often directly use a single constructed three-dimensional model for mineralization prediction, resulting in a fragmented prediction and judgment logic, low reliability of target area delineation, and poor interpretability of prediction results, making it difficult to meet the new round of prospecting needs in highly worked mining areas. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a three-dimensional prediction method and system for gold mineralization based on structural information fusion, which is suitable for large-scale structural information mineralization prediction in high-intensity gold mining areas. It integrates and processes multi-source data such as geological structure, ore body evolution process and geochemical anomalies, and innovatively integrates three-dimensional ore body modeling, structural expansion space simulation and structural superposition halo geochemical anomaly analysis. Through three-dimensional integration and cross-validation of multi-source data, it significantly improves the accuracy and prediction efficiency of mineralization target area determination, breaks through the technical bottleneck of traditional two-dimensional prediction and experience judgment, and has good engineering applicability and promotion value.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The first aspect of the present invention is a three-dimensional prediction method for gold mineralization based on structural information fusion, comprising the following steps:

[0009] Using engineering modeling as the main method and mid-section diagram modeling as the auxiliary method, a three-dimensional ore body model was constructed, and the three-dimensional ore body model was analyzed to delineate the first potential mineralization area;

[0010] Obtain cross-section point set data, perform numerical analysis, and generate a capacity expansion space distribution map as a ore-capacity space layer;

[0011] Obtain geochemical data of the sampling area, conduct three-dimensional prediction of mineralization trends, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the superimposed halo three-dimensional model, and obtain three-dimensional prediction results of blind ore bodies;

[0012] The first potential mineralization area, the ore-bearing space layer and the three-dimensional prediction results of the blind ore body are superimposed, and the predicted area of ​​gold mineralization is obtained according to the degree of overlap.

[0013] A second aspect of the present invention provides a three-dimensional gold mineralization prediction system based on structural information fusion, comprising:

[0014] The 3D ore body model construction module is configured to use a method that mainly uses engineering modeling and supplemented by mid-section map modeling to construct a 3D ore body model, perform 3D ore body model analysis, and delineate the first potential mineralization area;

[0015] The expansion space simulation module is configured to obtain cross-section point set data, perform numerical analysis, and generate an expansion space distribution map as a ore-bearing space layer;

[0016] The stacking halo construction module is configured to obtain geochemical data of the sampling area, perform three-dimensional metallogenic trend prediction, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the stacking halo three-dimensional model, and obtain the three-dimensional prediction results of blind ore bodies;

[0017] The superposition module is configured to superimpose the first potential mineralization area, the mineralization-bearing space layer and the blind ore body stereo prediction results, and obtain the predicted area of ​​gold mineralization according to the degree of overlap.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The method of this embodiment breaks through the limitations of traditional three-dimensional modeling methods at the geometric description level, and for the first time integrates ore-controlling structural information with geochemical trends and spatial structures, realizing a collaborative prediction mechanism of structure-geochemistry-ore body morphology, effectively enhancing the blind mine identification and peripheral prospecting capabilities. At the same time, a target area division mechanism based on the overlap of multiple source layers is constructed, which significantly improves the credibility and accuracy of the prediction results and meets the prospecting needs of high-intensity gold mining areas. The combination of engineering modeling and mid-section map modeling effectively avoids the spatial errors caused by traditional interpolation modeling in areas with sparse data or complex morphology, and improves the authenticity and continuity of the ore body structure. This method can more accurately capture key structural features in areas where the ore body morphology changes suddenly or the fracture is severe, improve modeling accuracy and geological interpretation capabilities, and significantly improve the adaptability of traditional methods in complex structural backgrounds.

[0020] The advantages of the present invention and its additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0022] Figure 1 This is a flow chart of the three-dimensional prediction method for gold mineralization according to Example 1 of the present invention;

[0023] Figure 2 This is a flowchart of the three-dimensional prediction method for gold mineralization according to Example 1 of the present invention;

[0024] Figure 3 Schematic diagram of the construction process of the three-dimensional ore body model of Example 1 of the present invention;

[0025] Figure 4 It is a three-dimensional ore body model constructed for the example area in the verification example of Example 1 of the present invention;

[0026] Figure 5 This is a vertical longitudinal projection contour map of the ore body metal content and a structural expansion space simulation map constructed for the example area in the verification example of Example 1 of the present invention;

[0027] Figure 6 is a deep expansion space prediction map for an example area in the verification example of embodiment 1 of the present invention;

[0028] Figure 7 It is a single element anomaly map for the example area and a corresponding R-type cluster analysis dendrogram in the verification example of Example 1 of the present invention;

[0029] Figure 8 It is a vertical longitudinal projection diagram of the structural superimposed halo element combination anomaly of the Guandao gold mine in the example area in the verification example of Example 1 of the present invention;

[0030] Figure 9 It is a large-scale structural mineralization prediction vertical projection map of the Guandao gold deposit in the example area in the verification example of Example 1 of the present invention; DETAILED DESCRIPTION

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0033] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof. It should be noted that, in the absence of conflict, the various embodiments of the present invention and the features in the embodiments can be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.

[0034] Example 1

[0035] In the technical solutions disclosed in one or more embodiments, Figures 1 to 9 As shown, a three-dimensional prediction method for gold mineralization based on structural information fusion includes the following steps:

[0036] Step 1: Using engineering modeling as the main method and mid-section map modeling as the auxiliary method, a three-dimensional ore body model is constructed, the three-dimensional ore body model is analyzed, and the first potential mineralization area is delineated;

[0037] Step 2: Obtain cross-section point set data, perform numerical analysis, and generate a capacity expansion space distribution map as a ore-bearing space layer;

[0038] Step 3: Obtain geochemical data of the sampling area, conduct three-dimensional prediction of mineralization trends, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction results of blind ore bodies;

[0039] Step 4: Superimpose the first potential mineralization area, the mineralization-bearing space layer, and the blind ore body stereo prediction results, and divide them according to the degree of overlap to obtain the predicted area of ​​gold mineralization.

[0040] This method first integrates the technical advantages of engineering modeling and mid-section modeling. A preliminary spatial model of the ore body is constructed by 3D reconstruction of known drillhole, tunnel, and mining engineering data. The model's boundaries and structure are then corrected using geological mid-section data to improve model integrity and spatial accuracy. Based on this, geometric analysis of the 3D model is performed, and the first potential mineralization area is identified by combining characteristic information such as ore body morphology, thickness distribution, and extension trends. Subsequently, spatial numerical processing of the cross-section point set is performed to analyze its expansion trend within the context of the fault structure and geological background. A layer representing the potential ore-bearing space is constructed, revealing the impact of structural deformation on the spatial potential of the ore body. Next, 3D trend modeling is performed using geochemical data from the sampling area. Using multivariate anomaly identification and three-dimensional metallogenic halo analysis, the metallogenic trends of blind ore bodies, peripheral ore bodies, and deep and shallow rich ore bodies are extracted, forming a three-dimensional stacked halo model driven by multi-source information. Finally, these multiple model layers are superimposed and fused, and spatial analysis is used to identify areas with high overlap as gold mineralization prediction targets. This stacking logic, based on the spatial coupling of synergistic metallogenic geological elements, enables quantitative identification of prediction targets.

[0041] The method of this embodiment breaks through the limitations of traditional three-dimensional modeling methods that stop at the geometric description level. For the first time, it integrates ore-controlling structural information with geochemical trends and spatial structures to achieve a collaborative prediction mechanism of structure-geochemistry-ore body morphology, effectively enhancing the blind mine identification and peripheral prospecting capabilities. At the same time, a target area division mechanism based on the overlap of multiple source layers is constructed, which significantly improves the credibility and accuracy of the prediction results and meets the prospecting needs of high-intensity gold mining areas. The combination of engineering modeling and mid-section map modeling effectively avoids the spatial errors caused by traditional interpolation modeling in areas with sparse data or complex morphology, and improves the authenticity and continuity of the ore body structure. This method can more accurately capture key structural features in areas where the ore body morphology changes suddenly or the fracture is severe, improve modeling accuracy and geological interpretation capabilities, and significantly improve the adaptability of traditional methods in complex structural backgrounds.

[0042] In step 1, the method for constructing a three-dimensional ore body model includes the following steps:

[0043] Step 11: Single-project ore body connection to achieve engineering modeling:

[0044] Step 111: Acquire the single-project data in the constructed ore body engineering database to circle the single-project ore body, identify the single-project ore body segment based on the set circle index, and determine the top and bottom boundaries;

[0045] Optionally, in step 11, prospecting data are collected, including mine production prospecting data, historical geological mineral surveys, prospecting and exploration data, etc. The mine production prospecting data includes single project data and mineralization and alteration data. The single project data includes data such as the spatial location, azimuth slope angle, and test results of collected samples of the single project. The mineralization and alteration data includes the grade and thickness of the ore body and the thickness of the alteration zone. A prospecting project database is constructed based on the collected prospecting data.

[0046] A single project is a specific engineering unit, such as a borehole is a drilling project, and a tunnel is a pit exploration project;

[0047] Optionally, the circle mineralization index can be set as a cut-off grade of 0.8g / t and a thickness of ≥1.00m;

[0048] The top boundary, or upper boundary, marks the beginning of the ore body, while the bottom boundary marks its end. These two lines represent the interval through which the ore body passes during the project. For example, if a borehole is drilled from the surface to 500 meters underground and gold concentrations above the demarcation standard are measured between 120 and 160 meters, then 120 meters is the top boundary of the ore body, and 160 meters is the bottom boundary. This 40-meter interval is the demarcated ore body section.

[0049] Step 112: sequentially connect the top and bottom boundaries of the ore bodies corresponding to the adjacent single projects, circle them to obtain the middle ore body boundary line, and assign attributes A such as attribute project and ore body layer number to the boundary points on the ore body boundary line;

[0050] After completing the marking of the top and bottom boundaries of the ore bodies of multiple prospecting projects, the connection modeling is carried out. Specifically, the ore body boundaries of adjacent projects are connected in sequence according to the spatial position to form the middle section ore body boundary line;

[0051] At this point, if a mid-section CAD drawing is imported as a reference, the system will also match and overlay the control lines in the CAD drawing with the engineering data to further optimize the accuracy of the boundary lines. By using engineering data as the primary driver and drawings as the auxiliary, a set of closed ore body boundaries is ultimately formed at each mid-section level.

[0052] Step 12: Obtain the CAD mid-section map of each mid-section, and perform morphological adjustment and spatial correction on the boundary lines of the mid-section ore body obtained by engineering modeling with reference to the CAD mid-section map to optimize the boundary lines and form a set of closed ore body boundary lines at each mid-section level:

[0053] Affected by factors such as engineering control density, single engineering inclination, and curvature, the boundary line of the middle section ore body obtained in step 112 deviates from the actual spatial position. Therefore, the boundary line of the CAD middle section map is used as an auxiliary to provide a modeling reference for the overall distribution characteristics of the middle section ore body for single engineering modeling.

[0054] In mining geology and mining, the midsection refers to a working level in a mine divided vertically at intervals. The midsection orebody boundary is formed by connecting the orebody boundary points at corresponding depths in multiple drill holes at a specific midsection level (e.g., -200 meters).

[0055] The CAD mid-section map is a "plane map" drawn at a certain depth of the mine. It is a manually drawn mid-section map based on the data collected from geological exploration and production, such as drill hole catalog data, tunnel layout diagrams, excavation / sampling records, and mining results.

[0056] Optionally, a method for performing morphological adjustment and spatial correction on the boundary line of the middle section ore body obtained by engineering modeling with reference to a CAD drawing comprises the following steps:

[0057] Step 121: extracting attribute information A of the boundary points on the boundary line of the middle section ore body, including the project number and the terminal layer number;

[0058] Step 122: Obtain the coordinates of the ore body boundary points in the CAD mid-section map, and extract the CAD project number of each boundary point and the three-dimensional ore body attribute B of the ore body attributes;

[0059] Step 123: Identify the boundary points associated with the attribute information A established based on the single project and the ore body attribute B, determine the degree of offset between the boundary points associated with the middle section ore body boundary line and the CAD middle section map, adjust the boundary points corresponding to the drilling points of non-single projects according to the boundary points on the CAD middle section map, and obtain the closed ore body boundary line at the corresponding middle section level.

[0060] The drilling points of a single project are the actual detected points, so the boundary points corresponding to the drilling points of a single project are accurate boundary points. The boundary line of the middle section ore body is connected based on these accurate points. Therefore, the points between two single projects are inaccurate points. By adjusting the boundary points of non-single projects based on the CAD middle section map, a more accurate ore body boundary line can be obtained.

[0061] Specifically, in step 123, the method for adjusting the boundary points of the non-single project according to the boundary points on the CAD mid-segment drawing includes the following steps:

[0062] 1) For each boundary point that needs to be adjusted , calculate the boundary points The shortest distance to the boundary line C of the CAD middle section and determine the nearest point ;in, It is the boundary point on the boundary line of the CAD mid-section drawing;

[0063] 2) Use the weighted offset method to Towards Translation , the formula is:

[0064] ;

[0065] );

[0066] in, is the maximum adjustment distance set. Yes The shortest distance to the boundary line C;

[0067] 3) Set all points that have been fine-tuned { }, combined with the single project boundary point {Pk}, spline interpolation is used to fit each boundary point to generate a closed boundary to obtain the adjusted middle boundary line;

[0068] The above solution preserves the authenticity of single-project data, allowing non-project points to closely follow the forms drawn by human experience. It also avoids jagged or broken mid-segment boundaries, and avoids model deviations caused by data sparsity, mid-segment distortion, or simplification.

[0069] Step 13: All the ore body boundary lines in the middle section are fitted by triangulation according to the corresponding spatial data to generate a three-dimensional ore body model of the continuous ore body, thereby realizing the construction of the ore body spatial structure;

[0070] Specifically, the orebody boundary closing lines generated in each mid-segment are stacked in spatial order, and a continuous three-dimensional orebody solid model is constructed using the software's triangulation fitting function. Triangulation technology connects the spaces between the closing lines in a planar manner, forming a closed three-dimensional structure that conforms to the geological topography. This model not only contains real-world spatial coordinate information but also incorporates mineralization data from each project, enabling subsequent resource estimation, mineralization trend analysis, and target area prediction.

[0071] This embodiment proposes a method for collaborative modeling of single engineering data and mid-section maps, fully integrating spatial control engineering data such as drilling holes with the ore body boundary information in the mid-section CAD map to achieve high-precision reconstruction of the ore body's three-dimensional structure. By introducing an attribute fusion mechanism for multi-source data (such as A / B attribute overlay analysis), the spatial accuracy and morphological authenticity of the modeling are effectively improved. On the one hand, this method breaks away from the limitations of traditional reliance on two-dimensional graphics to subjectively infer three-dimensional structure, avoiding model deviations caused by data sparsity and distortion or simplification of mid-section maps. On the other hand, it achieves accurate and visual representation of the ore body, providing a reliable three-dimensional geological model foundation for subsequent mineralization target prediction, resource assessment, and mining design, significantly enhancing the model's engineering applicability and predictive credibility.

[0072] In step 1, based on the constructed three-dimensional ore body model, the following multi-dimensional and systematic analysis method is proposed to explore the spatial distribution pattern of the ore body, favorable prospecting clues and mineralization spatial enrichment characteristics inherent in the model. The method for analyzing the three-dimensional ore body model includes the following steps:

[0073] Step 101: Based on the three-dimensional ore body model, the spatial coordinates of the ore bodies at different locations are identified, the spatial extension distance of the ore bodies along the strike and dip directions is extracted, key structural parameters such as the lateral direction, lateral angle, and spacing between rich ore bodies of the ore bodies are identified and quantitatively described, and the first target area is determined based on the key structural parameters; the technical role of the key structural parameters in prospecting is shown in Table 1;

[0074] Table 1 Meaning and technical role of key structural parameters;

[0075]

[0076] Step 102: Compare the ore body models from the exploration phase and the development phase, analyze the spatial variations of key parameters such as ore body morphology, boundary, and mineralization intensity through model comparison, extract spatial variation data on ore body spatial morphology, boundary offset distance, mineralization intensity, and location of rich ore sections, and delineate a second target area based on the spatial variation quantitative data.

[0077] Among them, the quantitative data of the spatial morphological changes of the ore body can include volume difference, volume deformation ratio, main axis direction deviation angle, etc.; the changes in mineralization intensity can include grade change, thickness change, ore grade distribution standard deviation, rich ore volume change rate, etc.

[0078] Step 103: Superimpose the first target area and the second target area to obtain a third target area containing the two target areas, extract the attribute information of the alteration zone, the equal spacing recurrence rate of the rich ore section, and the lateral direction of the ore body in the third target area, adopt the normalized weighted scoring method, normalize each type of attribute and assign different weight values, perform weighted superposition calculation to obtain the score, output the predicted score value field in the three-dimensional ore body model, and generate the predicted hot zone map of the ore section through threshold cutting as the first potential mineralization area.

[0079] In step 2, construct expansion space simulation: obtain cross-section point set data, perform numerical analysis, and generate expansion space distribution map as the ore-bearing space layer, including the following steps:

[0080] Step 21: Obtain cross-section point set data including ore body distribution data extracted from the three-dimensional ore body model constructed in step 1:

[0081] Section point data is point data collected on multiple ore body sections, which may include ore body boundary points, structural line control points or ore-bearing points;

[0082] Specifically, the cross-section point set data may include three-dimensional coordinate data of the fracture surface, spatial position and occurrence information of the structural surface obtained from engineering cataloging, three-dimensional ore body modeling boundary point set, and historical geological data; among which the occurrence information includes the strike, dip and inclination information of the fracture surface;

[0083] Table 2 Data types and sources of constructed transect point sets;

[0084]

[0085] Next, we conduct cross-section data trend analysis through numerical processing (such as spatial interpolation, regression analysis, surface fitting, etc.) to construct an expansion space distribution map, which serves as the spatial basic layer for subsequent mineralization space prediction, that is, the mineralization space layer.

[0086] Step 22: Perform trend surface analysis on the cross-section point set, perform a trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure;

[0087] Step 23: Using geometric decomposition, the main waveform of the cross-section structure is decomposed into multiple basic waveform functions, and the waveform parameters of amplitude, wavelength and direction of each basic waveform are extracted. The contribution of all waveforms to the spatial response of each point on the cross-section is calculated, thus realizing the construction of the motion direction parameter.

[0088] Among them, the spatial response contribution is the contribution value of the displacement or fluctuation intensity caused by a unidirectional wave (with fixed direction, wavelength, and amplitude) propagating in space to a point P(x, y) on a certain section. ;

[0089] According to the principle of wave superposition and decomposition in physics, if the complex fracture surface waveform is composed of n unidirectional waves (cylindrical waves) with different directions, amplitudes, wavelengths and starting points, then for any point P (x, y) on the fracture surface, the unidirectional wave The spatial response contribution of for:

[0090] ;

[0091] in, One-way wave Amplitude; One-way wave The wavelength, One-way wave The propagation direction can be expressed in the ordinary coordinate system as:

[0092] ;

[0093] Where, Indicates the coordinates of the current section point P, is the coordinate of the starting point of the unidirectional wave.

[0094] The contribution of n unidirectional waves to the spatial response of point P is:

[0095] ;

[0096] Find out the waveform parameters of each unidirectional wave. If we have the waveform parameters of each unidirectional wave, the waveform function formula becomes a definite function.

[0097] Step 24: Based on the spatial response contributions of all waveforms in the combined simulation to each point on the cross section, the waveform parameters are substituted into the mathematical model to simulate the fracture deformation response under different tectonic movement directions, and a calculation model of the expansion thickness in three-dimensional space is obtained;

[0098] This step is to simulate the distribution of ore-bearing space generated when the fault moves in different directions based on the grid data of the known fracture surface waveform, and produce a simulated expansion space distribution map;

[0099] Substitute the waveform parameters into the mathematical model, where the mathematical model construction process is as follows:

[0100] Use function To approximately describe the waveform of the fracture surface in the direction of movement, when the fracture shear displacement distance is a, under the condition of non-compression of rock, the thickness of the ore-containing space can be approximately described as:

[0101] ;

[0102] Under the condition that the rock is completely compressible, its ore-holding space is:

[0103] ;

[0104] in, d(x) >0; They represent the waveform amplitudes under non-compression condition, compression condition, and initial state respectively, and A represents the amplitude;

[0105] The simulation of structural expansion space is based on the above theoretical analysis and extends the application of the above formula to the three-dimensional space, that is, using the cross-sectional waveform function of the known area Z(x,y) To replace the , simulate and calculate the spatial distribution of expansion when the fracture moves in different directions, and obtain the expansion thickness of the three-dimensional fracture surface:

[0106] Replace the formula for Z(x,y) :

[0107] ;

[0108] In a general sense, it means along a certain direction The calculation model of the expansion thickness is obtained by shearing:

[0109] ;

[0110] Step 25: Based on the obtained calculation model of the expansion thickness, the least square method is used to fit and solve the problem. The movement direction that best matches the actual situation is determined by minimizing the fitting error, and this direction is used as the movement direction of the structural fault.

[0111] Compare the orebody thickness distribution map and linear metal content distribution map output by the 3D modeling module to determine which direction of simulation best matches the actual orebody thickness or mineralization intensity distribution. This simulated movement direction is the fracture movement direction. Fitting parameters include expansion space, the distribution range of orebody grade, thickness, and metal content, anomaly shape, and intensity.

[0112] Step 26: Based on the obtained cross-sectional points P of the fracture motion direction, the spatial response contribution of the unidirectional wave obtained in step 23 to each cross-sectional point is used to connect the cross-sectional points P to obtain the cross-sectional outward extension structural morphology, i.e., the predicted cross-sectional waveform, thereby achieving the prediction of the geometric morphology;

[0113] Through the waveform decomposition in step 23, the waveform parameters of each wave in the expression of the waveform function Z(x, y) have been obtained. Z(x, y) has become a fixed function. Given the coordinates of any point P(x, y) on the projection plane, its function value can be obtained. Determine the coordinate range of the deep prediction, calculate the function value at each point on the cross section, and draw a waveform diagram of the predicted deep fracture surface to obtain the expansion space of the cross section.

[0114] Step 27: Generate a capacity expansion space distribution map: extract the obtained cross-sectional waveform map into a spatial model as a ore-bearing space layer;

[0115] Specifically, according to the fault movement direction parameters obtained in step 25 and based on the peripheral fracture surface waveform data synthesized in step 26, the fault movement is simulated, the relative expansion space is calculated, and the deep expansion space distribution map is drawn.

[0116] Verification of the effectiveness of expanded space simulation predictions. When expanded space simulation technology encounters dislocation or large-angle inflection in the cross section, its geological parameter settings and the determination of the vertical distance of the primary trend surface are affected by sudden changes in geological conditions, affecting the prediction results. Directly implementing engineering to verify the effectiveness of expanded space mineralization simulation is somewhat blind, increasing the risk and investment of prediction.

[0117] In highly active mining areas or producing mines, a small number of previously constructed prospecting projects are located outside the densely populated areas, providing favorable conditions for verifying the rationality of parameter settings and the effectiveness of the technology. By simulating the spatial location and distribution characteristics of the expansion and the degree of fit between them and the ore bodies revealed by existing prospecting projects, the effectiveness of the technology in the study area was evaluated.

[0118] Step 3: Obtain geochemical data of the sampling area, perform three-dimensional metallogenic trend prediction, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in deep and shallow areas, obtain each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction results of blind ore bodies. The three-dimensional prediction process of blind ore bodies includes the following steps:

[0119] Step 31, mineralization element testing and analysis: obtaining the content values ​​of multiple chemical elements in the sampling area and performing preprocessing;

[0120] Specifically, sampling is carried out in the mining area, and the chemical composition of the samples is analyzed to obtain the results of multi-element geochemical anomaly distribution. The geochemical data in the sampling area are analyzed to analyze the content values ​​of multiple elements. The original element content data are then normalized to eliminate the interference of different dimensions and scales on subsequent clustering and comparison, and a standardized multi-element spatial database is constructed to prepare for subsequent clustering modeling.

[0121] Step 32: clustering the obtained multi-chemical element content values ​​to identify combined abnormal factors;

[0122] Optionally, R-type cluster analysis can be used to statistically analyze the affinity between elements and identify possible metallogenic assemblage structures;

[0123] Specifically, the correlation coefficient is calculated and analyzed by setting thresholds. The first threshold can be set to identify the combination of chemical elements with potential kinship, and the second threshold can be set to identify the strongly correlated elements.

[0124] At the level of correlation coefficient of 0.4, preliminary element groups were divided to identify chemical elements with potential affinity; at the level of correlation coefficient of not less than 0.8, strongly correlated elements were screened to further judge the geological rationality of the combination;

[0125] The cluster analysis results of this step can reflect the affinity between elements and the possible mineralization combination structure, providing a basis for the subsequent identification of combination anomaly areas.

[0126] This example performs an R-type cluster analysis on the content values ​​of each element, groups the elements at a correlation level of 0.4, identifies strongly correlated elements at a correlation level of 0.8, and conducts a geological rationality analysis of the combination of strongly correlated elements, thereby reflecting the close relationship between the elements. This provides a rough reference for studying the distribution of each factor and discovering its implicit geological background information, and also provides a reference basis for the subsequent element classification of combination anomalies.

[0127] Step 33, determining the lower limit of the eigenvalue statistical anomaly band: performing frequency statistics and cumulative distribution calculations on the sample values ​​of each element, and using the threshold method to divide the inner, middle, and outer band boundaries of the single element anomaly for subsequent anomaly layer drawing and zoning;

[0128] Optionally, the concentration thresholds can be set to 85%, 92%, and 98%, and the concentration values ​​of each element at the 85%, 92%, and 98% quantiles can be extracted. "Single element anomaly" refers to the abnormal enrichment of a chemical element (such as gold, copper, arsenic, antimony, etc.) at a certain sampling point or spatial region in geochemical exploration, where its content is significantly higher than the regional background value or the normal concentration range in the statistical distribution. The anomaly in this embodiment refers to abnormal element enrichment.

[0129] Step 34: Position and add the standardized element sample concentration values ​​to the three-dimensional ore body model of step 1, assign single element content, and circle the outer, middle, and inner abnormal zones of the single element based on the obtained zoning boundaries to obtain a single element anomaly map, also called a single element anomaly distribution map;

[0130] Using the aforementioned zoning boundaries, the spatial characteristic parameters of single-element anomalies can be analyzed and statistically analyzed through single-element anomaly maps, including: anomaly location distribution, number and area of ​​anomalies, intensity level and zoning characteristics;

[0131] Step 35: Superimpose the single element anomaly map with the known ore body model in three dimensions, analyze the spatial relative position relationship between the single element anomaly area and the ore body, and classify each combined section in the single element anomaly map and mark the superposition halo type based on the obtained combined anomaly factor and the metallogenic geochemical halo zoning pattern, and construct a superposition halo model;

[0132] Specifically, the single-element anomaly layer generated in the previous step is imported into the 3D modeling system; it is superimposed in 3D space with the known ore body model to analyze the spatial relative position relationship between the anomaly area and the ore body, such as whether it is located above, below, around, or on both sides of the ore body; whether it is in a ring-shaped, feather-shaped, or wedge-shaped geometric relationship;

[0133] Through these geometric relationships, we can determine the types of these abnormal areas, including:

[0134] Front halo: mineralization precursor reaction;

[0135] Near-ore halo: halo associated with the mineralization process;

[0136] Tail halo: residual abnormal or escaped components after mineralization.

[0137] Furthermore, the element combination factors identified by R-type cluster analysis were introduced into the analysis:

[0138] For example, Au-As-Sb is a combination factor that is commonly found at hydrothermal mineralization fronts; the Cu-Pb-Zn-Mo combination may appear in deep or peripheral mineralized environments; the spatial distribution of these combination factors is matched with the anomalous areas to determine which areas show obvious combination responses.

[0139] Furthermore, the metallogenic geochemical halo zoning model further confirms the divided superimposed halo type. Specifically, the existing metallogenic geochemical superimposed halo zoning model in this area or similar mining areas can be referred to, such as:

[0140] Leading halo: outside the ore body, with medium outliers and mostly highly mobile elements;

[0141] Proximal halo: Close to the edge of the ore body, it has the highest element enrichment and is the most direct indicator of ore prospecting.

[0142] Trailing halo: It may be in the footwall of the ore body or away from the ore body, with sparse composition and weak abnormal continuity;

[0143] According to the characteristics of these halo bands, each combined segment in the abnormal layer is classified and marked.

[0144] Based on the above spatial superposition analysis, element combination factor identification, and regional metallogenic experience, the anomaly information of the entire mining area is modeled as a structured structural superposition halo model, which includes the following information: which element combinations correspond to each type of halo (front / tail / near-ore); in which direction of the ore body they appear in space; how the intensity and distribution of element anomalies evolve;

[0145] Based on the superposition halo model constructed above, it is possible to identify anomalies with classification labels in three-dimensional space, the main controlling element combination of each type of anomaly, and the position relationship with the ore body (relative direction, elevation, distance, etc.);

[0146] Step 36: Calculate the comprehensive index of the combined anomaly body based on the single element anomaly map and the cluster combination characteristics, use the superposition algorithm to fuse the related element anomaly maps, identify the combined anomaly area, and construct a combined anomaly layer, also called a combined anomaly distribution map;

[0147] Among them, the comprehensive indicators of combined anomalies can include spatial overlap, peak intensity distribution, and zonal transition characteristics.

[0148] Step 37: Based on the superimposed halo model obtained in step 35, perform type judgment and spatial classification on the combined abnormal area to obtain a type classification layer;

[0149] Specifically, according to the elemental composition, spatial distribution and typical halo characteristics of the abnormal response, they are classified as: leading halo (far away from the periphery of the ore body), tail halo (inside the ore body or residual reaction zone) or near-ore halo (surrounding the core area of ​​the ore body);

[0150] Step 38: Superimpose the single element anomaly map, the combined anomaly layer, and the type classification layer to obtain the anomaly layers of the superimposed halo three-dimensional model. Superimpose the anomaly layers with the three-dimensional ore body model to obtain the three-dimensional relative spatial relationship with the ore body. Infer the potential mineralization extension direction based on the superimposed halo type, delineate the possible mineralization area, and obtain the blind ore body three-dimensional prediction result.

[0151] Step 4: Superimpose the abnormal layers of the superimposed halo stereo model, the three-dimensional ore body model and the structural expansion space model. The area where all three overlap is the area with the highest mineralization probability, which is determined as the target area spatial location information;

[0152] Furthermore, for the overlapping areas obtained after superposition, the empirical weight and information method are used to delineate the target areas and classify the mineralization target areas, which specifically includes the following steps:

[0153] Step 41, setting variables: according to the information of each abnormal layer of the superimposed halo three-dimensional model, the three-dimensional ore body model and the structural expansion space model, set the prediction variables and assign values, and set the empirical weights;

[0154] This embodiment sets 18 variables, as shown in Table 3;

[0155] Table 3 sets the predictor variables;

[0156]

[0157] In Table 3, the basis for setting these empirical values ​​is determined based on the information of each abnormal layer of the three-dimensional ore body model, structural expansion space model and superimposed halo three-dimensional model obtained from steps 1 to 3. The empirical values ​​are values ​​set based on experience, and the mineralization possibility of different prospecting information is obtained by the coupling relationship between the known ore body and geological information, expansion space information and superimposed halo information.

[0158] Variables were assigned using two-state and three-state assignment methods (see Table 3). In two-state assignment, the variable's state is considered: a "1" is assigned if the state exists; a "0" is assigned if the state does not exist or if the information is unclear. The three-state assignment principle assigns values ​​of "1," "0," or "-1," depending on the variable's relationship to gold mineralization. However, during the specific assignment process, attention was paid to the statistical significance of the states, ensuring that the values ​​obtained have statistical significance.

[0159] As mentioned above, the relationship between different predictive markers and gold mineralization varies significantly. Using an expert approach combined with prospecting experience, we assign weights to variables based on their role in gold deposit prediction, which can more reasonably reflect their importance. The empirical weights of the variables in the study area are shown in Table 3.

[0160] Step 42, target area delineation: multiply the scores of each set variable of the prediction block by the weight, and obtain the information score of the prediction block after weighting, and classify the obtained target area according to the obtained information score;

[0161] Step 421: Calculate the information content of the prediction block.

[0162] The predicted blocks are scored based on their respective prospecting indicators. The sum of the block's variable scores and the weights is the block's information score. The cumulative frequency method can intuitively divide the information value into three levels: <5, 5-10, and >10.

[0163] Step 422, target area delineation: According to the prediction results, the higher the information value, the greater the possibility of mineralization. Therefore, high-value areas of prospecting information (information value>10) are delineated outside the existing project scope, and areas with dense distribution of high-value blocks of information are regarded as comprehensive geological anomaly areas.

[0164] According to the calculation results of the block information volume, the target areas are divided into three A-level target areas (score > 10 points), one B-level target area (score 5-10 points) and two C-level target areas (score < 5 points). The target area variable assignment and score reflect the mineralization potential and prospecting prospects of the target area, see Table 4 for details.

[0165] Table 4 Example of target area circle connection;

[0166]

[0167] Through the above process, the mineralization target areas GBQ1 to GBQ6 are divided into three levels. When conducting field sampling verification, they can be confirmed according to the levels, which improves the efficiency of mineralization prediction.

[0168] To illustrate the above process and effect of this embodiment, take the actual Jiaodong gold mine as an example, and conduct prospecting for the Guandao gold mine in the Guandao section of the Jiaodong gold mine. The above process is implemented to achieve prospecting and accurate mineralization prediction, as described below.

[0169] (1) Construction of three-dimensional ore body model; Figure 4 As shown in the figure, models for the deep ore bodies (red blocks) from the third exploration phase and the ore bodies controlled by the infill mining project were established. The red blocks represent known ore bodies. Based on the ore body connection results from the exploration phase, the ore bodies controlled by the infill mining project are mainly distributed between -260m and -500m and -660m and -740m.

[0170] Geological analysis of the 3D orebody model: Identifying the lateral pattern of the orebody. The 3D orebody model shows that the Guandao section of the Jiaojia tectonic belt has an overall strike of 60°, with an lateral dip of 303° and a southwest deflection. The main orebodies of various deposits in the Jiaojia gold belt have similar lateral directions and angles, ranging from 260° to 300° and from 30° to 60°, indicating that they formed within a common, unified tectonic stress field.

[0171] The three-dimensional ore body model analysis re-understands the weak mineralization area. Comparing the ore body model established based on the exploration report and the mine infill control project, there are differences in the ore body boundaries. In the exploration drill hole 8ZK3 ( Figure 4 Thick gold deposits are developed in the yellow area (marked in the middle). During the mining exploration process, the ore bodies found are different from those submitted in the exploration report in terms of shape, scale and occurrence: the thick and rich ore bodies exposed by drilling are all verified to exist, but the thin and poor ore bodies exposed by drilling are often newly discovered in the surrounding areas, such as those with large-scale structural belts, high alteration intensity and obvious zoning. Figure 4 The three-dimensional ore body model (3D model) in the analysis and understanding focused on the weak mineralized interval between the shallow and deep sections of the main ore body. It was believed that the mineralization potential of the interval section where the 271ZK6 drill hole is located is questionable and needs to be re-evaluated.

[0172] Research into the previously documented drilling data for the weakly mineralized intervals revealed that the alteration zone extends into the weakly mineralized intervals, with no significant decrease in scale and intensity zoning. Furthermore, the weakly mineralized intervals are located in the lateral extension of the deep and shallow ore sections. Combined with the mineralization shown in the previous exploration drill holes, the re-evaluation of the weakly mineralized intervals is considered to have high research significance and prospecting value. A small number of weakly mineralized holes previously drilled may represent localized depletion within the rich ore section.

[0173] (2) Constructing expansion space simulation information;

[0174] Simulation of tectonic movement: The trend analysis of cross-section data shows that the overall trend of the ore belt is 240°. This is used as the new coordinate axis for coordinate changes. The expansion space distribution pattern when the fault moves in different directions is simulated. By comparing the metal content contour map of the ore body, it is concluded that the left-lateral tension-torsion tectonic movement of the upper plate moving to the lower left direction can produce the corresponding expansion space distribution pattern, such as Figure 5 As shown;

[0175] Expansion space simulation: The coordinate range of deep prediction is determined to be -1100m to 0m elevation from line 239 to line 300, and deep cross-section waveform and expansion space simulation are performed to finally obtain the expansion space distribution map of the prediction area, and circle the six expansion space prediction target areas KR1 to KR6, as shown in the figure. Figure 6 As shown;

[0176] Prediction of expanded space simulation information: By comparing the results of expanded space simulation, it is concluded that the movement direction of the ore-controlling fault in the main mineralization stage is left-lateral sliding, which is consistent with the field observation results; it is also consistent with the lateral characteristics of the ore body reflected by the three-dimensional ore body model; it is consistent with the characteristics of regional tectonic movement, that is, the activity of the main fault should belong to left-lateral normal fault movement.

[0177] To verify the validity of the prediction, we collected drilling data from exploration lines 239 and 247 in the adjacent Xincheng mining area and compared the ore findings with the predicted spatial distribution of expansion.

[0178] On exploration line 239, drill holes ZK239-8 and ZK239-12 revealed gold deposits grading 13.21g / t over 4.25m thick and 4.92g / t over 6.53m thick, respectively. The two projects controlled ore bodies at elevations between -420m and -580m. Deeper drill holes ZK239-16, 20, and 26 controlled mineralized zones at -700m, -820m, and -970m, respectively. However, no ore bodies were discovered, except for a 1.45m-thick ore body grading 3.14g / t in drill hole ZK239-20.

[0179] Expansion space prediction map ( Figure 6), the 239 exploration line is located at the edge of the map area, the anomaly is not enclosed, and there is an expansion zone at -520m to -620m, which is consistent with the location of the ore discovery; -280m to -420m is a low-gentle positive value zone, which is a weak expansion zone, and -420m to -520m is a compression zone. It is speculated that there may be a weak mineralization discontinuity between the two ore-discovering drill holes; -790m to -820m and -940m to -980m are low-gentle positive value zones. The ZK239-20 small ore body is found in the former, but no ore is found in the latter, which also shows that the mineralization potential of the weak expansion zone is limited.

[0180] (3) Constructing stereo prediction information of stacked halo;

[0181] Eigenvalue calculation: Calculate the geochemical characteristics of 19 elements including Au, Ag, Cu, Pb, Zn, As, Sb, Hg, B, Ba, Bi, Mo, Mn, Co, Ni, V, Ti, W, and Sn in the Guandao gold mine;

[0182] Calculation of anomaly zoning standards and drawing of single element geochemical maps;

[0183] The lower limit of the anomaly is calculated by cumulative frequency method. According to the content value of each element, the cumulative frequency of 85%, 92% and 98% are used as the three-level concentration zones of abnormal outer, middle and inner respectively. The abnormal zone standards of each element are shown in Table 5. The single element geochemical map of the structural superposition halo is drawn, as shown in Figure 7 As shown, part a is a single element anomaly map, and the right side b area is the R-type cluster analysis tree diagram;

[0184] Table 5: Standards for the outer, middle, and inner zones of elements;

[0185]

[0186] Tectonic superimposed halo single element geochemical map ( Figure 7 ) shows that: with the gold ore body as the center, the concentration of Au decreases gradually upward, to the sides and downward, and Ag is positively correlated with Au, and has similar characteristics. Au and Ag are near-ore indicator elements; Cu, Pb, Zn, anomalies reflect the superposition of the II and III mineralization stages, and are mostly positively correlated with gold, and are near-ore indicator elements; As, Sb, Hg, B, and Ba are strongly anomalies mostly distributed in the upper part of the ore body and the leading halo, and are characteristic indicator elements of the leading halo; Bi, Mo, Mn, Co, Ni, V, and Ti are strongly anomalies mostly distributed in the lower part of the ore body and the tail halo, and are characteristic indicator elements of the tail halo.

[0187] Then, through correlation analysis, a superimposed halo stereo model is constructed, such as Figure 8As shown, each abnormal chemical element is marked. When the element anomalies of the tail halo and the near-ore halo of the ore body are superimposed, it indicates that the ore body extends to the shallow part, while the superposition of the element anomalies of the front halo and the near-ore halo of the deep ore body indicates that the ore body extends to the depth. The structural superposition halo single element anomalies of the Guandao gold mine are well matched, and there are 5 comprehensive anomalies ZH1-ZH5.

[0188] Will Figure 4 、 Figure 6 and Figure 8 Superposition to obtain Figure 9 , which is a large-scale structural mineralization prediction vertical projection map of the Guandao gold deposit; three A-level target areas were obtained, and the favorable mineralization position of the A1 target area is located between lines 263 and 295, with an elevation of -480m to -630m ( Figure 9 The combined anomalies of three superimposed halo elements, ZH1, ZH2, and ZH3, can fully reflect the characteristics of the ore body. The favorable mineralization area in the A2 target area is located between lines 255 and 263, with an elevation of -250m to -120m. Structural expansion space is found near the elevations of -200m on lines 255 and 263. The lower near-ore halo and tail halo are well developed, and the ore body extends shallowly, indicating that the shallow part of the anomaly has good prospecting prospects. The A3 target area, GBQ3, is located around the known ore body and may extend northeastward along strike to depth. The favorable mineralization area is located northeast of line 247, with an elevation below -700m. The shallow part extends toward the Xincheng mining area, representing the discovered line 239 ore body. For the above-mentioned Class A target areas, engineering verification was gradually carried out. After exploration engineering verification, three Class A target areas were revealed, and industrial ore bodies were revealed in all of them, with a thickness of 9.20m to 26.00m and an average grade of 1.68g / t to 4.29g / t. This shows that this embodiment integrates three-dimensional ore body modeling, structural expansion space simulation and structural superposition halo geochemical anomaly analysis, and significantly improves the accuracy and prediction efficiency of mineralization target area determination through three-dimensional integration and cross-validation of multi-source data.

[0189] Example 2

[0190] Based on Example 1, this embodiment provides 8. a three-dimensional gold mineralization prediction system based on structural information fusion, including:

[0191] The 3D ore body model construction module is configured to use a method that mainly uses engineering modeling and supplemented by mid-section map modeling to construct a 3D ore body model, perform 3D ore body model analysis, and delineate the first potential mineralization area;

[0192] The expansion space simulation module is configured to obtain cross-section point set data, perform numerical analysis, and generate an expansion space distribution map as a ore-bearing space layer;

[0193] The stacking halo construction module is configured to obtain geochemical data of the sampling area, perform three-dimensional metallogenic trend prediction, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the stacking halo three-dimensional model, and obtain the three-dimensional prediction results of blind ore bodies;

[0194] The superposition module is configured to superimpose the first potential mineralization area, the mineralization-bearing space layer and the blind ore body stereo prediction results, and obtain the predicted area of ​​gold mineralization according to the degree of overlap.

[0195] Furthermore, the building blocks of the three-dimensional ore body model include:

[0196] The single-project ore body segment boundary delineation module is configured to obtain the single-project data in the constructed ore body engineering database to circle the single-project ore body, identify the single-project ore body segment based on the set circle index, and determine the top and bottom boundaries;

[0197] The single-project ore body connection module is configured to sequentially connect the top and bottom boundaries of the corresponding ore bodies of adjacent single projects to obtain the boundary line of the middle ore body;

[0198] a correction module configured to obtain a CAD mid-section map of each mid-section, perform morphological adjustment and spatial correction on the boundary lines of the mid-section ore body obtained by engineering modeling with reference to the CAD mid-section map, thereby optimizing the boundary lines and forming a set of closed ore body boundary lines at each mid-section level;

[0199] The fitting module is configured to fit all the ore body boundary lines in the middle section through triangulation according to the corresponding spatial data to generate a three-dimensional ore body model of the continuous ore body.

[0200] Furthermore, the space simulation module is expanded to include:

[0201] The cross-section point set data acquisition module is configured to acquire cross-section point set data including ore body distribution data extracted from the three-dimensional ore body model:

[0202] The main waveform construction module is configured to perform trend surface analysis on the cross-section point set, perform a trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure;

[0203] The calculation module is configured to use geometric decomposition to decompose the main waveform of the cross-section structure into multiple basic waveform functions, extract the waveform parameters of the amplitude, wavelength and direction of each basic waveform, and calculate the contribution of all waveforms to the spatial response of each point on the cross-section;

[0204] The computational model building module is configured to simulate the fracture deformation response under different tectonic movement directions based on the spatial response contribution of all waveforms simulated in combination to each point on the fracture surface, and obtain a computational model of the expansion thickness in three-dimensional space;

[0205] The solution module is configured to use the least square method to fit the calculation model of the obtained expansion thickness, and determine the movement direction that best matches the actual situation by minimizing the fitting error, and use it as the movement direction of the structural fault;

[0206] The predicted waveform determination module is configured to connect the cross-sectional points P based on the obtained fracture movement direction, using the spatial response contribution of the unidirectional wave to each cross-sectional point, to obtain the cross-sectional waveform diagram of the outward extension structure of the cross-sectional surface;

[0207] The ore-bearing space generation module is configured to extract the obtained cross-sectional waveform diagram into a spatial model as a ore-bearing space layer.

[0208] It should be noted here that the various modules in this embodiment correspond one-to-one to the various steps in Example 1, and the specific implementation processes are the same, which will not be repeated here.

[0209] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0210] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A three-dimensional prediction method for gold mineralization based on structural information fusion, characterized by: The steps include: Using engineering modeling as the main method and mid-section diagram modeling as the auxiliary method, a three-dimensional ore body model was constructed, and the three-dimensional ore body model was analyzed to delineate the first potential mineralization area; Obtain cross-section point set data, perform numerical analysis, and generate a capacity expansion space distribution map as a ore-capacity space layer; Obtain geochemical data of the sampling area, conduct three-dimensional prediction of mineralization trends, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the superimposed halo three-dimensional model, and obtain three-dimensional prediction results of blind ore bodies; The first potential mineralization area, the ore-bearing space layer and the three-dimensional prediction results of the blind ore body are superimposed, and the predicted area of ​​gold mineralization is obtained according to the degree of overlap.

2. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 1, characterized in that: The method for constructing a three-dimensional ore body model includes the following steps: Obtain the single-project data in the constructed ore body engineering database to circle the single-project ore body, identify the single-project ore body segment based on the set circle index, and determine the top and bottom boundaries; Connect the top and bottom boundaries of the ore bodies corresponding to adjacent single projects in sequence, and circle them to obtain the boundary line of the middle ore body; Obtain the CAD mid-section map of each mid-section, and perform morphological adjustment and spatial correction on the boundary lines of the mid-section ore body obtained by engineering modeling with reference to the CAD mid-section map to optimize the boundary lines and form a set of closed ore body boundary lines at each mid-section level; The boundary lines of all middle sections of the ore bodies are fitted through triangulation according to the corresponding spatial data to generate a three-dimensional ore body model of the continuous ore body.

3. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 2, characterized in that: The method for performing morphological adjustment and spatial correction on the boundary line of the middle section ore body obtained by engineering modeling with reference to the CAD drawing comprises the following steps: Extract the attribute information A of the boundary point on the boundary line of the middle section ore body, including the project number and terminal layer number; Automatically read the coordinates of the ore body boundary points in the CAD mid-section map, extract the CAD project number of each boundary point, and the three-dimensional ore body attribute B of the ore body attributes; Identify the boundary points associated with the attribute information A established based on a single project and the ore body attribute B, determine the degree of offset between the boundary line of the middle section ore body and the boundary points associated with the CAD middle section map, adjust the boundary points corresponding to the drilling points of non-single projects according to the boundary points on the CAD middle section map, and obtain the closed ore body boundary line at the corresponding middle section level.

4. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 3 is characterized in that: The method for adjusting the boundary points corresponding to the drilling points of non-single projects according to the boundary points on the CAD mid-section drawing includes the following steps: For each boundary point to be adjusted , calculate the boundary points The shortest distance to the boundary line C of the CAD middle section and determine the nearest point ; The weighted offset method is used to Towards Translation ; The set of all points that have been fine-tuned is { }, combined with the single project boundary point {Pk}, spline interpolation is used to fit each boundary point to generate a closed boundary to obtain the adjusted middle boundary line.

5. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 1, characterized in that: Obtain cross-section point set data, perform numerical analysis, and generate a capacity expansion space distribution map as a ore-capacity space layer, including the following steps: Obtain cross-section point set data including ore body distribution data extracted from a 3D ore body model: Perform trend surface analysis on the cross-section point set, perform a trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure; Using geometric decomposition, the main waveform of the cross-section structure is decomposed into multiple basic waveform functions, the waveform parameters of amplitude, wavelength and direction of each basic waveform are extracted, and the contribution of all waveforms to the spatial response of each point on the cross-section is calculated; Based on the spatial response contribution of all waveforms simulated in the combined simulation to each point on the cross section, the fracture deformation response under different tectonic movement directions is simulated to obtain a calculation model of the expansion thickness in three-dimensional space; Based on the calculation model of the expansion thickness, the least square method is used to fit the solution, and the movement direction that best matches the actual situation is determined by minimizing the fitting error, which is used as the movement direction of the tectonic fault. For each cross-section point of the obtained fracture movement direction, the spatial response contribution of the unidirectional wave to each cross-section point is used to connect each cross-section point P to obtain the cross-section outward extension structure morphology, that is, the predicted cross-section waveform diagram; The obtained cross-sectional waveform diagram is extracted into a spatial model as a ore-bearing space layer.

6. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 1, characterized in that: The blind ore body stereo prediction process includes the following steps: Obtain the content values ​​of multiple chemical elements in the sampling area and perform preprocessing; Cluster the obtained multi-chemical element content values ​​and identify combined abnormal factors; The frequency statistics and cumulative distribution calculations were performed on the sample values ​​of each element, and the threshold method was used to divide the inner, middle and outer bands of single element anomalies; The standardized element sample concentration values ​​are added to the three-dimensional ore body model. Based on the obtained zoning boundaries, the outer, middle and inner zone abnormal areas of the single element are circled to obtain the single element anomaly map. The single element anomaly map is superimposed on the known ore body model in three dimensions, and the spatial relative position relationship between the single element anomaly area and the ore body is analyzed. Based on the obtained combined anomaly factors and the metallogenic geochemical halo zoning pattern, each combined section in the single element anomaly map is classified and the superposition halo type is marked to construct a superposition halo model. Based on the single element anomaly map and cluster combination characteristics, the comprehensive index of the combined anomaly body is calculated, and the related element anomaly maps are fused using the superposition algorithm to identify the combined anomaly area and construct the combined anomaly layer. According to the obtained superposition halo model, the combined anomaly area is judged and spatially classified to obtain a type classification layer; The single-element anomaly map, combined anomaly layer and type classification layer are uniformly superimposed to obtain the anomaly layers of the superimposed halo three-dimensional model. The anomaly layers of the superimposed halo three-dimensional model are superimposed with the three-dimensional ore body model to obtain the three-dimensional relative spatial relationship with the ore body. According to the superimposed halo type, the potential mineralization extension direction is inferred, the possible mineralization area is delineated, and the blind ore body three-dimensional prediction result is obtained.

7. The three-dimensional prediction method for gold mineralization based on structural information fusion according to claim 1, characterized in that: The abnormal layers of the stacked halo stereo model, the three-dimensional ore body model and the structural expansion space model are superimposed. For the overlapping areas obtained after superposition, the empirical weight and information method are used to delineate the target areas and classify the mineralization target areas, including the following steps: According to the information of each abnormal layer of the superimposed halo stereo model, the three-dimensional ore body model and the structural expansion space model, the prediction variables are set and assigned, and the empirical weights are set; The scores of each set variable of the prediction block are multiplied by the weights, and the information score of the prediction block is obtained after weighting. The obtained target areas are graded according to the obtained information score.

8. The three-dimensional prediction system of gold mineralization based on structural information fusion is characterized by: include: The 3D ore body model construction module is configured to use a method that mainly uses engineering modeling and supplemented by mid-section map modeling to construct a 3D ore body model, perform 3D ore body model analysis, and delineate the first potential mineralization area; The expansion space simulation module is configured to obtain cross-section point set data, perform numerical analysis, and generate an expansion space distribution map as a ore-bearing space layer; The stacking halo construction module is configured to obtain geochemical data of the sampling area, perform three-dimensional metallogenic trend prediction, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in deep and shallow areas, determine the abnormal layers of the stacking halo three-dimensional model, and obtain the three-dimensional prediction results of blind ore bodies; The superposition module is configured to superimpose the first potential mineralization area, the mineralization-bearing space layer and the blind ore body stereo prediction results, and obtain the predicted area of ​​gold mineralization according to the degree of overlap.

9. The three-dimensional gold mineralization prediction system based on structural information fusion according to claim 8, characterized in that: The building blocks of the 3D orebody model include: The single-project ore body segment boundary delineation module is configured to obtain the single-project data in the constructed ore body engineering database to circle the single-project ore body, identify the single-project ore body segment based on the set circle index, and determine the top and bottom boundaries; The single-project ore body connection module is configured to sequentially connect the top and bottom boundaries of the ore bodies corresponding to adjacent single projects to obtain the boundary line of the middle ore body; a correction module configured to obtain a CAD mid-section map of each mid-section, perform morphological adjustment and spatial correction on the boundary lines of the mid-section ore body obtained by engineering modeling with reference to the CAD mid-section map, thereby optimizing the boundary lines and forming a set of closed ore body boundary lines at each mid-section level; The fitting module is configured to fit all the ore body boundary lines in the middle section through triangulation according to the corresponding spatial data to generate a three-dimensional ore body model of the continuous ore body.

10. The three-dimensional gold mineralization prediction system based on structural information fusion according to claim 8, characterized in that: Expanded space simulation module, including: The cross-section point set data acquisition module is configured to acquire cross-section point set data including ore body distribution data extracted from the three-dimensional ore body model: The main waveform construction module is configured to perform trend surface analysis on the cross-section point set, perform a trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure; The calculation module is configured to use geometric decomposition to decompose the main waveform of the cross-section structure into multiple basic waveform functions, extract the waveform parameters of the amplitude, wavelength, and direction of each basic waveform, and calculate the contribution of all waveforms to the spatial response of each point on the cross-section; The computational model building module is configured to simulate the fracture deformation response under different tectonic movement directions based on the spatial response contribution of all waveforms simulated in combination to each point on the fracture surface, and obtain a computational model of the expansion thickness in three-dimensional space; The solution module is configured to use the least square method to fit the calculation model of the obtained expansion thickness, and determine the movement direction that best matches the actual situation by minimizing the fitting error, and use it as the movement direction of the structural fault; The predicted waveform determination module is configured to connect the cross-sectional points P based on the obtained fracture movement direction, using the spatial response contribution of the unidirectional wave to each cross-sectional point, to obtain the cross-sectional waveform diagram of the outward extension structure of the cross-sectional surface; The ore-bearing space generation module is configured to extract the obtained cross-sectional waveform diagram into a spatial model as a ore-bearing space layer.

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