Gold ore metallogenic three-dimensional prediction method and system based on construction information fusion

Abstract

The invention relates to the technical field of geological exploration, provides a gold ore metallogenic three-dimensional prediction method and system based on structure information fusion, and breaks through the limitation that a traditional three-dimensional modeling method stops in the aspect of geometric description through fusion processing of multi-source data such as geological structures, ore body evolution processes and geochemical anomalies. Ore control structure information is fused with a geochemical trend and a space structure for the first time, a structure-geochemistry-ore body form collaborative prediction mechanism is realized, and blind ore recognition and peripheral prospecting capabilities are effectively enhanced. Meanwhile, a target region division mechanism based on multi-source layer overlapping is constructed, the credibility and precision of a prediction result are remarkably improved, and the prospecting requirement of a high-work-degree gold mining area is met. By adopting a mode of combining engineering modeling and mid-segment graph modeling, space errors generated by traditional interpolation modeling in a data sparse or form complex area are effectively avoided, and the authenticity and continuity of an ore body structure are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and specifically, to a three-dimensional gold ore-forming prediction method and system based on the fusion of structural information. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] With the continuous increase in the depth and intensity of mineral resource exploitation, the shallow easily mined ore bodies are gradually exhausted, and the prospecting work is increasingly extended to deep and concealed ore bodies. Under this background, how to improve the accuracy and efficiency of ore-forming prediction has become a key issue in mineral resource exploration. The three-dimensional ore-forming prediction technology has become an important means for current deep prospecting and shows good application prospects in multiple ore concentration areas.

[0004] In gold ore mining areas with a high level of work or mines that have entered the production stage, a large number of engineering operations such as drilling and roadways have usually been completed, accumulating extremely rich original geological information such as structures, mineralizations, and alterations. Facing such high-density and high-value data resources, how to make full use of them to carry out more targeted and accurate ore-prospecting prediction directly serving mine production prospecting has become the key direction of current technological development. Traditional gold ore-forming prediction methods mostly focus on indirect inference through geophysical and geochemical anomalies. However, in mining areas with a high level of work and dense engineering coverage, the applicability and accuracy of such methods have gradually been challenged.

[0005] When prospecting for ore in gold ore mining areas with a high level of work or in production, the existing three-dimensional ore-forming prediction methods still have significant limitations and are difficult to fully support target area identification and resource volume assessment under complex ore-forming backgrounds. On the one hand, interpolation algorithms (such as Kriging interpolation, inverse distance weighting, etc.) are generally used in the three-dimensional modeling process to construct ore body models, which rely on the integrity and uniformity of preprocessed data and are prone to spatial errors in data-sparse or morphologically complex areas, resulting in insufficient authenticity of ore body structures; especially in areas with sudden changes in ore body morphology and severe fractures, it is difficult for the interpolation model to capture key structural features. On the other hand, existing methods often directly use the single constructed three-dimensional model for ore-forming prediction, resulting in fragmented prediction judgment logic, low reliability in target area delineation, and poor interpretability of prediction results, making it difficult to meet the new round of prospecting requirements in mining areas with a high level of work. 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 ore formation based on structural information fusion, which is applicable to large-scale structural information metallogenic prediction in high-workload gold ore areas. It conducts fusion processing on multi-source data such as geological structures, ore body evolution processes, and geochemical anomalies, innovatively integrates three-dimensional ore body modeling, structural dilation space simulation, and structural superimposed halo geochemical anomaly analysis. Through three-dimensional integration and cross-validation of multi-source data, it significantly improves the accuracy and prediction efficiency of ore-forming target area determination, breaks through the technical bottlenecks of traditional two-dimensional prediction and empirical judgment, and has good engineering applicability and promotion value.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions: The three-dimensional prediction method for gold ore formation based on structural information fusion in the first aspect of the present invention includes the following steps: Adopt the method of mainly using engineering modeling and supplemented by intermediate section drawing modeling to construct a three-dimensional ore body model, conduct three-dimensional ore body model analysis, and delimit the first potential ore-forming area; Obtain cross-section point set data, conduct numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer; Obtain the geochemical data of the sampling area, conduct three-dimensional space ore-forming trend prediction, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction result of blind ore bodies; Overlay the first potential ore-forming area, the ore-hosting space layer, and the three-dimensional prediction result of blind ore bodies, and divide according to the overlap degree to obtain the predicted area of gold ore formation. The second aspect of the present invention provides a three-dimensional prediction system for gold ore formation based on structural information fusion, including: A three-dimensional ore body model construction module configured to adopt the method of mainly using engineering modeling and supplemented by intermediate section drawing modeling to construct a three-dimensional ore body model, conduct three-dimensional ore body model analysis, and delimit the first potential ore-forming area; A dilation space simulation module configured to obtain cross-section point set data, conduct numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer; A superimposed halo structure module configured to obtain the geochemical data of the sampling area, conduct three-dimensional space ore-forming trend prediction, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction result of blind ore bodies; An overlay module configured to overlay the first potential ore-forming area, the ore-hosting space layer, and the three-dimensional prediction result of blind ore bodies, and divide according to the overlap degree to obtain the predicted area of gold ore formation.

[0008] Compared with the prior art, the beneficial effects of the present invention are: The method of this embodiment breaks through the limitations of traditional 3D modeling methods at the geometric description level. For the first time, it integrates ore-controlling structure information with geochemical trends and spatial structures, realizes a collaborative prediction mechanism for structure-geochemistry-ore body morphology, and effectively enhances the ability to identify blind ores and prospect for ores in the periphery. At the same time, a target area division mechanism based on the overlap of multi-source layers is constructed, significantly improving the credibility and accuracy of the prediction results and meeting the ore prospecting requirements of gold ore districts with a high degree of work. By combining engineering modeling and cross-section map modeling, it effectively avoids the spatial errors generated by traditional interpolation modeling in areas with sparse data or complex morphology, and improves the authenticity and continuity of the ore body structure. In areas where the ore body morphology changes suddenly or the fractures are severe, this method can more accurately capture key structural features, improve the modeling accuracy and geological interpretation ability, and significantly improve the adaptability of traditional methods under complex structural backgrounds.

[0009] The advantages of the present invention and the advantages of additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute a limitation to the present invention.

[0011] Figure 1 is a flowchart of the three-dimensional gold ore formation prediction method according to Embodiment 1 of the present invention; Figure 2 is a flow block diagram of the three-dimensional gold ore formation prediction method according to Embodiment 1 of the present invention; Figure 3 is a schematic diagram of the construction process of the three-dimensional ore body model according to Embodiment 1 of the present invention; Figure 4 is a three-dimensional ore body model constructed for the example area in the verification example of Embodiment 1 of the present invention; Figure 5 is an isometric line diagram of the vertical longitudinal projection of the ore body metal amount and a simulation diagram of the structural dilation space constructed for the example area in the verification example of Embodiment 1 of the present invention; Figure 6 is a deep dilation space prediction map for the example area in the verification example of Embodiment 1 of the present invention; Figure 7 is a single-element anomaly map and the corresponding R-type clustering analysis dendrogram for the example area in the verification example of Embodiment 1 of the present invention; Figure 8 is a vertical longitudinal projection map of the element combination anomaly of the Guandao gold ore structure superposition halo for the example area in the verification example of Embodiment 1 of the present invention; Figure 9 is a large-scale structural ore formation prediction longitudinal projection map of the Guandao gold deposit for the example area in the verification example of Embodiment 1 of the present invention; Detailed implementation manners The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0012] It should be noted that the following detailed description is exemplary and is 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 of ordinary skill in the technical field to which the present invention belongs.

[0013] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the 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 "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. It should be noted that, without conflict, the various embodiments and features in the present invention can be combined with each other. The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0014] Embodiment 1 In the technical solutions disclosed in one or more embodiments, as Figures 1 to 9 shown, a three-dimensional prediction method for gold ore formation based on structural information fusion includes the following steps: Step 1: Construct a three-dimensional ore body model by mainly using engineering modeling and supplemented by cross-section map modeling, perform analysis of the three-dimensional ore body model, and delimit the first potential ore-forming area; Step 2: Obtain cross-section point set data, perform numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer; Step 3: Obtain geochemical data of the sampling area, perform three-dimensional ore-forming trend prediction, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction result of the blind ore body; Step 4: Superimpose the first potential ore-forming area, the ore-hosting space layer, and the three-dimensional prediction result of the blind ore body, and divide the prediction area of gold ore formation according to the overlap degree.

[0015] This method first integrates the technical advantages of engineering modeling and middle-section map modeling. By performing 3D reconstruction on known drilling, tunnel, and mining engineering data, a preliminary ore body spatial model is constructed, and the boundary and structure are corrected with the help of geological middle-section map data to improve the integrity and spatial accuracy of the model. On this basis, geometric analysis is carried out on the 3D model. Combining with characteristic information such as the morphological changes, thickness distribution, and extension trend of the ore body, the first potential ore-forming area is determined. Subsequently, through spatial numerical processing of the cross-section point set, the dilation trend under the fault structure and geological background is analyzed, and a layer representing the potential ore-hosting space is constructed, which can reveal the influence of structural deformation on the spatial potential of the ore body. Then, 3D trend modeling is carried out using the geochemical data of the sampling area. With the help of multivariate anomaly recognition and three-dimensional ore-forming halo analysis, the ore-forming trends of blind ore bodies, peripheral ore bodies, and shallow and deep rich ore bodies are extracted to form a superimposed halo 3D model driven by multi-source information. Finally, the above multiple model layers are superimposed and fused, and the areas with high overlap are identified through spatial analysis as the prediction target areas for gold ore formation. This superimposition logic is based on the spatial coupling law of the synergistic action of ore-forming geological elements to achieve the quantitative identification of the prediction target areas.

[0016] The method of this embodiment breaks through the limitation of traditional 3D modeling methods that stop at the geometric description level. For the first time, ore-controlling structure information is integrated with geochemical trends and spatial structures to achieve a synergistic prediction mechanism of structure-geochemistry-ore body morphology, effectively enhancing the ability to identify blind ores and prospect for peripheral ores. At the same time, a target area division mechanism based on the overlap of multi-source layers is constructed, significantly improving the credibility and accuracy of the prediction results and meeting the prospecting requirements of gold ore areas with a high degree of work. By combining engineering modeling and middle-section map modeling, the spatial errors generated by traditional interpolation modeling in data-sparse or morphologically complex areas are effectively avoided, and the authenticity and continuity of the ore body structure are improved. In areas where the ore body morphology changes suddenly or the faults are severe, this method can more accurately capture the key structural features, improve the modeling accuracy and geological interpretation ability, and significantly improve the adaptability of traditional methods under complex structural backgrounds.

[0017] In step 1, the construction method of the 3D ore body model includes the following steps: Step 11: Realize engineering modeling by connecting single-project ore bodies: Step 111: Obtain the single-project data in the constructed ore body engineering database for connecting single-project ore bodies, identify single-project ore body segments based on the set ore-drawing indexes, and determine the top boundary line and the bottom boundary line; Optionally, in step 11, prospecting data is collected, including mine production prospecting data, historical geological and mineral surveys, prospecting and exploration data, etc. The mine production prospecting data includes single-project data and mineralization alteration data. The single-project data includes data such as the spatial location, azimuth slope angle, and test results of the collected samples of a single project. The mineralization alteration data includes the orebody grade, thickness, and alteration zone thickness; a prospecting engineering database is constructed based on the collected prospecting data. A single project is a specific engineering unit. For example, a borehole is a drilling project, and a roadway is an adit project. Optionally, the ore delineation index can be set to a cut-off grade of 0.8 g / t and a thickness ≥ 1.00 m. The top boundary line, i.e., the upper boundary, is the starting point delineated as the orebody, and the bottom boundary line is the end point of the orebody; these two lines represent the interval through which the orebody passes in this project. For example, if a borehole is drilled from the surface to 500 meters underground and the gold content is measured to be higher than the ore delineation standard at a depth of 120 meters to 160 meters, then 120 meters is the top boundary line of the orebody, 160 meters is the bottom boundary line of the orebody, and this interval (40 meters) is the orebody section delineated as the orebody.

[0018] Step 112: Connect the top boundary lines and bottom boundary lines of the corresponding ore bodies of adjacent single projects in sequence, delineate and connect to obtain the intermediate-level orebody boundary line, and assign attributes such as project and orebody layer number, etc., attribute A to the boundary points on the orebody boundary line. After marking the top and bottom boundary lines of the ore bodies of multiple prospecting projects, connection modeling is carried out. Specifically, according to the spatial location, the ore body boundary lines in adjacent projects are connected in sequence to form the intermediate-level orebody boundary line. At this time, if the intermediate-level 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 line. Through the method of mainly driven by engineering data and supplemented by drawings, a set of closed orebody boundary lines is finally formed at each intermediate-level.

[0019] Step 12: Obtain the CAD intermediate-level drawings of each intermediate level, and adjust the shape and perform spatial correction on the boundary lines of the intermediate-level ore bodies obtained from the engineering modeling with reference to the CAD intermediate-level drawings to optimize the boundary lines, and form a set of closed orebody boundary lines at each intermediate-level: Affected by factors such as the engineering control density, inclination, and bending of a single project, there are deviations between the intermediate-level orebody boundary lines obtained in step 112 and the actual spatial positions. Therefore, the CAD intermediate-level drawing boundary is used as an aid to provide a modeling reference for the overall distribution characteristics of the intermediate-level ore bodies for single-project modeling.

[0020] In mine geology and mining, a level refers to a working horizontal plane divided at a certain height interval in the vertical direction of the mine. The boundary line of the ore body at a level is a contour line formed by connecting the boundary points of the ore body at the corresponding depths of multiple boreholes on a certain level horizontal plane (such as -200 meters).

[0021] The CAD level drawing is a "plane map" drawn at a certain depth of the mine, which is an artificially drawn level drawing by collecting borehole logging data, roadway layout drawings, tunneling / sampling records, and orebody delineation results from geological exploration and production.

[0022] Optionally, a method for adjusting the shape and spatial correction of the boundary line of the ore body at a level with reference to the CAD drawing obtained by engineering modeling includes the following steps: Step 121: Extract the attribute information A of the boundary points on the boundary line of the ore body at a level, including the engineering number and the terminal layer number; Step 122: Obtain the coordinates of the boundary points of the ore body on the CAD level drawing, and extract the CAD engineering number and the three-dimensional ore body attribute B of the ore body attribute of each boundary point; Step 123: Identify the boundary points associated with the attribute information A and the ore body attribute B based on a single project, judge the offset degree of the boundary points where the boundary line of the ore body at a level is associated with the CAD level drawing, and adjust the boundary points corresponding to the borehole points of non-single projects according to the boundary points on the CAD level drawing to obtain a closed ore body boundary line at the corresponding level.

[0023] The borehole points of a single project are the actually detected points. Therefore, the boundary points corresponding to the borehole points of a single project are accurate boundary points. The boundary line of the ore body at a level is delineated based on these accurate points. Therefore, the points between two single projects are inaccurate points. Adjusting the boundary points of non-single projects based on the CAD level drawing can obtain a relatively accurate ore body boundary line; Specifically, in step 123, the method for adjusting the boundary points of non-single projects according to the boundary points on the CAD level drawing includes the following steps: 1) For each boundary point to be adjusted , calculate the shortest distance from the boundary point to the boundary line C of the CAD level drawing and determine the nearest point ; where is the boundary point on the boundary line of the CAD level drawing; 2) Use the weighted offset method to translate towards to obtain , and the formula is: ; ); where is the set maximum adjustment distance, is the point to the shortest distance of the boundary line C; 3) Combine all the sets of points { } that have been fine-tuned in position with the single-project boundary points {Pk}, and use spline interpolation to fit each boundary point to generate a closed boundary to obtain the adjusted middle-section boundary line; In the above solution, the authenticity of the single-project data is retained, allowing non-project points to approximate the shape drawn based on manual experience. At the same time, it can avoid problems such as sawtooth and fracture of the middle-section boundary line, and avoid model deviations caused by sparse data, distorted or simplified middle-section drawings; Step 13: Fit the ore body boundary lines of all middle sections through a triangular network according to the corresponding spatial data to generate a three-dimensional ore body model of a continuous ore body, realizing the construction of the spatial structure of the ore body; Specifically, stack the ore body boundary closed lines generated in each middle section in the vertical order according to the spatial sequence, and use the software triangular network fitting function to construct a continuous three-dimensional ore body solid model. The triangular network technology can connect the space between the closed lines in a planar manner to form a closed three-dimensional structure that conforms to the geological shape. This model not only has real spatial coordinate information but also integrates the mineralization data of each project, and can be used for subsequent work such as resource volume estimation, ore-forming trend analysis, and target area prediction.

[0024] In this embodiment, by proposing a method for collaborative modeling of single-project data and middle-section drawings, the spatial control engineering data such as boreholes and the ore body boundary information in the middle-section CAD drawings are fully integrated to achieve high-precision reconstruction of the three-dimensional structure of the ore body. 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 gets rid of the limitation of the traditional subjective inference of the three-dimensional structure from two-dimensional graphics, and avoids model deviations caused by sparse data, distorted or simplified middle-section drawings; on the other hand, it realizes the accurate and visual expression of the ore body, provides a reliable three-dimensional geological model basis for subsequent ore-forming target area prediction, resource volume assessment, and mining design, etc., and significantly enhances the engineering applicability and prediction credibility of the model.

[0025] In Step 1, based on the constructed three-dimensional ore body model, the following multi-dimensional and systematic analysis methods are proposed to explore the internal ore body spatial distribution law, favorable prospecting clues, and ore mineralization spatial enrichment characteristics of the model. The method for analyzing the three-dimensional ore body model includes the following steps: Step 101: Based on the three-dimensional ore body model, identify the spatial coordinates of the ore body in different parts, extract the extension distances of the ore body in the strike and dip directions in space, identify and quantitatively describe the key structural parameters such as the plunge direction, plunge angle, and distance between rich ore bodies of the ore body, and determine the first target area according to the key structural parameters; The technical role of the key structural parameters for prospecting is shown in Table 1; Table 1 Meanings and technical functions of key structural parameters

[0026] Step 102: Compare the ore body models in the exploration stage and the development stage, analyze the spatial variations of key parameters such as the ore body shape, boundary, and mineralization intensity through model comparison, extract the spatial variation data of the ore body spatial shape change, boundary offset distance, mineralization intensity change, and the position change of the high-grade ore section, and delimit the second target area based on the spatial variation quantification data Among them, the quantification data of the ore body spatial shape change may include volume difference, volume deformation ratio, main axis direction offset angle, etc.; the mineralization intensity change may include grade change, thickness change, standard deviation of ore grade distribution, high-grade ore volume change rate, etc. Step 103: Overlay the first target area and the second target area to obtain a third target area containing the two target areas, extract the alteration zone attributes, equal-spacing recurrence rate of the high-grade ore section, and attribute information of the ore body plunge direction in the third target area, adopt the normalized weighted scoring method, assign different weight values after normalizing each type of attribute, perform weighted overlay calculation to obtain a score, and output a predicted score value field in the 3D ore body model. This score value field generates a predicted hot zone map of the ore section through threshold cutting, which is used as the first potential ore-forming area

[0027] In Step 2, for the simulation of the tectonic dilation space: Obtain the cross-section point set data, conduct numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer, including the following steps Step 21: Obtain the cross-section point set data including the ore body distribution data extracted from the 3D ore body model constructed in Step 1 The cross-section point set data is the point set data collected on multiple ore body cross-sections, which may include ore body boundary points, tectonic line control points, or ore-bearing points Specifically, the cross-section point set data may include the 3D coordinate data of the fault surface, the spatial position and attitude information of the tectonic surface obtained from engineering logging, the 3D ore body modeling boundary point set, and the historical geological data; among them, the attitude information includes the strike, dip, and dip angle information of the fault surface Table 2 Data types and sources of the tectonic cross-section point set

[0028] Next, conduct trend analysis on the cross-section data through numerical processing (such as spatial interpolation, regression analysis, surface fitting, etc.) to construct a dilation space distribution map as the spatial basic layer for subsequent ore-forming space prediction, that is, the ore-hosting space layer

[0029] Step 22: Conduct trend surface analysis on the cross-section point set, perform a first-order trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure Step 23: By using geometric form decomposition, the main waveform of the cross-section structure is decomposed into multiple basic waveform functions, the waveform parameters such as amplitude, wavelength, and direction of each basic waveform are extracted, and the spatial response contributions of all waveforms to each point on the cross-section are calculated, thus realizing the construction of the movement direction parameter; Among them, the spatial response contribution refers to the contribution value of displacement or fluctuation intensity caused by a unidirectional wave (with a fixed direction, wavelength, and amplitude) when propagating in space to a point P(x, y) on a certain cross-section. ; According to the principle of wave superposition and decomposition in physics, assuming that the complex fracture surface waveform is a waveform synthesized by 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 spatial response contribution of the unidirectional wave is: ; Among them, is the amplitude of the unidirectional wave ; is the wavelength of the unidirectional wave , is the propagation direction of the unidirectional wave , and in the ordinary coordinate system, it can be expressed as: ; In the formula, represents the coordinates of the current cross-section point P, is the starting point coordinates of the unidirectional wave.

[0030] The spatial response contributions of n unidirectional waves to point P are: ; Find out the waveform parameters of each unidirectional wave. If the waveform parameters of each unidirectional wave are available, the waveform function formula will become a definite function.

[0031] Step 24: Based on the spatial response contributions of all waveforms to each point on the cross-section obtained from the combined simulation, substitute the waveform parameters into the mathematical model to simulate the fracture deformation response under different tectonic movement directions, and obtain the calculation model of the dilation thickness in three-dimensional space; In this step, given the fracture surface waveform diagram, based on the grid data of the waveform diagram, the distribution of the ore-hosting space generated when the fracture moves in different directions is simulated, and a simulated dilation space distribution diagram is made; Substitute the waveform parameters into the mathematical model, and the construction process of the mathematical model is as follows: Use the function to approximately describe the waveform of the fracture surface in the movement direction. When the fracture shear displacement distance is a, under the condition of non-compression of the rock, the thickness of the ore-hosting space can be approximately described as: ; Under the condition that the rock is completely compressible, its ore-hosting space is: ; Among them, d(x) > 0; respectively represent the waveform amplitudes in the non-compression condition, compression condition, and initial state, and A represents the amplitude; Based on the above theoretical analysis, the construction of the dilation space simulates the result of extending the application of the above formula to three-dimensional space, that is, using the cross-section waveform function Z(x,y) in the known area to replace in the formula, simulates and calculates the distribution of the dilation space when the fracture moves in different directions, and obtains the dilation thickness of the three-dimensional fracture surface: Replace in the formula with Z(x,y) : ; The general expression is the shear along a certain direction , and the calculation model for obtaining the dilation thickness is: ; Step 25: Based on the obtained calculation model of the dilation thickness, use the least squares method to fit and solve, and determine the most realistic movement direction by the minimum fitting error as the construction fracture movement direction; Compare the ore body thickness distribution map and the linear metal amount distribution map output by the three-dimensional modeling module, and see which direction has the highest degree of coincidence between the simulation results and the distribution of the actual ore body thickness or mineralization intensity. Then this simulated movement direction is the fracture movement direction. The fitting parameters include the distribution range, abnormal shape, intensity, etc. of the dilation space and the ore body grade thickness and metal amount.

[0032] Step 26: Based on each cross-section point P of the cross-section of the obtained fracture movement direction, use the unidirectional wave obtained in Step 23 to connect each cross-section point P according to the contribution of the spatial response of each cross-section point, and obtain the extended structure shape of the cross-section outward, that is, the predicted cross-section waveform diagram, realizing the prediction of the geometric morphology; Through the waveform decomposition in Step 23, the waveform parameters of each level of waves in the expression of the waveform function Z(x, y) have been obtained, and Z(x, y) has become a definite 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 values of each point on the cross-section, and draw the predicted deep fracture surface waveform diagram, and the dilation space of the cross-section can be obtained.

[0033] Step 27: Generate the dilation space distribution map: Extract the obtained cross-section waveform diagram into a spatial model as the ore-hosting space layer; Specifically, based on the fracture movement direction parameters obtained in step 25 and the waveform data of the peripheral fracture surface synthesized in step 26, simulate the fracture movement, calculate the relative dilation space, and draw the deep dilation space distribution map.

[0034] Verification of the effectiveness of dilation space simulation prediction: When the dilation space simulation technology is used in the case of faulting or large-angle turning of the section, the setting of its geological parameters and the calculation of the vertical distance of the primary trend surface occurrence will be affected by the sudden change of geological conditions, which will affect the prediction effect. Directly implementing engineering verification to simulate the mineralization effectiveness of the dilation space has a certain degree of blindness, increasing the risk and investment of prediction.

[0035] In high-workload mining areas or production mines, there will be a small number of prospecting projects that have been constructed in the past outside the densely distributed engineering areas, which provides favorable conditions for verifying the rationality of parameter settings and the effectiveness of technology application. By simulating the fitting degree between the position and distribution characteristics of the dilation space and the ore bodies exposed by the existing prospecting projects, an effectiveness evaluation of this technology in the study area is given.

[0036] Step 3: Obtain the geochemical data of the sampling area, conduct a three-dimensional spatial metallogenic trend prediction, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in the shallow and deep parts, obtain each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction result of the blind ore body. The three-dimensional prediction process of the blind ore body includes the following steps: Step 31: Test and analyze metallogenic elements: Obtain the content values of multiple chemical elements in the sampling area and conduct preprocessing; Specifically, sample the mining area, analyze the chemical composition of the samples to obtain the distribution results of multi-element geochemical anomalies, analyze the content values of various elements in the geochemical data of the sampling area; then normalize the original element content data to eliminate the interference of different dimensions and scales on subsequent clustering and comparison, and construct a standardized multi-element spatial database to prepare for subsequent clustering modeling; Step 32: Cluster the obtained content values of multiple chemical elements to identify combined anomaly factors; Optionally, R-type cluster analysis can be used to statistically analyze the genetic relationship between elements and identify possible metallogenic combination structures; Specifically, calculate the correlation coefficient and conduct analysis by setting thresholds. The first threshold can be set to identify the combined chemical elements with potential genetic relationships, and the second threshold can be set to identify strongly correlated elements At the level of a correlation coefficient of 0.4, divide the preliminary element groups and identify the combined chemical elements with potential genetic relationships; at the level of a correlation coefficient not less than 0.8, screen out the strongly correlated elements and further judge the geological rationality of the combination; The clustering analysis results of this step can reflect the affinity relationships between elements and the possible ore-forming combination structures, providing a basis for the subsequent identification of combined anomaly areas.

[0037] In this embodiment, R-type clustering analysis is performed on the content values of each element. Element grouping is carried out at a correlation level of 0.4, strong correlation elements are identified at a correlation level of 0.8, and geological rationality analysis of strong correlation element combinations is conducted, thereby reflecting the affinity relationships between elements, providing a general reference for studying the distribution of each factor and discovering the implicit geological background information, and also providing a reference basis for the element categories of subsequent combined anomalies.

[0038] Step 33: Determination of the lower limit of abnormal zoning for eigenvalue statistics: Frequency statistics and cumulative distribution calculations are performed on the sample values of each element, and the threshold method is used to divide the zoning boundaries of the inner zone, middle zone, and outer zone of single-element anomalies for subsequent drawing and zoning of abnormal layers. 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 are extracted; "single-element anomaly" refers to the phenomenon in geochemical exploration where the content of a certain chemical element (such as gold, copper, arsenic, antimony, etc.) at a certain sampling point or spatial area is significantly higher than the regional background value or the normal concentration range in the statistical distribution, showing an abnormal enrichment phenomenon. The anomaly in this embodiment is the abnormal enrichment phenomenon of elements. Step 34: The standardized element sample concentration values are positioned and added to the three-dimensional ore body model in Step 1, single-element content assignment is performed, and according to the obtained zoning boundaries, the outer zone, middle zone, and inner zone anomaly areas of single elements are delineated to obtain a single-element anomaly map, also known as a single-element anomaly distribution map. Using the aforementioned zoning boundaries, through the single-element anomaly map, spatial characteristic parameters of single-element anomalies can be analyzed and statistically calculated, which can include: anomaly location distribution, number and area of anomaly bodies, intensity level, and zoning characteristics. Step 35: The single-element anomaly map is superimposed in three-dimensional space with the known ore body model, the spatial relative position relationship between the single-element anomaly area and the ore body is analyzed, and according to the obtained combined anomaly factors and the zoning pattern of the ore-forming geochemical halo zone, each combined section in the single-element anomaly map is classified and marked with the superimposed halo type to construct a superimposed halo model. Specifically, the single-element anomaly layer generated in the previous step is imported into the three-dimensional modeling system; it is superimposed in three-dimensional space with the known ore body model, and the spatial relative position relationship between the anomaly area and the ore body is analyzed, such as: whether it is above, below, around, or on both sides of the ore body; whether it shows geometric relationships such as zonal, plumose, or wedge-shaped. Through these geometric relationships, it can be judged that the types of these anomaly areas include: Frontier halo: Ore-forming precursor reaction. Proximal halo: The associated halo during the mineralization process; Trailing halo: The residual anomaly or escaping components after mineralization.

[0039] Furthermore, the element combination factors identified through R-type cluster analysis are introduced into the analysis: For example: Au-As-Sb belongs to one combination factor, commonly found in the front edge of hydrothermal mineralization; the Cu-Pb-Zn-Mo combination may appear in the deep or peripheral mineralization environment; match the spatial distribution of these combination factors with the anomaly areas to determine which areas show obvious combined responses.

[0040] Furthermore, the zoning pattern of the ore-forming geochemical halo zone further confirms the divided superposed halo types. Specifically, the existing ore-forming geochemical superposed halo zone zoning patterns in this area or similar mining areas can be referred to, such as: Leading halo: Outside the ore body, with medium anomaly values, and the element combination is mostly high-mobility elements; Proximal halo: Close to the edge of the ore body, with the highest degree of element enrichment, and it is the most direct prospecting indicator; Trailing halo: May be in the footwall of the ore body or away from the ore body direction, with sparse composition and weak anomaly continuity; According to the characteristics of these halo zones, classify and label the types of each combined section in the anomaly layer.

[0041] Based on the above spatial superposition analysis, element combination factor identification, and regional ore-forming experience, the anomaly information of the entire mining area is modeled into a structured tectonic superposed halo model, including the following information: which element combinations correspond to each type of halo (leading / trailing / proximal); in which direction of the ore body it appears in space; how the element anomaly intensity and distribution pattern evolve; Based on the above constructed superposed halo model, it is possible to identify the anomaly bodies with classification labels in three-dimensional space, the main controlling element combinations of each type of anomaly body, and the information on the positional relationship with the ore body (relative direction, elevation, distance, etc.); Step 36: According to the single-element anomaly map and clustering combination characteristics, calculate the comprehensive index of the combined anomaly body, use the superposition algorithm to fuse the relevant element anomaly maps, identify the combined anomaly area, and construct a combined anomaly layer, also known as the combined anomaly distribution map; Among them, the comprehensive index of the combined anomaly body can include spatial coincidence degree, peak intensity distribution, zoning transition characteristics, etc.

[0042] Step 37: According to the superposed halo model obtained in Step 35, conduct type judgment and spatial classification on the combined anomaly area to obtain a type classification layer; Specifically, according to the elemental composition, spatial distribution and typical halo characteristics of the abnormal response, it is classified into: leading edge halo (far from the outer periphery of the ore body), trailing edge halo (inside the ore body or residual reaction zone), or near-ore halo (surrounding the core area of the ore body); Step 38: Overlay the single-element anomaly map, combined anomaly layer and type classification layer to obtain each anomaly layer of the superimposed halo three-dimensional model. Overlay the anomaly layer with the three-dimensional ore body model to obtain the three-dimensional relative spatial relationship with the ore body. Based on the superimposed halo type, speculate on the potential ore-forming expansion direction, delimit the possible ore-forming areas, and obtain the three-dimensional prediction result of the blind ore body; Step 4: Overlay each anomaly layer of the superimposed halo three-dimensional model, the three-dimensional ore body model and the tectonic dilation space model. The area where all three overlap has the highest ore-forming probability and is determined as the target area spatial position information; Further, for the overlapping area obtained after superimposition, use the empirical weight and information method to delineate the target area and classify the ore-forming target area. The specific steps are as follows: Step 41: Set variables: According to the information of each anomaly layer of the superimposed halo three-dimensional model, the three-dimensional ore body model and the tectonic dilation space model, set prediction variables and assign values, and set empirical weights; In this embodiment, 18 variables are set, as shown in Table 3; Table 3 Set prediction variables;

[0043] In Table 3, the setting basis of these empirical values is determined according to the information of the three-dimensional ore body model, the tectonic dilation space model and each anomaly layer of the superimposed halo three-dimensional model obtained from Steps 1 to 3. The empirical values are values set according to experience and are the mineralization possibilities of different prospecting information obtained from the coupling relationship of known ore bodies, geological information, dilation space information and superimposed halo information.

[0044] For the assignment of variables, the two-state and three-state assignment methods are used (as shown in Table 3). When using two-state assignment, look at the response state of the variable. When this state exists, assign a value of "1"; when it does not exist or the information is unclear, assign a value of "0". The three-state assignment principle depends on the relationship between the variable and gold mineralization, and assigns values of "1", "0" or "-1" respectively. However, in the specific assignment process, the research on the statistical significance of the state is noted, so that the "value" obtained has statistical meaning.

[0045] For the setting of variable weights, as mentioned above, the relationship between different prediction indicators and gold mineralization varies greatly. Using the expert method combined with its own prospecting experience, assign certain weights to the variables according to the role of each variable in gold ore prediction, which can more reasonably reflect the importance of the variables. The empirical weights of the variables in the study area are shown in Table 3.

[0046] Step 42. Target area delineation: Multiply the scores of each set variable of the predicted block by the weight, and after weighting, obtain the information content score of the predicted block. Grade the obtained target area according to the information content score. Step 421. Calculate the information content for the predicted block. The predicted blocks conduct variable scoring according to their respective ore prospecting signs. The sum of the products of the variable scores of each block and the weight is the information content score of the block. The cumulative frequency method can visually divide the information content values into 3 levels, and the criteria for level division can be: <5, 5 - 10, and >10.

[0047] Step 422. Target area delineation: According to the prediction results, the higher the information content value, the greater the possibility of mineralization. Therefore, delineate the high - value area of ore prospecting information content (information content value > 10) outside the existing engineering scope, and regard the area where the high - value blocks of information content are densely distributed as the comprehensive geological anomaly area.

[0048] According to the calculation results of the block information content, the delineated target areas are divided into 3 Class A target areas (score > 10 points), 1 Class B target area (score 5 - 10 points), and 2 Class C target areas (score < 5 points). The variable assignment and scoring of the target area reflect the mineralization potential and ore prospecting prospects of the target area. See Table 4 for details.

[0049] Table 4. Example of target area connection

[0050] Through the above process, the mineralization target areas GBQ1 to GBQ6 are divided into three grades, and can be confirmed according to the grades during on - site sampling verification, improving the efficiency of mineralization prediction.

[0051] To illustrate the above process and effects of this embodiment, taking the actual Jiaodong Gold Mine as an example, prospecting for the Guandao Gold Mine in the Guandao section of the Jiaodong Gold Mine, implementing the above process for ore - prospecting based on existing mines, achieving accurate mineralization prediction, the specific description is as follows; (1) Construction of the three - dimensional ore body model; as Figure 4 shown, respectively establish the deep ore body models of the 3 exploration phases (red - colored area blocks) and the mine - encrypted controlled ore body model. The red blocks are known ore bodies. Based on the ore body connection results in the exploration stage, the mine - encrypted controlled ore bodies are mainly distributed in two parts: - 260m to - 500m and - 660m to - 740m.

[0052] Geological analysis of the three - dimensional ore body model: Identify the ore body plunge law. The three - dimensional ore body model shows that the overall strike of the Guandao section of the Jiaojia tectonic belt is 60°, the ore body plunge direction is 303°, deflecting towards the SW. The plunge directions and plunge angles of the main ore bodies of each deposit on the Jiaojia Gold Mine belt are similar. The plunge directions are basically between 260° and 300°, and the plunge angles are generally between 30° and 60°, indicating that they are formed in a common and unified tectonic stress field.

[0053] The three-dimensional ore body model is analyzed to re-understand the weakly mineralized section. Comparing the ore body models established based on the exploration reports and the mine's additional exploration control engineering, there are differences in the ore body boundaries. In the Jiaojia Gold Belt, a thick and large gold ore body occurs at the location of exploration borehole 8ZK3 ( Figure 4 marked as the yellow area in it). During the mine exploration process, it is found that there are certain differences in the shape, scale, and occurrence of the ore body compared with the ore body submitted in the exploration report: The existence of the thick and large rich ore body exposed by the boreholes has been verified, but for the thin, poor, and small ore bodies exposed by the boreholes, such as those in large-scale structural zones with high alteration intensity and obvious zonation, thick and large gold ore bodies are often newly discovered around them. Combining the above Figure 4 analysis and understanding of the three-dimensional ore body model (3D model), focusing on the weakly mineralized interval between the shallow and deep sections of the main ore body, it is considered that the metallogenic potential of the interval where borehole 271ZK6 is located is questionable and requires re-evaluation.

[0054] Studying the previous borehole logging data of the above-mentioned weakly mineralized interval, it is found that the alteration zone extends into the weakly mineralized interval, and the zonation of scale and intensity does not significantly weaken, and the weakly mineralized interval is located at the extension part of the plunge direction of the shallow and deep ore sections. Combining the mineralization shows in the previous exploration boreholes, it is considered that the re-evaluation and understanding of the weakly mineralized interval have high research significance and prospecting value. A small number of previous weakly mineralized boreholes constructed may be local impoverished parts within the rich ore section.

[0055] (2) Simulation information of tectonic dilation space; Simulation of tectonic movement mode: Through trend analysis of the section data, the overall strike of the ore zone is obtained as 240°. Taking this as the new coordinate axis direction for coordinate transformation, the distribution pattern of dilation space when the fault moves in different directions is simulated. Comparing with the isometric line map of ore body metal amount, it is obtained that the left-lateral tensional shearing tectonic movement form of the hanging wall moving left and downward can produce the corresponding dilation space distribution pattern, as Figure 5 shown; Dilation space simulation: The coordinate range predicted in the deep part is determined to be from line 239 to line 300 at elevations from -1100 m to 0 m. The waveform of the deep section and the dilation space are simulated. Finally, the dilation space distribution map of the prediction area is obtained, and 6 dilation space prediction target areas KR1 - KR6 are outlined and connected, as Figure 6 shown; Prediction of dilation space simulation information: By comparing the simulation results of the dilation space, it is obtained that the movement direction of the ore-controlling fault during the main ore-forming stage is left-lateral sliding downward, which is consistent with the field observation results; it is also consistent with the plunge characteristics of the ore body reflected by the three-dimensional ore body model; it conforms to the regional tectonic movement characteristics, that is, the activity of the main fault should belong to left-lateral normal fault movement.

[0056] To verify the effectiveness of the prediction, the drilling engineering data of exploration lines 239 and 247 in the adjacent Xincheng Mining Area are collected, and the ore-bearing situation of the boreholes is compared with the predicted dilation space distribution: In boreholes ZK239-8 and ZK239-12 on Exploration Line 239, gold ore bodies with thicknesses of 4.25 m and grades of 13.21 g / t and thicknesses of 6.53 m and grades of 4.92 g / t were respectively encountered. The elevation of the ore body controlled by the two projects is between -420 m and -580 m. In the deep part, boreholes ZK239-16, 20, and 26 respectively controlled the mineralized zones at -700 m, -820 m, and -970 m. However, except for the ore body with a thickness of 1.45 m and a grade of 3.14 g / t encountered in borehole ZK239-20, no ore bodies were found.

[0057] Expansion space prediction map ( Figure 6 ) On Exploration Line 239, which is located at the edge of the map area, the anomaly is not closed. There is an expansion area between -520 m and -620 m, which coincides with the ore-bearing position; the area between -280 m and -420 m is a low and gentle positive value area, which is a weak expansion area, and the area between -420 m and -520 m is a squeezing area. It is speculated that there may be a weak mineralization discontinuity between the two ore-bearing boreholes; the areas between -790 m and -820 m and between -940 m and -980 m are low and gentle positive value areas. The former encountered a small ore body in ZK239-20, while the latter did not encounter ore, indicating that the ore-forming potential of the weak expansion area is limited.

[0058] (3) Stereoscopic prediction information of structural superimposed halos; Characteristic value 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 Guandao Gold Mine; Calculation of abnormal zoning standards and drawing of single-element geochemical maps; The lower limit of the anomaly is calculated by the cumulative frequency method. According to the content values of each element, the cumulative frequencies of 85%, 92%, and 98% are respectively used as the outer, middle, and inner three-level concentration zonings of the anomaly. The abnormal zoning standards of each element are shown in Table 5, and the single-element geochemical map of the structural superimposed halo is drawn, as Figure 7 shown. Among them, part a is the single-element anomaly map, and the right b area is the dendrogram of R-type cluster analysis; Table 5 Zoning standards for the outer, middle, and inner zones of elements;

[0059] Single-element geochemical map of structural superimposed halo ( Figure 7Show that: The concentration of Au gradually decreases upward, to both sides, and downward with the gold ore body as the center. Ag is positively correlated with Au and has similar characteristics. Au and Ag are near-ore indicator elements; The anomalies of Cu, Pb, Zn reflect the superimposed parts of the II and III metallogenic stages, mostly positively correlated with gold, and are near-ore indicator elements; The strong anomalies of As, Sb, Hg, B, Ba are mostly distributed in the upper part and the front halo of the ore body, and are characteristic indicator elements of the front halo; The strong anomalies of Bi, Mo, Mn, Co, Ni, V, Ti are mostly distributed in the lower part and the trailing halo of the ore body, and are characteristic indicator elements of the trailing halo.

[0060] Then, through correlation analysis, a three-dimensional model of the superimposed halo is constructed. As Figure 8 shown, the abnormal chemical elements are marked. When the anomalies of the elements in the trailing halo and the near-ore halo of the ore body are superimposed, it indicates the shallow extension of the ore body, while the superimposition of the anomalies of the elements in the front halo and the near-ore halo of the deep ore body indicates the deep extension of the ore body. The single-element anomalies of the structural superimposed halo in Guandao Gold Mine are well nested, and there are a total of 5 comprehensive anomalies ZH1-ZH5 distributed; Combining Figure 4 、 Figure 6 and Figure 8 for superposition to obtain Figure 9 , which is the longitudinal projection map of the large-scale structural ore-forming prediction of Guandao Gold Deposit; Three A-level target areas are obtained. The favorable ore-forming part of Target Area A1 is located between Line 263 and Line 295, and the occurrence elevation is -480m to -630m ( Figure 9 ). The comprehensive anomalies of the three superimposed halo elements of ZH1, ZH2, and ZH3 can reflect the complete characteristics of the ore body; The favorable ore-forming part of Target Area A2 is located between Line 255 and Line 263, and the occurrence elevation is -250m to -120m. The structural dilation space is seen near the elevation of -200m on Line 255 and Line 263; The lower near-ore halo and trailing halo are developed, and the ore body extends to the shallow part, that is, there is a good prospecting potential in the shallow part of the anomaly; GBQ3 in Target Area A3 is around the known ore body, and it may extend deep along the strike to the NE side. The favorable ore-forming part is located northeast of Line 247, and the occurrence elevation is below -700m, while the shallow part extends to the Xincheng Mining Area, which is the ore body on Line 239 that has been discovered. For the above A-level target areas, engineering verification is gradually carried out. After being verified by prospecting engineering, industrial ore bodies are exposed in all three A-level target areas, with thicknesses ranging from 9.20m to 26.00m and average grades ranging from 1.68g / t to 4.29g / t. This shows that this embodiment integrates three-dimensional ore body modeling, structural dilation space simulation, and structural superimposed halo geochemical anomaly analysis, and through three-dimensional integration and cross-verification of multi-source data, significantly improves the accuracy and prediction efficiency of ore-forming target area determination.

[0061] Example 2 Based on Example 1, in this example, an 8. Three-dimensional gold ore-forming prediction system based on structural information fusion is provided, including: The construction module of the three-dimensional ore body model is configured to construct a three-dimensional ore body model by mainly using engineering modeling and supplemented by cross-section map modeling, perform analysis of the three-dimensional ore body model, and delimit the first potential mineralization area; The dilation space simulation module is configured to obtain cross-section point set data, perform numerical analysis, and generate a dilation space distribution map as the ore-hosting space layer; The superimposed halo structure module is configured to obtain geochemical data of the sampling area, predict the mineralization trend in three-dimensional space, identify blind ore bodies, peripheral ore bodies, and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the three-dimensional model of the superimposed halo, and obtain the three-dimensional prediction result of the blind ore body; The superimposition module is configured to superimpose the first potential mineralization area, the ore-hosting space layer, and the three-dimensional prediction result of the blind ore body, and divide the prediction area of gold mineralization according to the overlap degree.

[0062] Furthermore, the construction module of the three-dimensional ore body model includes: The single-project ore body section boundary delimitation module is configured to obtain single-project data in the constructed ore body engineering database for connecting the single-project ore body, identify single-project ore body sections based on set ore connection indicators, and determine the top boundary line and the bottom boundary line; The single-project ore body connection module is configured to sequentially connect the top and bottom boundary lines of the corresponding ore bodies of adjacent single-projects to connect and obtain the cross-section ore body boundary line; The correction module is configured to obtain the CAD cross-section map of each cross-section, adjust the morphology and perform spatial correction on the boundary line of the cross-section ore body obtained by engineering modeling with reference to the CAD cross-section map, optimize the boundary line, and form a set of closed ore body boundary lines at each cross-section level; The fitting module is configured to fit the ore body boundary lines of all cross-sections through a triangular network according to the corresponding spatial data to generate a three-dimensional ore body model of a continuous ore body.

[0063] Furthermore, the dilation space simulation module includes: The cross-section point set data acquisition module is configured to obtain cross-section point set data including ore body distribution data extracted from the three-dimensional ore body model; The main body waveform construction module is configured to perform trend surface analysis on the cross-section point set, perform a primary trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main body waveform of the cross-section structure; The calculation module is configured to decompose the main body waveform of the cross-section structure into multiple basic waveform functions by using geometric form decomposition, extract the waveform parameters of the amplitude, wavelength, and direction of each basic waveform, and calculate the spatial response contribution of all waveforms to each point on the cross-section. A calculation model construction module, configured to simulate the fracture deformation response under different tectonic movement directions based on the spatial response contributions of all waveforms of the combined simulation to each point on the cross-section, and obtain a calculation model of the dilation thickness in three-dimensional space; A solution module, configured to perform a least squares fitting solution based on the obtained calculation model of the dilation thickness, and determine the most realistic movement direction by the minimum fitting error as the tectonic fracture movement direction; A predicted waveform determination module, configured to connect each cross-section point P based on the spatial response contributions of one-way waves to each cross-section point of the cross-section of the obtained fracture movement direction, and obtain the extended structure form extending outward from the cross-section, i.e., the predicted cross-section waveform diagram; An ore-hosting space generation module, configured to extract the obtained cross-section waveform diagram into a spatial model as the ore-hosting space layer.

[0064] It should be noted here that each module in this embodiment corresponds to each step in Embodiment 1 one by one, and the specific implementation process is the same, so it will not be repeated here.

[0065] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0066] Although the specific implementation manners of the present invention are described above in conjunction with the drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A three-dimensional prediction method for gold ore formation based on the fusion of structural information, characterized in that, The method includes the following steps: Adopt the method with engineering modeling as the main method and mid-section diagram modeling as the auxiliary method to construct a three-dimensional ore body model, analyze the three-dimensional ore body model, and delimit the first potential metallogenic area; Obtain cross-section point set data, conduct numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer; Obtain the geochemical data of the sampling area, conduct metallogenic trend prediction in three-dimensional space, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the superimposed halo three-dimensional model, and obtain the three-dimensional prediction result of blind ore bodies; Superimpose the first potential metallogenic area, the ore-hosting space layer and the three-dimensional prediction result of blind ore bodies, and divide the prediction area of gold ore formation according to the overlap degree.

2. The three-dimensional prediction method for gold ore formation based on structural information fusion according to claim 1, wherein The construction method of the three-dimensional ore body model includes the following steps: Obtain the single-project data in the constructed ore body engineering database to conduct single-project ore body delineation, identify single-project ore body segments based on the set ore delineation indexes, and determine the top boundary line and the bottom boundary line; Connect the top boundary lines and bottom boundary lines of the corresponding ore bodies of adjacent single projects in sequence to delineate the mid-section ore body boundary line; Obtain the CAD mid-section diagrams of each mid-section, adjust the morphology and perform spatial correction on the boundary lines of the mid-section ore bodies obtained by engineering modeling with reference to the CAD mid-section diagrams to optimize the boundary lines, and form a set of closed ore body boundary lines at each mid-section level; Fit the ore body boundary lines of all mid-sections through triangular meshing according to the corresponding spatial data to generate a three-dimensional ore body model of continuous ore bodies.

3. The three-dimensional prediction method for gold ore formation based on structural information fusion according to claim 2, wherein The method for adjusting the morphology and performing spatial correction on the boundary lines of the mid-section ore bodies obtained by engineering modeling with reference to the CAD diagram includes the following steps: Extract the attribute information A of the boundary points on the mid-section ore body boundary line, including the project number and the terminal layer number; Automatically read the coordinates of the ore body boundary points of the CAD mid-section diagram, and extract the CAD project number and the three-dimensional ore body attribute B of the ore body attribute of each boundary point; Identify the associated boundary points based on the single project to establish the attribute information A and the ore body attribute B, judge the offset degree of the boundary points of the mid-section ore body boundary line associated with the CAD mid-section diagram, and adjust the boundary points corresponding to the non-single-project drilling points according to the boundary points on the CAD mid-section diagram to obtain the closed ore body boundary line at the corresponding mid-section level.

4. The three-dimensional prediction method for gold ore formation based on structural information fusion according to claim 3, characterized in that The method for adjusting the boundary points corresponding to the non-single-project drilling points according to the boundary points on the CAD mid-section diagram includes the following steps: For each boundary point to be adjusted , calculate the shortest distance from the boundary point to the mid-section CAD boundary line C and determine the nearest point ; The method of weighted offset is adopted to translate to obtain ; Combine all the sets of points { } with fine-tuned positions with the single-process boundary points {Pk}, and use spline interpolation to fit each boundary point to generate a closed boundary to obtain the adjusted mid-section boundary line.

5. The three-dimensional gold ore-forming prediction method based on structural information fusion according to claim 1, characterized in that Obtain cross-section point set data, conduct numerical analysis, and obtain a generated dilation space distribution map as the ore-hosting space layer, including the following steps: Obtain cross-section point set data including ore body distribution data extracted from the three-dimensional ore body model; Conduct trend surface analysis on the cross-section point set, conduct a primary trend analysis to extract the main control direction trend and deformation amplitude characteristics, and obtain the main waveform of the cross-section structure; Adopt geometric form 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 spatial response contribution of all waveforms to each point on the cross-section; Based on the spatial response contribution of all waveforms to each point on the cross-section by combined simulation, simulate the fracture deformation response under different tectonic movement directions to obtain a calculation model of the dilation thickness in three-dimensional space; Based on the obtained calculation model of the expansion thickness, the least squares method is used for fitting and solving. The most realistic movement direction is determined by the minimum fitting error and used as the structural fracture movement direction. For each section point of the section with the obtained fracture movement direction, the contribution of the unidirectional wave to the spatial response of each section point is used, and each section point P is connected to obtain the extended structural form of the section extending outward, that is, the predicted section waveform diagram. The obtained section waveform diagram is extracted into a spatial model as the ore-hosting space layer.

6. The three-dimensional prediction method for gold ore formation based on structural information fusion according to claim 1, wherein The process of three-dimensional prediction of blind ore bodies includes the following steps: Obtain the content values of multiple chemical elements in the sampling area and perform preprocessing. Cluster the obtained content values of multiple chemical elements to identify combined anomaly factors. Perform frequency statistics and cumulative distribution calculations on the sample values of each element, and use the threshold method to divide the zoning boundaries of the inner zone, middle zone, and outer zone of the single-element anomaly. Locate and add the standardized element sample concentration values to the three-dimensional ore body model. According to the obtained zoning boundaries, delineate the outer zone, middle zone, and inner zone anomaly areas of the single element to obtain the single-element anomaly map. Perform three-dimensional spatial superposition of the single-element anomaly map and the known ore body model, analyze the spatial relative position relationship between the single-element anomaly area and the ore body, and according to the obtained combined anomaly factors and the zoning pattern of the ore-forming geochemical halo zone, classify and mark the superposition halo types of each combined section in the single-element anomaly map to construct a superposition halo model. According to the single-element anomaly map and the clustering combination characteristics, calculate the comprehensive index of the combined anomaly body, use the superposition algorithm to fuse the relevant element anomaly maps, identify the combined anomaly area, and construct a combined anomaly layer. According to the obtained superposition halo model, perform type judgment and spatial classification on the combined anomaly area to obtain a type classification layer. Unify and superimpose the single-element anomaly map, the combined anomaly layer, and the type classification layer to obtain each anomaly layer of the superposition halo three-dimensional model. Superimpose each anomaly layer of the superposition halo three-dimensional model with the three-dimensional ore body model to obtain the three-dimensional relative spatial relationship with the ore body. According to the superposition halo type, speculate on the potential ore-forming expansion direction, delimit the possible ore-forming area, and obtain the three-dimensional prediction result of the blind ore body.

7. The three-dimensional prediction method for gold ore formation based on structural information fusion according to claim 1, characterized in that Superimpose each anomaly layer of the superposition halo three-dimensional model, the three-dimensional ore body model, and the structural expansion space model. For the overlapping area obtained after superposition, use the empirical weight and information method to delineate the target area and classify the ore-forming target area, including the following steps: According to the information of each anomaly layer of the superposition halo three-dimensional model, the three-dimensional ore body model, and the structural expansion space model, set prediction variables and assign values, and set empirical weights. Multiply the scores of each set variable of the prediction block by the weight, and obtain the information content score of the prediction block after weighting. Classify the obtained target area according to the obtained information content score.

8. A three-dimensional gold ore-forming prediction system based on the fusion of structural information, characterized in that, Including: A three-dimensional ore body model construction module configured to construct a three-dimensional ore body model by using engineering modeling as the main method and cross-section drawing modeling as the auxiliary method, perform three-dimensional ore body model analysis, and delimit the first potential ore-forming area. An expansion space simulation module configured to obtain cross-section point set data, perform numerical analysis, and obtain a generated expansion space distribution map as the ore-hosting space layer. The superimposed halo construction module is configured to obtain the geochemical data of the sampling area, predict the metallogenic trend in three-dimensional space, identify blind ore bodies, peripheral ore bodies and potential rich ore bodies in the shallow and deep parts, determine each abnormal layer of the three-dimensional model of the superimposed halo, and obtain the three-dimensional prediction result of the blind ore body; The superimposing module is configured to superimpose the first potential metallogenic area, the ore-hosting space layer and the three-dimensional prediction result of the blind ore body, and divide the prediction area of gold mineralization according to the overlap degree.

9. The three-dimensional gold ore-forming prediction system based on structural information fusion according to claim 8, wherein The construction module of the three-dimensional ore body model includes: The single-project ore body section boundary delineation module is configured to obtain the single-project data in the constructed ore body engineering database for the connection of the single-project ore body, identify the single-project ore body section based on the set ore connection index, and determine the top boundary line and the bottom boundary line; The single-project ore body connection module is configured to sequentially connect the top boundary line and the bottom boundary line of the corresponding ore bodies of adjacent single-projects to connect and obtain the boundary line of the ore body in the middle section; The correction module is configured to obtain the CAD middle-section map of each middle section, and adjust the morphology and space of the boundary line of the ore body in the middle section obtained by engineering modeling with reference to the CAD middle-section map to optimize the boundary line, and form a set of closed ore body boundary lines at each middle-section level; The fitting module is configured to fit the boundary lines of the ore bodies of all middle sections 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 ore-forming prediction system based on structural information fusion according to claim 8, characterized in that The dilatation space simulation module includes: The cross-section point set data acquisition module is configured to obtain the cross-section point set data including the ore body distribution data extracted from the three-dimensional ore body model: The main body waveform construction module is configured to perform a trend surface analysis on the cross-section point set, perform a primary trend analysis to extract the main control direction trend and the deformation amplitude characteristics, and obtain the main body waveform of the cross-section structure; The calculation module is configured to decompose the main body waveform of the cross-section structure into multiple basic waveform functions by using geometric form decomposition, extract the waveform parameters of the amplitude, wavelength and direction of each basic waveform, and calculate the spatial response contribution of all waveforms to each point on the cross-section; The calculation model construction module is configured to simulate the fracture deformation response under different tectonic movement directions based on the spatial response contribution of all waveforms to each point on the cross-section by combined simulation, and obtain the calculation model of the dilatation thickness in three-dimensional space; The solution module is configured to perform a least squares fitting solution based on the obtained calculation model of the dilatation thickness, and determine the most realistic movement direction by the minimum fitting error as the tectonic fracture movement direction; The predicted waveform determination module is configured to connect each cross-section point P based on the spatial response contribution of the unidirectional wave to each cross-section point of the cross-section in the obtained fracture movement direction to obtain the extended structure form of the cross-section extending outward, that is, the predicted cross-section waveform diagram; The ore-hosting space generation module is configured to extract the obtained cross-section waveform diagram into a spatial model as the ore-hosting space layer.

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