A structural superposition halo prospecting prediction method, system, equipment and medium

Abstract

The present application relates to a method, system, equipment and medium for prospecting and prediction of structural superposition halo, which belongs to the field of geological exploration technology. The method includes: obtaining geological sample data in the target mining area and performing data preprocessing to obtain element distribution characteristics; obtaining fractal dimension characteristics according to element distribution characteristics, constructing a concentration-length fractal model to extract element anomaly thresholds for geological sample data, and determining abnormal geological samples; performing element combination analysis on abnormal geological samples, and determining the primary halo element combination according to concentration zoning characteristics, axial zoning sequences and element correlations; generating grade contour maps according to geological sample data and determining ore body location information; constructing a practical model of structural superposition halo in the mining area and predicting prospecting target areas based on element anomaly thresholds, concentration zoning characteristics, axial zoning sequences, primary halo element combinations and ore body location information to obtain prospecting prediction results. The present application can improve the accuracy of prospecting predictions.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and in particular to a structural superposition halo prospecting prediction method, system, equipment and medium. Background Art

[0002] How to improve the accuracy of deep prediction results is of great practical significance for finding a second prospecting space in the mining area and achieving increased reserves and production.

[0003] At present, in the field of geological prospecting, especially for the exploration of hydrothermal minerals, traditional prospecting methods rely on the phenomenon of primary halo, that is, the specific distribution pattern of elements in rocks or soils during the mineralization process. This method has achieved remarkable results since the 1970s, helping to discover a large number of new ore bodies and increase the reserves of mineral resources. However, one challenge in prospecting practice is the "anti-zoning" phenomenon exhibited by some deposits, which makes the traditional single-period primary halo interpretation seem inadequate.

[0004] The concept of primary superposition halo proposed in the prior art provides a new perspective for understanding these complex phenomena. The superposition halo theory takes into account the characteristics of multiple periods and stages in the formation of mineral deposits, and can better explain and utilize these complex geological information to guide mineral exploration. In addition, combined with the viewpoint of structural control, the structural superposition halo theory further improves the accuracy and efficiency of mineral exploration.

[0005] Nevertheless, traditional prospecting methods still face some challenges in practical applications, such as human comprehension bias during data analysis and poor processing effects when faced with the nonlinear characteristics of geochemical element distribution during mineralization. Therefore, how to objectively and truly reflect the deep mineralization information contained in the drill core in order to more accurately predict and explore mineral resources has become an urgent problem to be solved. Summary of the invention

[0006] In order to improve the accuracy of prospecting prediction, the present application provides a structural superposition halo prospecting prediction method, system, equipment and medium.

[0007] In the first aspect, the present application provides a method for prospecting and predicting structural superposition halos, which adopts the following technical solution:

[0008] A structural superposition halo prospecting prediction method, the method comprising:

[0009] Acquire geological sample data in the target mining area and perform data preprocessing to obtain element distribution characteristics; the geological sample data includes sample length information and element concentration information;

[0010] According to the element distribution characteristics, fractal dimension characteristics are obtained, and a concentration-length fractal model is constructed;

[0011] Extracting element anomaly thresholds from the geological sample data based on the concentration-length fractal model and determining abnormal geological samples; performing element combination analysis on the abnormal geological samples and determining the primary halo element combination according to concentration zoning characteristics, axial zoning sequence and element correlation;

[0012] Generating a grade contour map according to the geological sample data, and determining ore body location information according to geometric features of the grade contour map;

[0013] Based on the element anomaly threshold, concentration zoning characteristics, axial zoning sequence, primary halo element combination and ore body location information, a practical model of structural superimposed halo in the mining area is constructed;

[0014] The prospecting target area is predicted according to the practical model of structural superposition halo in the mining area to obtain the prospecting prediction result.

[0015] By adopting the above technical solution, the sample length is innovatively introduced as a weight index, and the CL fractal model is used to optimize the traditional anomaly extraction method. This method comprehensively considers the difference in the amount of information carried by samples of different lengths, and pays special attention to the unevenness of the length of the drill core samples. It can objectively and truly reflect the deep mineralization information contained in the drill core. The series of steps in this application integrate geological, geochemical and mathematical models to form a systematic prospecting prediction process, which effectively solves the difficulties in deep prospecting prediction, especially the optimization of anomaly threshold extraction and element combination determination, as well as the scientific nature of model construction, which significantly improves the accuracy and practicality of prospecting prediction, and provides scientific guidance and theoretical basis for actual mining engineering deployment.

[0016] Optionally, the data preprocessing includes:

[0017] According to the geological sample data, analyzing and obtaining sample length information and element concentration information;

[0018] According to the sample length information and element concentration information of all geological samples, the total element content is obtained;

[0019] The average element concentration information is obtained based on the total element content and the sum of the sample length information of all geological samples.

[0020] By adopting the above technical solution, processing the element concentration data can better reflect the element distribution characteristics of deep rocks. The average concentration of the element is calculated by introducing the sample length as a weight factor. The calculation process of the element distribution characteristics is an important bridge connecting data preprocessing and model construction.

[0021] Optionally, the step of performing element combination analysis on the abnormal geological sample and determining the element combination of the primary halo according to the concentration zoning characteristics, axial zoning sequence and element correlation comprises:

[0022] Performing data conversion according to the abnormal data subset of the abnormal geological sample;

[0023] Generate an element concentration zoning diagram based on the converted abnormal data subset, and identify the concentration zoning characteristics and axial zoning sequence;

[0024] Performing dimensionality reduction processing on the transformed abnormal data subset, identifying correlations and element combinations between elements, and obtaining element correlation analysis results;

[0025] According to the concentration zoning characteristics, axial zoning sequence and element correlation analysis results, the elements that show the distribution characteristics of the leading edge, near the ore and tail halo in space are determined, and the primary halo element combination is obtained.

[0026] By adopting the above technical scheme, in order to identify whether there is a statistical interdependence between different elements in geological samples, element correlation analysis is of great significance for understanding geological processes, mineralization mechanisms and guiding mineral exploration; by comprehensively judging which elements show the distribution characteristics of leading edge, near-ore and tail halo in space, the element combination of the primary halo can be finally determined.

[0027] Optionally, the ore body positioning information includes the spatial inclination, lateral direction and fluid migration direction of the ore body.

[0028] Optionally, the geological sample data further includes coordinates of geological sampling points and corresponding grade values; the steps of generating a grade contour map according to the geological sample data, and determining ore body location information according to geometric features of the grade contour map include:

[0029] Generating a grade contour map based on the coordinates of the geological sampling points and the corresponding grade values;

[0030] Based on an image processing algorithm, the long axis direction of the grade contour map is identified according to the geometric features of the grade contour map to obtain the spatial inclination of the ore body;

[0031] Determine the lateral direction of the ore body according to the vertical direction of the long axis direction;

[0032] Establishing a geological structure model according to the geological structure information of the target mining area and the grade contour map;

[0033] Based on the geological structure model, the fluid movement path is simulated according to the lateral direction to determine the fluid migration direction;

[0034] The ore body location information is obtained according to the spatial inclination, lateral direction and fluid migration direction of the ore body.

[0035] By adopting the above technical solutions, based on the deep integration of geological principles and modern computing technology, it aims to more accurately predict the movement trajectory of mineralized fluids, integrate geological structure information and lateral laws, realize accurate prediction of fluid migration direction, and provide a scientific basis for in-depth positioning of ore bodies.

[0036] Optionally, the step of predicting the prospecting target area according to the mining area structural superposition halo practical model to obtain the prospecting prediction result includes:

[0037] Determine the prospecting target area according to the model prediction results of the practical model of structural superposition halo of the mining area;

[0038] Receiving field verification data of the prospecting target area input by a user;

[0039] Judging whether the field verification data is consistent with the model prediction result, and if so, determining that the prospecting target area is the prospecting prediction result;

[0040] If not, the mining area structural superposition halo practical model is optimized and corrected according to the field verification data, and the model parameters are adjusted until the model prediction results are consistent with the fed-back field verification data, so as to obtain the optimized mining area structural superposition halo practical model;

[0041] The prospecting target area is predicted according to the optimized mining area structural superposition halo practical model, and the prospecting target area is determined as the prospecting prediction result.

[0042] By adopting the above technical solutions, the model continuously absorbs feedback from field verification data, gradually approaches the actual mineralization law, improves the predictive performance of the practical model of structural superposition halo in the mining area, and provides more accurate guidance for deep mineral exploration.

[0043] In the second aspect, the present application provides a structural superposition halo prospecting prediction system, which adopts the following technical solution:

[0044] A structural superposition halo prospecting prediction system, the system comprising:

[0045] An acquisition module is used to acquire geological sample data in a target mining area; the geological sample data includes sample length information and element concentration information;

[0046] A preprocessing module, used for preprocessing the geological sample data to obtain element distribution characteristics;

[0047] A fractal model construction module, used to obtain fractal dimension characteristics according to the element distribution characteristics and construct a concentration-length fractal model;

[0048] An abnormal geological sample identification module is used to extract element abnormality thresholds from the geological sample data based on the concentration-length fractal model and determine abnormal geological samples;

[0049] A primary halo element combination determination module is used to perform element combination analysis on the abnormal geological sample and determine the primary halo element combination according to concentration zoning characteristics, axial zoning sequence and element correlation;

[0050] An ore body location information determination module, used to generate a grade contour map according to the geological sample data, and determine the ore body location information according to the geometric features of the grade contour map;

[0051] A practical model building module is used to build a practical model of structural superposition halo in a mining area based on the element anomaly threshold, concentration zoning characteristics, axial zoning sequence, primary halo element combination and ore body location information;

[0052] The module for generating mineral exploration prediction results is used to predict the mineral exploration target area according to the practical model of the structural superposition halo of the mining area to obtain the mineral exploration prediction results.

[0053] By adopting the above technical solutions and integrating geological, geochemical and mathematical models, a mineral prospecting prediction system has been formed, which effectively solves the difficult problems in deep mineral prospecting prediction, especially the optimization of anomaly threshold extraction and element combination determination, as well as the scientific nature of model construction. It has significantly improved the accuracy and practicality of mineral prospecting prediction, and provided scientific guidance and theoretical basis for actual mining engineering deployment.

[0054] Optionally, the preprocessing module includes:

[0055] A data analysis unit, used to analyze the geological sample data to obtain sample length information and element concentration information;

[0056] The element total content calculation unit is used to obtain the element total content according to the sample length information and element concentration information of all geological samples;

[0057] The element average concentration information calculation unit is used to obtain the element average concentration information according to the total content of the element and the sum of the sample length information of all geological samples.

[0058] In a third aspect, the present application provides a computer device, which adopts the following technical solution:

[0059] A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0061] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect.

[0062] To summarize, the present application includes at least one of the following beneficial technical effects: a series of steps of the present application form a systematic mineral prospecting prediction process by integrating geological, geochemical and mathematical models, effectively solving the difficulties in deep mineral prospecting prediction, especially the optimization of anomaly threshold extraction and element combination determination, as well as the scientific nature of model construction, which significantly improves the accuracy and practicality of mineral prospecting prediction, and provides scientific guidance and theoretical basis for actual mining engineering deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a first flow chart of a structural superposition halo prospecting prediction method according to one of the embodiments of the present application.

[0064] Figure 2 It is a second flow chart of the structural superposition halo prospecting prediction method of one of the embodiments of the present application.

[0065] Figure 3 It is a third flow chart of the structural superposition halo prospecting prediction method of one of the embodiments of the present application.

[0066] Figure 4 It is a fourth flow chart of the structural superposition halo prospecting prediction method of one of the embodiments of the present application.

[0067] Figure 5 It is the fifth flow chart of the structural superposition halo prospecting prediction method of one of the embodiments of the present application.

[0068] Figure 6 It is a specific technical roadmap for the practical application of the structural superposition halo prospecting prediction method of one of the embodiments of the present application.

[0069] Figure 7 It is a schematic diagram of the practical model of structural superposition halo established in the actual research process of this application. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0071] First, the terms related to this embodiment are explained.

[0072] Primary halo: Primary halo refers to the abnormal increase in the content of geochemical elements in the rocks around the ore deposit, especially around the ore body. This increase usually occurs synchronously with the mineralization process, accompanies the formation process of the ore body, and manifests itself in a certain spatial form and element distribution characteristics, reflecting the migration and enrichment distribution law of mineralizing elements in the primary environment. Primary halo is an important clue in prospecting research, indicating the possible range of the mineralization process and ore body.

[0073] Primary superimposed halo: Primary superimposed halo refers to the overlapping combination of different primary halos formed by the superposition of different mineralization stages or mineralization in complex mineral deposits. The primary superimposed halo theory takes into account the multi-stage formation process of mineral deposits. The superimposed distribution and time series of multiple element combinations in space can provide indications of deeper mineralization activities.

[0074] Reverse zoning phenomenon: Reverse zoning phenomenon refers to the situation in which the element distribution is opposite to the expected normal zoning sequence (such as front, near ore, tail) during the mineralization process. This may be caused by multiple stages of mineralization, structural control or abnormal fluid complexity.

[0075] Structural superposition halo theory: The tectonic superposition halo theory is based on the idea that hydrothermal deposits are strictly controlled by structure. It believes that structural characteristics (such as faults and folds) affect the distribution of mineralizing fluids and emphasizes the control of structure on the axial zoning of the primary halo. Through structural analysis, we can understand the spatial distribution of mineralization and ore body prediction under structural control.

[0076] Prospecting target area: A prospecting target area is a specific area or location with high prospecting potential predicted by a series of technical means in the process of mineral exploration, combining geological theory with practical experience. It is the object of key research and exploration in the exploration work and is the location where mineral resources are most likely to be discovered based on existing data and analysis.

[0077] Secondly, the background of this application is further introduced.

[0078] At present, in the identification of geochemical anomalies, although traditional statistical methods (such as the mean plus minus standard deviation method based on normal distribution) are widely used, they are often difficult to adapt to the complexity and nonlinear characteristics of the mineralization process. To solve this problem, some scholars have introduced fractal theory to more accurately identify anomalies by considering the relationship between concentration and size, area, and volume. However, for geological sampling, especially drill hole samples, due to the heterogeneity of their length, the direct application of these methods may ignore the difference in sample information, thereby reducing the accuracy of prospecting predictions.

[0079] Based on this, an embodiment of the present application discloses a method for predicting mineral exploration using structural superposition halos.

[0080] Reference Figure 1, a structural superposition halo prospecting prediction method, the prediction method includes:

[0081] Step S101, obtaining geological sample data in the target mining area and performing data preprocessing to obtain element distribution characteristics; the geological sample data includes sample length information and element concentration information;

[0082] Geologists obtain rock samples in the field or through drilling. These samples may come from different depths and strata, such as the Cambrian and Silurian, which are sedimentary rock layers from specific periods in Earth's history. Samples are selected based on the variation in the physical characteristics of the rock, such as color, grain size (the size of the rock particles), material composition (the type of minerals contained in the rock), and the degree of mineralization and alteration.

[0083] In some embodiments, data preprocessing can be to clean the collected data, remove outliers, and ensure the accuracy and consistency of the data; for example, the central logarithm ratio method can be used to convert the data from simplex space to Euclidean space to achieve standardized analysis of data with different geological backgrounds.

[0084] Step S102, obtaining fractal dimension characteristics according to element distribution characteristics, and constructing a concentration-length fractal model;

[0085] Among them, the embodiment of the present application innovatively introduces sample length as a weight indicator, uses fractal theory to analyze the relationship between element concentration and sample length in geological sample data, constructs a concentration-length fractal model, optimizes the traditional anomaly extraction method, and takes into account the difference in the amount of information carried by samples of different lengths. It aims to solve the challenges brought by the unevenness of borehole sampling length and improves the accuracy of anomaly identification.

[0086] It should be noted that the geochemical background usually obeys a normal or log-normal distribution, while the geochemical anomaly obeys a power-law distribution. Studies in recent years have shown that the traditional method is not ideal in the complex geochemical data processing process, and the fractal model can quantitatively characterize the intensity of nonlinear geological anomalies, becoming an important method for determining and interpreting anomaly thresholds. Since the borehole sampling length is artificially divided into different lengths by the geological cataloger based on the identification of the borehole lithology and mineralization, it is non-uniform, but the sampling is uniform and continuous within a certain sample length space, so it has the characteristics of discreteness. In order to more scientifically extract the anomaly threshold of the borehole geochemical field, this application proposes a concentration-length (CL) fractal model.

[0087] Specifically, the expression of the concentration-length fractal model is: L(>C)∝C -αThat is, there is an exponential relationship between the sample length L (>C) with element concentration higher than C and the concentration C. The power exponent α is the embodiment of the fractal dimension characteristics. By fitting the data points, the value of the power exponent α can be obtained, which helps to understand the distribution characteristics of the data.

[0088] As an implementation method for obtaining fractal dimension characteristics, the specific steps include: constructing a double logarithmic graph of element concentration and sample length L corresponding to a specific concentration C based on the collected geological sample data; observing the data distribution in the double logarithmic graph, if the data points are roughly distributed in a straight line, this indicates that there is a power law relationship, and the data points are linearly fitted by the least squares method or other linear fitting methods to obtain a straight line, the slope of which is the power exponent α, and the calculated power exponent α is the embodiment of the fractal dimension characteristics. Among them, the specific concentration C can be the background concentration of the element or a threshold value preliminarily determined by other methods. In the double logarithmic coordinate system, the logarithmic value of the concentration C is used as the horizontal axis, and the logarithmic value of the sample length L exceeding the concentration is used as the vertical axis, and then all data points are marked on the graph.

[0089] Step S103, extracting element anomaly thresholds from geological sample data based on a concentration-length fractal model, and determining abnormal geological samples;

[0090] In one of the embodiments of the present application, based on fractal theory and combining sample length as a weight indicator, a length-based CL fractal model is proposed to extract element anomaly thresholds, separate geological background and anomalies, and clarify which geological samples have element contents exceeding the background level. If the elements reach an abnormal state, they are considered to be abnormal geological samples, which may indicate mineralization activity, thereby more accurately identifying geological samples containing mineralization information and providing a basis for subsequent ore body positioning.

[0091] As an implementation method of extracting element anomaly thresholds using a concentration-length fractal model, the specific steps include: sorting the element concentration data in each geological sample data, and calculating the cumulative length of samples whose length L is greater than C at different concentration levels C; displaying the relationship between concentration and length through a log-log plot, and fitting three straight lines, corresponding to a low background area, a high background area, and a high anomaly area, respectively; wherein the intersection of the two straight lines is defined as the critical point between the background and the anomaly, that is, the lower limit of the anomaly; and determining the anomaly critical point of each element based on the fitting results, the anomaly critical point is the anomaly threshold, representing the boundary between the normal background and the mineralization anomaly.

[0092] Step S104, performing element combination analysis on the abnormal geological sample, and determining the primary halo element combination according to the concentration zoning characteristics, axial zoning sequence and element correlation;

[0093] Among them, element combination analysis is to determine the element combination of different stages in the primary halo, such as the leading halo, near-ore halo and tail halo, through in-depth research on abnormal samples, according to concentration zoning characteristics, axial zoning sequence and element correlation; these element combinations provide key chemical markers for ore body positioning, because they can indicate the scope and intensity of mineralization, as well as the migration path of mineralizing fluids and the mineralization process.

[0094] Specifically, the concentration zoning characteristics refer to the spatial variation pattern of element concentration. Usually, the concentration is higher near the center of the ore body and gradually decreases outward. This distribution pattern helps to distinguish the mineralization center and the peripheral area; the axial zoning sequence refers to the orderly change of the element distribution along the axial direction of the ore body. The relative enrichment of different elements at different depths or heights reflects the zoning information of hydrothermal activity; element correlation analysis reveals the symbiotic relationship between different elements, helping to understand how element combinations jointly indicate the mineralization process and the existence of ore bodies.

[0095] In one of the embodiments of the present application, the primary halo element combination (such as As and Sb as the leading halo, Au, Ag, Cu, Zn, Ni, Mo as the near-ore halo, and Pb, Mn, Co, W, Fe as the tail halo) provides a direct indication of the mineralization process and is indispensable geochemical information in model construction.

[0096] Step S105, generating a grade contour map according to the geological sample data, and determining the ore body location information according to the geometric features of the grade contour map;

[0097] Among them, the ore body positioning information includes the spatial inclination, lateral direction and fluid migration direction of the ore body; by drawing grade contour maps, it is not only possible to reveal the spatial distribution characteristics of the ore body, such as spatial inclination and lateral direction, but also to infer the fluid migration direction based on the trend of the contour lines, thereby more intuitively showing the spatial layout of the ore body and providing a direct visual basis for deep mineral exploration.

[0098] Step S106, constructing a practical model of structural superposition halo in the mining area based on element anomaly threshold, concentration zoning characteristics, axial zoning sequence, primary halo element combination and ore body location information;

[0099] Among them, the element anomaly threshold provides a quantitative standard for distinguishing mineralized areas from non-mineralized areas in the model. The concentration zoning characteristics and axial zoning sequences help to reproduce the longitudinal and lateral zoning characteristics of the mineralized zone in the model. The primary halo element combination provides indispensable geochemical information for model construction. The ore body positioning information is crucial to reflecting the three-dimensional spatial distribution of the ore body in model construction.

[0100] Step S107, predicting the prospecting target area according to the practical model of the structural superposition halo of the mining area to obtain the prospecting prediction result.

[0101] Specifically, under the guidance of the model's predictions, favorable mineralization spaces can be identified, especially at specific depths. Based on the superposition of leading halos and tail halos, abnormal characteristics and other geological parameters, potential mineralization locations can be predicted and identified as prospecting targets.

[0102] In the above implementation, the sample length is innovatively introduced as a weight index, and the CL fractal model is used to optimize the traditional anomaly extraction method. This method comprehensively considers the difference in the amount of information carried by samples of different lengths, pays special attention to the unevenness of the length of the drill core samples, and can objectively and truly reflect the deep mineralization information contained in the drill core. The series of steps in this application integrate geological, geochemical and mathematical models to form a systematic prospecting prediction process, which effectively solves the difficult problems in deep prospecting prediction, especially the optimization of anomaly threshold extraction and element combination determination, as well as the scientific nature of model construction, which significantly improves the accuracy and practicality of prospecting prediction, and provides scientific guidance and theoretical basis for actual mining engineering deployment.

[0103] As an implementation method for constructing a practical model of structural superposition halo in a mining area, the specific steps include:

[0104] Based on the geological background and existing ore body knowledge, design the model framework and determine the geological structure, mineralization stage, fluid migration path and other elements that the model needs to include;

[0105] Import the analysis results of element combination analysis, ore body location, zoning information, etc. into the model to form a detailed geological-geochemical database;

[0106] Using GIS or 3D geological modeling software, the 3D shape of the ore body is constructed according to the ore body inclination, lateral lying law and fluid migration direction, and the element concentration distribution is superimposed to form spatial zoning;

[0107] Superimpose structural information in the model to simulate the axial zoning and superposition structure of mineralization under structural control;

[0108] Based on the model prediction, the prospecting target area is verified through subsequent drilling or pit exploration projects, and the model parameters are adjusted and optimized through feedback.

[0109] In the above implementation, based on the constructed mining area structural superposition halo practical model, accurate prediction of mining area structural superposition halo and positioning of deep mineral resources can be achieved.

[0110] Reference Figure 2 As an implementation of data preprocessing in step S101, data preprocessing includes:

[0111] Step S201, analyzing and obtaining sample length information and element concentration information according to geological sample data;

[0112] Step S202, obtaining the total element content according to the sample length information and element concentration information of all geological samples;

[0113] Among them, the total element content is the sum of the products of sample length and element concentration;

[0114] Step S203, obtaining the average element concentration information according to the total element content and the sum of the sample length information of all geological samples.

[0115] Specifically, the calculation formula for the average element concentration is:

[0116]

[0117] Where Cx is the average concentration of an element, n is the total number of samples, Ci and Li are the element concentration and length of the i-th sample, respectively; this weighted average reflects the average distribution of the element in the entire rock volume, rather than the concentration in a single sample.

[0118] It is understandable that since the sampling of drilling and exploration trenches is based on the careful observation of the core by geological catalogers, and samples of different lengths are divided according to the changes in the color, particle size, material composition and mineralization and alteration of the rock, the method of directly obtaining the average value of the element cannot objectively reflect the true situation of the deep rock. In order to eliminate this effect, this application introduces length as a weight into the process of calculating the average value of the element. The obtained average concentration information of the element is the necessary input data for constructing the concentration-length fractal model, which can more reasonably identify outliers and provide a more accurate basis for subsequent element combination analysis and prospecting prediction.

[0119] In the above implementation, processing the element concentration data can better reflect the element distribution characteristics of deep rocks. The average concentration of the element is calculated by introducing the sample length as a weight factor. The calculation process of the element distribution characteristics is an important bridge connecting data preprocessing and model construction.

[0120] Reference Figure 3 As an implementation method of step S104, the step of performing element combination analysis on the abnormal geological sample and determining the element combination of the primary halo according to the concentration zoning characteristics, axial zoning sequence and element correlation includes:

[0121] Step S301, performing data conversion according to an abnormal data subset of an abnormal geological sample;

[0122] The abnormal data subset only contains the element concentration data exceeding the abnormal threshold.

[0123] In some specific embodiments, the closed geochemical data can be "opened" by using the central logarithmic ratio method, so that the abnormal data subset is converted from the simplex space to the Euclidean space; and, because the distribution of the surface sampling medium is heterogeneous, the data of different geological units are leveled to facilitate subsequent comprehensive analysis and comparison;

[0124] Step S302, generating an element concentration zoning diagram based on the converted abnormal data subset, and identifying the concentration zoning characteristics and axial zoning sequence;

[0125] Among them, the concentration zoning characteristics and axial zoning sequences of elements are used to clarify the spatial distribution characteristics of elements and the direction of mineralization flow.

[0126] In some embodiments, by generating an element concentration zoning map on a vertical longitudinal projection map, it is helpful to intuitively display the concentration distribution pattern of the elements in space, and then identify the zoning with obvious concentration gradient changes to obtain concentration zoning characteristics; the modified Gregorian index method (such as the method proposed by Wang Jianxin et al. in 2007) can be applied to automatically calculate the zoning index of each element through programming, and determine the axial zoning sequence based on the calculation results.

[0127] Step S303, performing dimensionality reduction processing on the converted abnormal data subset, identifying the correlation between elements and element combinations, and obtaining element correlation analysis results;

[0128] In some embodiments of the present application, the transformed data can be subjected to dimensionality reduction processing by R-type factor analysis (such as principal component analysis) to identify the correlation and element combination between elements. For example, the relationship strength (load value) between each element and the factor can be displayed by rotating the factor loading matrix, wherein the load whose absolute value is greater than a preset threshold is considered to be significant.

[0129] Step S304, according to the concentration zoning characteristics, axial zoning sequence and element correlation analysis results, determine the elements that present the distribution characteristics of the leading edge, near the ore, and the tail halo in space, and obtain the primary halo element combination.

[0130] In the above implementation mode, in order to identify whether there is a statistical interdependence between different elements in geological samples, element correlation analysis is of great significance for understanding geological processes, mineralization mechanisms and guiding mineral exploration; by comprehensively judging which elements show the distribution characteristics of leading edge, near-ore and tail halo in space, the element combination of the primary halo can be finally determined.

[0131] Reference Figure 4As an implementation method of step S105, the geological sample data also includes the coordinates of the geological sampling points and the corresponding grade values; the step of generating a grade contour map according to the geological sample data and determining the ore body location information according to the geometric features of the grade contour map specifically includes:

[0132] Step S401, generating a grade contour map based on the coordinates of the geological sampling points and the corresponding grade values;

[0133] In some embodiments, a computer graphics processing technique, such as an interpolation algorithm (such as Kriging interpolation, inverse distance weighted interpolation, etc.), may be used to automatically generate a grade contour map on a vertical longitudinal projection map according to the coordinate positions of the sampling points and the corresponding grade values; step S402, based on an image processing algorithm, the long axis direction of the grade contour map is identified according to the geometric features of the grade contour map to obtain the spatial inclination of the ore body;

[0134] In some embodiments, image processing algorithms, such as Hough transform, can be used to reveal that the direction in which the grade and concentration change most significantly is the major axis direction. The major axis direction reflects the dominant direction of the concentration trend of the mineralized elements and usually indicates the extension direction of the ore body; and the spatial tendency refers to the angle between the extension direction of the ore body and the horizontal plane.

[0135] Step S403, determining the lateral direction of the ore body according to the vertical direction of the long axis direction;

[0136] Among them, the lateral direction is the direction perpendicular to the long axis of the grade contour line.

[0137] Step S404, establishing a geological structure model based on the geological structure information and grade contour map of the target mining area;

[0138] Among them, geological structure information includes but is not limited to geological maps of the target mining area, fault distribution, fold axis, rock layer dip and inclination, etc. This information can be obtained through field surveys, remote sensing data, drilling records, etc., and can be digitized and entered into the computer system.

[0139] In one of the embodiments of the present application, a geographic information system (GIS) and three-dimensional geological modeling software can be used to combine these structural information with grade contour maps to form a comprehensive geological structural model. Structural features such as faults and folds will be specially marked in the model. These structural features are usually channels or barriers to fluid migration.

[0140] Step S405, based on the geological structure model, simulating the fluid movement path according to the lateral direction to determine the fluid migration direction;

[0141] Specifically, in computer models, the lateral direction implies the lateral diffusion trend of mineralized fluids and is usually used as a lateral law to constrain the lateral boundary conditions of fluid migration. Combined with the fault direction and fold axis in the geological structure model, the possible path of the fluid in three-dimensional space can be simulated.

[0142] It can be understood that by simulating the fluid pressure and velocity distribution in different flow directions, the fluid movement path that best conforms to the geological structure characteristics and lateral laws can be found, and then the direction of fluid migration can be determined.

[0143] Step S406, obtaining ore body location information according to the spatial inclination, lateral direction and fluid migration direction of the ore body.

[0144] In the above implementation, based on the deep integration of geological principles and modern computing technology, it aims to more accurately predict the movement trajectory of mineralized fluids, integrate geological structure information and lateral laws, realize accurate prediction of fluid migration direction, and provide a scientific basis for in-depth positioning of ore bodies.

[0145] Reference Figure 5 As an implementation method of step S107, the step of predicting the prospecting target area according to the practical model of the superposition halo of the mining area structure and obtaining the prospecting prediction result includes:

[0146] Step S501, determining the prospecting target area according to the model prediction results of the mining area structural superposition halo practical model;

[0147] Step S502, receiving field verification data of the prospecting target area input by the user;

[0148] In some embodiments, drilling or tunnel engineering can be used to verify the predicted prospecting target area, and the user collects field verification data and inputs it into the computer system, such as collecting geological catalogs, ore sample analysis, grade data, especially information such as element content, structure, and mineralization type.

[0149] Step S503, judging whether the actual verification data is consistent with the model prediction result; if so, jumping to step S504; if not, jumping to step S505;

[0150] Step S504, determining the prospecting target area as a prospecting prediction result;

[0151] It can be understood that the field verification data is compared with the prospecting target area results predicted by the model to judge the prediction accuracy. If they are consistent, it can be determined that the model prediction is highly consistent with the actual results, and the prospecting target area predicted by the model can be used as the final prospecting prediction result; if they are inconsistent, the source of the prediction error is identified, such as the deviation or parameter setting in the model.

[0152] Step S505, optimizing and correcting the mining area structural superposition halo practical model according to the field verification data, adjusting the model parameters until the model prediction results are consistent with the fed-back field verification data, and obtaining the optimized mining area structural superposition halo practical model;

[0153] In some embodiments, the anomaly threshold, concentration zoning criteria, element weights, fractal parameters, etc. can be adjusted based on the feedback of field verification data; the element combination can also be re-analyzed, and the primary halo element combination can be adjusted if new element relationships are discovered or certain indicative features are excluded; in addition, if verification shows that the position of the ore body is offset or has a different lateral position, the structural control structure parameters of the model are adjusted to match, such as fault displacement.

[0154] It can be understood that by continuously iteratively optimizing the model, using the optimized model each time to predict new targets again, and using the field verification data of the new targets for cyclic feedback, the accuracy of the prediction is improved until the model prediction results are highly consistent with the actual verification results.

[0155] Step S506, predicting the prospecting target area according to the optimized mining area structural superposition halo practical model, and determining the prospecting target area as the prospecting prediction result.

[0156] In the above implementation mode, the model continuously absorbs feedback from field verification data, gradually approaches the real mineralization law, improves the prediction performance of the practical model of structural superposition halo in the mining area, and provides more accurate guidance for deep mineral exploration.

[0157] Reference Figure 6 , which is a specific technical roadmap for the practical application of the structural superposition halo prospecting prediction method of one of the embodiments of this application. Although there are differences in elemental combinations and zoning sequences among various mineral species (gold, silver, tungsten, etc.) and ore deposit types (magmatic hydrothermal, contact metasomatism, dynamic metamorphism, etc.), the overall research idea of ​​this application is still a process from field investigation to indoor processing, field re-verification, and model re-improvement, which mainly includes the following two parts:

[0158] (1) Conduct detailed field geological surveys, summarize the geological laws of mineralization, and systematically collect samples. By studying the regional geological background, typical ore deposit analysis, and geological characteristics of the mining area, we can understand the regional tectonic-structural framework, geological evolution history, and the structural-stratum-magma relationship of the mining area; further, based on the vein cutting relationship and mineral assemblage characteristics, we can clarify the ore generation sequence, ore body enrichment and superposition characteristics, and their spatial distribution laws. On this basis, we can collect the strongest mineralized-altered samples in the main stage of mineralization along the ore-bearing structural belt.

[0159] (2) Scientific data analysis to clarify the direction of fluid migration and the spatial distribution of elements. This method proposes a CL (concentration-length) fractal model based on fractal theory, and adds sample length as an important weight indicator to each calculation link to make anomaly identification and threshold extraction more accurate; in addition, in view of the closure effect of component data, the logarithmic ratio transformation method is used to open the data and convert the data from simplex space to Euclidean space, which can directly perform mathematical statistical analysis and calculate zoning indexes. Then, grade contour maps, concentration zoning maps and calculated zoning indexes are drawn on the vertical longitudinal projection map to further clarify the direction of fluid migration, discover the lateral law of ore bodies, determine element combinations and axial zoning sequences. According to the above analysis, an ideal primary halo is established, and the prospecting target area is delineated in combination with the geological analysis of the deep favorable mineralization space and the primary halo prospecting indication. Finally, we will go to the field to verify the target area through drilling or tunneling. Based on the verification results, we will further improve the practical model of structural superposition halo. Finally, we will apply this model to other ore bodies or ore belts to guide deep mineral exploration, greatly improving the accuracy of mineral exploration predictions.

[0160] Reference Figure 7 The figure shows a schematic diagram of the practical model of structural superposition halo established in the actual research process of this application. The actual research is based on the Xinjiazui gold mine in Longmenshan. Through the known ore body side-lying law, it is determined that the ore body is side-lying in the northwest direction, and the lateral migration direction of the fluid from the southwest to the northeast is clarified. It is inferred that there is a favorable mineralization space in the depth, and finally established Figure 7 The practical model of structural superposition halos in the mining area shown in the figure shows that there are indicative features such as superposition of leading halo and tail halo, axial zoning anomalies, and turning points of geochemical parameters at a depth of 600 meters. It is predicted that there are blind ore bodies in the depth. Therefore, this favorable mineralization space is determined as the prediction target, providing a scientific basis for the subsequent deployment of mining engineering projects.

[0161] The embodiment of the present application also discloses a structural superposition halo prospecting prediction system.

[0162] A structural superposition halo prospecting prediction system, the system comprising:

[0163] An acquisition module is used to acquire geological sample data in the target mining area; the geological sample data includes sample length information and element concentration information;

[0164] A preprocessing module is used to preprocess geological sample data to obtain element distribution characteristics;

[0165] A fractal model construction module is used to obtain fractal dimension characteristics according to element distribution characteristics and construct a concentration-length fractal model; an abnormal geological sample identification module is used to extract element anomaly thresholds from geological sample data based on the concentration-length fractal model and identify abnormal geological samples;

[0166] The primary halo element combination determination module is used to perform element combination analysis on abnormal geological samples and determine the primary halo element combination based on concentration zoning characteristics, axial zoning sequence and element correlation;

[0167] The ore body location information determination module is used to generate a grade contour map based on geological sample data and determine the ore body location information based on the geometric features of the grade contour map;

[0168] A practical model building module is used to build a practical model of structural superposition halo in a mining area based on element anomaly thresholds, concentration zoning characteristics, axial zoning sequences, primary halo element combinations and ore body location information;

[0169] The module for generating mineral exploration prediction results is used to predict mineral exploration target areas according to the practical model of mineral area structural superposition halo and obtain mineral exploration prediction results.

[0170] In the above implementation mode, geological, geochemical and mathematical models are integrated to form a mineral prospecting prediction system, which effectively solves the difficulties in deep mineral prospecting prediction, especially the optimization of anomaly threshold extraction and element combination determination, as well as the scientific nature of model construction, which significantly improves the accuracy and practicality of mineral prospecting prediction and provides scientific guidance and theoretical basis for actual mining engineering deployment.

[0171] As an implementation of the preprocessing module, the preprocessing module includes:

[0172] A data analysis unit, used to analyze and obtain sample length information and element concentration information based on geological sample data;

[0173] The element total content calculation unit is used to obtain the element total content according to the sample length information and element concentration information of all geological samples;

[0174] The element average concentration information calculation unit is used to obtain the element average concentration information according to the total element content and the sum of the sample length information of all geological samples.

[0175] In the above implementation, processing the element concentration data can better reflect the element distribution characteristics of deep rocks. The calculation process of the element distribution characteristics is an important bridge connecting data preprocessing and model construction.

[0176] The structural superposition halo prospecting prediction system of the embodiment of the present application can implement any of the above-mentioned structural superposition halo prospecting prediction methods, and the specific working process of each module in the structural superposition halo prospecting prediction system can refer to the corresponding process in the above-mentioned method embodiment.

[0177] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0178] The embodiment of the present application also discloses a computer device.

[0179] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the structural superposition halo prospecting prediction method as described above is implemented.

[0180] The embodiment of the present application also discloses a computer-readable storage medium.

[0181] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any of the structural superposition halo prospecting prediction methods described above.

[0182] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0183] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A structural superposition halo prospecting prediction method, characterized in that: The method comprises: Acquire geological sample data in the target mining area and perform data preprocessing to obtain element distribution characteristics; the geological sample data includes sample length information and element concentration information; According to the element distribution characteristics, fractal dimension characteristics are obtained, and a concentration-length fractal model is constructed; The expression of the concentration-length fractal model is: L(>C)∝C -α , that is, there is a power exponential relationship between the sample length L and the concentration C when the element concentration is higher than C, and the power exponent α is the characteristic of fractal dimension; Extracting element anomaly thresholds from the geological sample data based on the concentration-length fractal model, and determining abnormal geological samples; The element concentration data in each geological sample data are sorted, and the cumulative length of samples with length L greater than C at different concentration levels C is calculated; the relationship between concentration and length is displayed through a double logarithmic graph, and three straight lines are fitted, corresponding to the low background area, high background area and high anomaly area respectively; the intersection of the two straight lines is defined as the critical point between the background and the anomaly, that is, the lower limit of the anomaly; according to the fitting results, the anomaly critical point of each element is determined as the element anomaly threshold; Conducting element combination analysis on the abnormal geological samples, and determining the primary halo element combination based on concentration zoning characteristics, axial zoning sequence and element correlation; Generating a grade contour map according to the geological sample data, and determining ore body location information according to geometric features of the grade contour map; Based on the element anomaly threshold, concentration zoning characteristics, axial zoning sequence, primary halo element combination and ore body location information, a practical model of structural superimposed halo in the mining area is constructed; Predicting the prospecting target area according to the practical model of structural superposition halo in the mining area, and obtaining the prospecting prediction result; The data preprocessing includes: According to the geological sample data, analyzing and obtaining sample length information and element concentration information; According to the sample length information and element concentration information of all geological samples, the total element content is obtained; The average element concentration information is obtained based on the total element content and the sum of the sample length information of all geological samples.

2. A method for prospecting and predicting structural superposition halo according to claim 1, characterized in that: The steps of performing element combination analysis on the abnormal geological sample and determining the element combination of the primary halo according to the concentration zoning characteristics, axial zoning sequence and element correlation include: Performing data conversion according to the abnormal data subset of the abnormal geological sample; Generate an element concentration zoning diagram based on the converted abnormal data subset, and identify the concentration zoning characteristics and axial zoning sequence; Performing dimensionality reduction processing on the transformed abnormal data subset, identifying correlations and element combinations between elements, and obtaining element correlation analysis results; According to the concentration zoning characteristics, axial zoning sequence and element correlation analysis results, the elements that show the distribution characteristics of the leading edge, near the ore and tail halo in space are determined, and the primary halo element combination is obtained.

3. A method for prospecting and predicting structural superposition halo according to claim 1, characterized in that: The ore body location information includes the spatial inclination, lateral direction and fluid migration direction of the ore body.

4. A method for prospecting and predicting structural superposition halo according to claim 3, characterized in that: The geological sample data also includes the coordinates of the geological sampling points and the corresponding grade values; The steps of generating a grade contour map according to the geological sample data and determining the ore body location information according to the geometric features of the grade contour map include: Generating a grade contour map based on the coordinates of the geological sampling points and the corresponding grade values; Based on an image processing algorithm, the long axis direction of the grade contour map is identified according to the geometric features of the grade contour map to obtain the spatial inclination of the ore body; Determine the lateral direction of the ore body according to the vertical direction of the long axis direction; Establishing a geological structure model according to the geological structure information of the target mining area and the grade contour map; Based on the geological structure model, the fluid movement path is simulated according to the lateral direction to determine the fluid migration direction; The ore body location information is obtained according to the spatial inclination, lateral direction and fluid migration direction of the ore body.

5. A method for prospecting and predicting structural superposition halo according to any one of claims 1 to 4, characterized in that: The steps of predicting the prospecting target area according to the practical model of the structural superposition halo of the mining area and obtaining the prospecting prediction result include: Determine the prospecting target area according to the model prediction results of the practical model of structural superposition halo of the mining area; Receiving field verification data of the prospecting target area input by a user; Judging whether the field verification data is consistent with the model prediction result, and if so, determining that the prospecting target area is the prospecting prediction result; If not, the mining area structural superposition halo practical model is optimized and corrected according to the field verification data, and the model parameters are adjusted until the model prediction results are consistent with the fed-back field verification data, so as to obtain the optimized mining area structural superposition halo practical model; The prospecting target area is predicted according to the optimized mining area structural superposition halo practical model, and the prospecting target area is determined as the prospecting prediction result.

6. A structural superposition halo prospecting prediction system, characterized in that: A system for performing a method for prospecting and predicting a structural superposition halo according to any one of claims 1 to 5, comprising: An acquisition module is used to acquire geological sample data in a target mining area; the geological sample data includes sample length information and element concentration information; A preprocessing module, used for preprocessing the geological sample data to obtain element distribution characteristics; A fractal model construction module, used to obtain fractal dimension characteristics according to the element distribution characteristics and construct a concentration-length fractal model; An abnormal geological sample identification module is used to extract element abnormality thresholds from the geological sample data based on the concentration-length fractal model and determine abnormal geological samples; A primary halo element combination determination module is used to perform element combination analysis on the abnormal geological sample and determine the primary halo element combination according to concentration zoning characteristics, axial zoning sequence and element correlation; An ore body location information determination module, used to generate a grade contour map according to the geological sample data, and determine the ore body location information according to the geometric features of the grade contour map; A practical model building module is used to build a practical model of structural superposition halo in a mining area based on the element anomaly threshold, concentration zoning characteristics, axial zoning sequence, primary halo element combination and ore body location information; The prospecting prediction result generating module is used to predict the prospecting target area according to the mining area structural superposition halo practical model to obtain the prospecting prediction result; the preprocessing module includes: A data analysis unit, used to analyze the geological sample data to obtain sample length information and element concentration information; The element total content calculation unit is used to obtain the element total content according to the sample length information and element concentration information of all geological samples; The element average concentration information calculation unit is used to obtain the element average concentration information according to the total content of the element and the sum of the sample length information of all geological samples.

7. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the method according to any one of claims 1 to 5 is implemented when the processor executes the program.

8. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 5.

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