A method and system for extracting geological and mineral exploration data

Abstract

The present invention relates to the field of mining technology, and specifically to a method and system for extracting geological and mineral exploration data, comprising the following steps: acquiring rock sample data, standardizing the rock sample data, removing noise and interference from the rock sample data, performing trend analysis on the rock sample data, identifying geological anomalies in the rock sample data, and generating time-dependent analysis results. The present invention enhances the ability to capture subtle changes in geological data, combines standardization with noise removal, and improves the recognition rate of geological anomalies. Trend analysis and anomaly score calculation are used to identify anomalous data points, and simulation and cross-validation are used to ensure the accuracy of the results. This not only improves the accuracy of resource assessments, but also optimizes the mineral resource location process. Simultaneously, through resource abundance prediction and distribution characteristic analysis, the potential of mineral resources is fully tapped.

Description

Technical Field

[0001] The present invention relates to the field of mining technology, and in particular to a method and system for extracting geological and mineral exploration data. Background Art

[0002] The field of mining technology involves the science, technology, and engineering of extracting minerals from the Earth's crust. This includes the exploration, mining, extraction, and processing of various natural resources to obtain useful geological materials.

[0003] However, existing technologies suffer from limited accuracy and operational efficiency in resource assessment, leaving room for improvement in maximizing resource potential. Furthermore, a lack of trend and cluster analysis often results in resource development plans being based on incomplete or inaccurate data, increasing economic burdens and development risks. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for extracting geological and mineral exploration data.

[0005] In order to achieve the above object, the present invention adopts the following technical solution, a method for extracting geological and mineral exploration data, comprising the following steps:

[0006] Acquiring rock sample data, performing standardization processing on the rock sample data, removing noise and interference in the rock sample data, performing trend analysis on the rock sample data, identifying geological anomalies in the rock sample data, and generating time-dependent analysis results;

[0007] Based on the time-dependence analysis result, calculating the anomaly score of each geological anomaly data point in the time-dependence analysis result, identifying the anomaly data point, obtaining an anomaly detection result, simulating and cross-validating the anomaly detection result, and generating a complete anomaly result;

[0008] Performing clustering processing on the complete anomaly results, dividing the complete anomaly results into multiple resource positioning areas, obtaining a mineral area division result, performing centroid analysis on each resource positioning area in the mineral area division result, locating the concentrated area of mineral resources, and generating a mineral area positioning result;

[0009] Based on the mineral area positioning results, the resource richness of each mineral resource concentration area is predicted, the distribution characteristics and potential of the mineral resources in the concentrated area are analyzed, and the resource distribution prediction results are obtained. Based on the resource distribution prediction results, the data is visualized to generate a resource distribution prediction map.

[0010] Preferably, the steps of obtaining the time-dependent analysis results are:

[0011] Acquiring rock sample data, and performing standardization processing on the rock sample data to remove noise and interference, thereby obtaining standardized rock sample data;

[0012] Based on the standardized rock sample data, trend analysis is performed on the standardized rock sample data to identify geological anomalies. The calculation formula is:

[0013] ;

[0014] in, is the anomaly score of the i-th data point, is the value of the i-th data point, is the mean of the sample data, is the standard deviation of the sample data;

[0015] According to the anomaly scores, anomaly points are identified and integrated to generate a time-dependent analysis result of the rock sample data.

[0016] Preferably, the steps for obtaining the abnormality detection result are:

[0017] Based on the time-dependent analysis results, extracting the anomaly score of each data point to obtain a basic anomaly score dataset;

[0018] Each data point in the basic anomaly score dataset is processed using the following formula:

[0019] ;

[0020] in, is the weighted anomaly score of the i-th data point, is the anomaly score of the k-th data point, is the average of the anomaly scores, is the total number of data points;

[0021] Based on the weighted anomaly scores, geological anomaly data points are identified and an anomaly detection result is generated.

[0022] Preferably, the steps for obtaining the complete abnormal result are:

[0023] Based on the anomaly detection results, a simulation test environment is deployed, and simulation runs are performed using past geological data sets to obtain simulation verification results;

[0024] Based on the simulation verification results, a cross-validation process is designed, and the consistency and repeatability of the anomaly detection results are verified in batches using historical data to generate cross-validation results;

[0025] According to the cross-validation results, the detected anomalies are sorted to form a complete anomaly result.

[0026] Preferably, the steps for obtaining the mineral area division result are:

[0027] Based on the complete abnormal results, the abnormal data are grouped, abnormal points with similar geological characteristics are identified and summarized, and resource clustering grouping results are obtained;

[0028] Analyzing the resource clustering grouping results, determining the geological similarity and spatial continuity of abnormal data points within each cluster, and generating refined resource clustering analysis results;

[0029] Based on the refined resource cluster analysis results, resource location areas are defined and divided, and the boundaries and characteristics of each resource location area are determined to obtain mineral area division results.

[0030] Preferably, the steps for obtaining the mineral area positioning result are:

[0031] Based on the mineral area division result, extracting geological data points of each resource positioning area to obtain a geological data point set;

[0032] Based on the set of geological data points, the average geographical location of each data point is calculated to determine the centroid coordinates of each resource location area. The calculation formula is:

[0033] and ;

[0034] in, and are the positions of the centroid of the resource location area in x and y coordinates, respectively. and is the geographic coordinate of the i-th data point, Score the importance of the geological features of the i-th data point, is the total number of data points;

[0035] Based on the centroid coordinates, the concentrated area of mineral resources is determined and a mineral area positioning result is generated.

[0036] Preferably, the steps for obtaining the resource distribution prediction result are:

[0037] Based on the mineral area positioning results, extract the centroid coordinates, geological composition, exploration records and geographical features of each positioning area to obtain a basic data set;

[0038] Based on the basic data set, the resource richness score is calculated using the following formula:

[0039] ;

[0040] in, Score the resource richness of the j-th mineral region, is the geological composition index of the i-th data point, is the weighted coefficient of geological exploration index of the data point, is the total number of data points;

[0041] Based on the resource richness score of each region, the distribution characteristics of mineral resources are quantitatively analyzed to determine the mining potential and economic value of mineral resources and obtain resource distribution prediction results.

[0042] The present invention provides a geological and mineral exploration data extraction system, comprising:

[0043] A data preprocessing module acquires rock sample data, performs standardization on the rock sample data, removes noise and interference in the rock sample data, performs trend analysis on the rock sample data, and generates standardized and trend analysis data;

[0044] The geological anomaly identification module calculates the anomaly score of each data point based on the standardized and trend analysis data, identifies the abnormal data points, simulates and cross-validates the anomaly detection results, generates geological anomaly detection results, and performs cluster analysis on the geological anomaly detection results to obtain clustered anomaly results;

[0045] The mineral resource positioning module, based on clustering anomaly results, performs centroid analysis on each resource positioning area, locates the concentrated areas of mineral resources, predicts the resource richness of each concentrated area of mineral resources, analyzes the distribution characteristics and potential of mineral resources in the concentrated area, and visualizes the data to generate a resource distribution prediction map.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] This invention enhances the ability to capture subtle changes in geological data. By combining standardization with noise removal, it improves the recognition rate of geological anomalies. Trend analysis and anomaly score calculation are used to identify anomalous data points, and simulation and cross-validation ensure the accuracy of the results. This not only improves the accuracy of resource assessments but also optimizes the mineral resource location process. Furthermore, through resource abundance prediction and distribution characteristic analysis, the potential of mineral resources can be fully explored. The generation of resource distribution prediction maps provides intuitive visual support for decision-making, thereby improving the efficiency and cost-effectiveness of mineral exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] See also Figure 1 The present invention provides a technical solution, a method for extracting geological and mineral exploration data, comprising the following steps:

[0051] Acquire rock sample data, standardize the rock sample data, remove noise and interference in the rock sample data, perform trend analysis on the rock sample data, identify geological anomalies in the rock sample data, and generate time-dependent analysis results;

[0052] Based on the time-dependence analysis results, the anomaly score of each geological anomaly data point in the time-dependence analysis results is calculated, the anomaly data points are identified, anomaly detection results are obtained, and the anomaly detection results are simulated and cross-validated to generate complete anomaly results;

[0053] Perform clustering processing on the complete anomaly results, divide the complete anomaly results into multiple resource positioning areas, obtain the mineral area division results, perform centroid analysis on each resource positioning area in the mineral area division results, locate the concentrated area of mineral resources, and generate the mineral area positioning results;

[0054] Based on the results of mineral area positioning, the resource richness of each mineral resource concentration area is predicted, the distribution characteristics and potential of mineral resources in the concentrated area are analyzed, and the resource distribution prediction results are obtained. Based on the resource distribution prediction results, the data is visualized and a resource distribution prediction map is generated.

[0055] The steps for obtaining the time-dependent analysis results are:

[0056] Acquiring rock sample data, and performing standardization processing on the rock sample data to remove noise and interference, thereby obtaining standardized rock sample data;

[0057] Based on the standardized rock sample data, trend analysis is performed on the standardized rock sample data to identify geological anomalies. The calculation formula is:

[0058] ;

[0059] in, is the anomaly score of the i-th data point, is the value of the i-th data point, is the mean of the sample data, is the standard deviation of the sample data;

[0060] According to the anomaly scores, outliers are identified and integrated to generate time-dependent analysis results of rock sample data.

[0061] Specifically, rock sample data is obtained and immediately sent to the laboratory for analysis. The laboratory first uses a scanning electron microscope to perform high-resolution imaging of the sample. The imaging data, supplemented by spectral analysis, can accurately identify the mineral composition and texture structure of the sample. Afterwards, the sample data is cleaned through an algorithm to remove various potential noise and environmental interference caused by the equipment, such as electromagnetic interference and temperature changes. This step ensures the accuracy of subsequent data processing, and the generated result is the cleaned standardized rock sample data.

[0062] formula: The benefits of this are that the degree of deviation of each rock sample data point from the group average can be calculated. This deviation is standardized and reflects the degree of abnormality of the data point, which is crucial for the detection of geological anomalies because it allows researchers to quickly identify areas that may contain mineral resources. The values of specific data points obtained by laboratory scanning electron microscopy and spectral analysis; parameters The parameter is obtained by taking the arithmetic mean of all rock sample data points; is obtained by calculating the standard deviation of all sample data points.

[0063] Calculation process: There is a set of data points in the sample , obtained through data monitoring, calculate the average value and standard deviation ,For example , , and calculate the first data point Anomaly score:

[0064] ;

[0065] The results show that represent A value of one standard deviation above the mean indicates a geological anomaly.

[0066] Based on the anomaly score of each data point, points with scores above a set threshold are marked as significant geological anomalies by comparing them. This threshold is derived through statistical analysis of historical geological data. For example, a threshold of 2 is set, meaning that a data point's score must be greater than 2 to be considered a significant anomaly. These marked points are then integrated and analyzed, combining geological maps with existing geological information to determine the mineral resources that may exist at these anomaly points. The resulting result is a time-dependent analysis of the rock sample data, which is provided to mineral exploration companies as a basis for further exploration decisions.

[0067] The steps to obtain anomaly detection results are:

[0068] Based on the time-dependent analysis results, the anomaly score of each data point is extracted to obtain the basic anomaly score dataset;

[0069] Each data point in the basic anomaly score dataset is processed using the following formula:

[0070] ;

[0071] in, is the weighted anomaly score of the i-th data point, is the anomaly score of the k-th data point, is the average of the anomaly scores, is the total number of data points;

[0072] Based on the weighted anomaly scores, geological anomaly data points are identified and anomaly detection results are generated.

[0073] Specifically, based on the time-dependent analysis results of the rock sample data, the anomaly score of each data point is extracted. This process first requires the establishment of a basic data set containing all data points. This data set contains the values of each data point identified by the trend analysis in the previous step. The extraction of each data point is based on the degree of its deviation from the average trend line. The anomaly score is obtained by calculating the difference between the data point and the trend line. The larger these values, the more obvious the anomaly of the point. The calculation of the anomaly score is not just a simple difference, but also includes the relative position of the point in the overall data set and its relative relationship with the neighboring points. These calculations are completed in a continuous iteration and comparison process to obtain a basic anomaly score data set.

[0074] formula The benefit of this method is that by calculating the sum of the squares of the deviations of each data point from the mean and then taking the square root of the mean, it can effectively enhance the sensitivity to outliers. Especially when the distribution of data points is uneven, it can more accurately identify true geological anomalies, thereby reducing misjudgments in geological exploration.

[0075] Calculation process: Setting is the specific abnormality score, is the average of all anomaly scores, is the total number of data points. For example, if there are 5 data points with scores {2, 4, 5, 8, 11}, then the average , the squares of the deviations of the scores of each data point from the mean are {16, 4, 1, 4, 25}, the sum is 50, the average is 10, and the square root is 3.16, so The calculated value of represents the weighted anomaly score.

[0076] The weighted anomaly score of 3.16 indicates that the significance of geological anomalies in this sample is significantly higher than the average level. If this value is larger, it means that the anomalies are more concentrated and significant, otherwise they are more widely distributed and less obvious.

[0077] Based on the weighted anomaly score Si, the threshold analysis method is applied to identify significant geological anomaly data points. This step first sets a threshold, which is usually derived from historical data analysis to ensure that the threshold can cover most possible anomalies while not causing misjudgment due to the threshold being set too low. Data points with a weighted anomaly score greater than or equal to this threshold are identified as geological anomalies. This judgment criterion is achieved by comparing the relationship between the weighted score of each data point and the threshold. Through this method, abnormal data points with geological exploration value can be efficiently and accurately screened out to generate anomaly detection results.

[0078] The steps to obtain complete abnormal results are:

[0079] Based on the anomaly detection results, a simulation test environment is deployed and simulation runs are performed using past geological datasets to obtain simulation verification results.

[0080] Based on the simulation verification results, a cross-validation process is designed, and the consistency and repeatability of the anomaly detection results are verified in batches using historical data to generate cross-validation results;

[0081] According to the cross-validation results, the detected anomalies are sorted to form a complete anomaly result.

[0082] Specifically, based on the anomaly detection results, a simulation test environment was deployed to verify the reliability of the results. Historical geological data sets were used for simulation to actually verify the anomaly detection results. The simulation test included running a variety of geological data. These historical data contained geological samples from different regions and different ages. Each sample was pre-marked with anomalies by geological experts for comparison and analysis. During the test, the anomaly detection results were compared with the expert markings. The statistical analysis included precision, recall rate and F1 score. These statistical indicators reflect the model's ability to identify geological anomalies. The preliminary simulation verification results show the performance of the anomaly detection model on different data sets and provide a preliminary assessment of the model's stability and reliability.

[0083] Based on the preliminary simulation verification results, a cross-validation process was designed. The process was carried out using a historical geological dataset to test the consistency and repeatability of the anomaly detection results with different batches of data. This cross-validation involves dividing the dataset into multiple subsets, alternating between using certain subsets as training data and the remaining subsets as test data. In this way, the performance of the model on new, unseen data can be evaluated. During the cross-validation process, each iteration generates a verification result, which is used to calculate average indicators of model performance, such as average precision and average recall. Through these indicators, the overall performance of the anomaly detection results can be accurately estimated, and finally the cross-validation results are generated.

[0084] The overall performance of the anomaly detection model was evaluated through comprehensive simulation testing and cross-validation results. This comprehensive evaluation covered metrics obtained from multiple test cycles and datasets, such as overall precision, recall, and the model's ability to identify new anomalies. By statistically analyzing these metrics, we can fully understand the effectiveness and reliability of the model in practical applications. In addition, a detailed statistical analysis of the detected anomalies was performed, including the frequency and distribution of anomaly types and possible geological causes. This comprehensive analysis helps geologists and engineers improve the identification methods of geological anomalies and ultimately form a complete anomaly result.

[0085] The steps to obtain the mineral area division results are:

[0086] Based on the complete anomaly results, the anomaly data is grouped, anomaly points with similar geological characteristics are identified and summarized, and resource clustering grouping results are obtained;

[0087] Analyze the resource clustering grouping results, determine the geological similarity and spatial continuity of abnormal data points within each cluster, and generate detailed resource cluster analysis results;

[0088] Based on the refined resource cluster analysis results, the resource positioning areas are defined and divided, the boundaries and characteristics of each resource positioning area are determined, and the mineral area division results are obtained.

[0089] Specifically, based on the complete anomaly results, the K-means clustering algorithm is used to group the anomaly data. First, K cluster centers are initialized, and the data points are assigned to the nearest cluster center according to the Euclidean distance between each data point and the cluster center. The first round of clustering is performed, and the position of the cluster center is optimized through multiple iterations until the cluster center is stable. The mean of each cluster is calculated in each iteration, and the cluster center is re-determined. This process is repeated until the sum of the distances from each data point to its cluster center is minimized. This process effectively summarizes anomalies with similar geological characteristics, thereby obtaining preliminary resource clustering grouping results.

[0090] In the preliminary resource clustering grouping results, each cluster is deeply analyzed to evaluate the abnormal data points within each cluster. By calculating the similarity of geographical and geological indicators between points in the cluster, statistical methods are used to compare the standard deviation and coefficient of variation of data points in each cluster to ensure the spatial continuity and consistency of geological characteristics of the clusters. In this way, it is confirmed whether each cluster represents a single geological structure. If so, the cluster is further refined. If not, the clustering parameters are adjusted and re-clustered. Through this process, it is ensured that each cluster represents a potential mineral resource area, and finally a refined resource clustering analysis result is generated.

[0091] Based on the results of the detailed resource cluster analysis, the specific boundaries of each mineral resource positioning area are determined. The geological map and resource distribution map of each area are drawn using GIS tools. The boundaries of each cluster area are accurately divided. The geological data and historical mineral mining data in the area are analyzed, and the mineral resource potential and mining value of each area are evaluated. Finally, the characteristics and boundaries of each resource positioning area are defined based on these data to accurately obtain the results of mineral area division.

[0092] The steps to obtain the mineral area positioning results are:

[0093] Based on the mineral area division results, the geological data points of each resource location area are extracted to obtain a geological data point set;

[0094] Based on the set of geological data points, the average geographical location of each data point is calculated to determine the centroid coordinates of each resource location area. The calculation formula is:

[0095] and ;

[0096] in, and are the positions of the centroid of the resource location area in x and y coordinates, respectively. and is the geographic coordinate of the i-th data point, Score the importance of the geological features of the i-th data point, is the total number of data points;

[0097] Based on the centroid coordinates, the concentrated area of mineral resources is determined and the mineral area positioning results are generated.

[0098] Based on the results of the mineral area division, the geological data points of each resource positioning area are analyzed. These data points include geographic coordinates and related geological features. This process ensures an accurate understanding of the mineral resources. By carefully extracting the geological data of each resource positioning area, a complete set of geological data points can be obtained. This set provides the necessary basic data for subsequent analysis. This data set reflects the geological characteristics and potential resource distribution of each area, which is a prerequisite for centroid analysis.

[0099] The benefit of the formula is that it can more accurately determine the geographical center of the resource location area through the weighted average of the geographical coordinates and the geological feature importance score, which helps to accurately locate the concentrated area of mineral resources, thereby improving the efficiency and accuracy of resource development; parameters and The geographic coordinates representing each data point are usually obtained through field measurements or remote sensing data; parameters Indicates the importance score of geological features, which is based on the analysis of geological exploration data and historical geological information; parameter is the total number of data points, which is obtained by counting all available geological sample points.

[0100] Calculation process: There are three data points, whose coordinates and feature importance scores are (10,3), (15,5), and (20,2), respectively. Then calculate C x and C y as follows:

[0101] ;

[0102] ;

[0103] The results show that the x-coordinate of the centroid is 14.5 and the y-coordinate is 24.5, which indicates the exact center position of the resource location area, facilitating resource location and development in practical applications.

[0104] Determine the concentrated area of mineral resources based on the centroid coordinates and analyze how the centroid coordinates affect the final positioning of the mineral area. This process not only reflects the weighted average position of each data point in terms of geographical and geological characteristics, but also establishes the effective distribution of mineral resources. The generated mineral area positioning results are helpful for subsequent resource development planning and management, providing a clear geographic reference to ensure the effectiveness and feasibility of resource development.

[0105] The steps to obtain resource distribution prediction results are:

[0106] Based on the mineral area positioning results, the centroid coordinates, geological composition, exploration records and geographical features of each positioning area are extracted to obtain the basic data set;

[0107] Based on the basic data set, the resource richness score is calculated using the following formula:

[0108] ;

[0109] in, Score the resource richness of the j-th mineral region, is the geological composition index of the i-th data point, is the weighted coefficient of geological exploration index of the data point, is the total number of data points;

[0110] Based on the resource richness score of each region, the distribution characteristics of mineral resources are quantitatively analyzed to determine the mining potential and economic value of mineral resources and obtain resource distribution prediction results.

[0111] Specifically, based on the mineral area positioning results, the centroid coordinates and relevant geological data of each positioning area are extracted, including geological composition, historical exploration records and geographical features. These data come from existing geological exploration archives and GIS analysis. These data will be used to construct an analysis model for resource richness. The basic data set for resource richness analysis includes geological composition data and historical exploration data of hundreds of sample points collected from different mining areas. These data will be screened and verified through evaluation by geological experts to ensure their accuracy and relevance, resulting in a set of highly accurate data sets for analysis.

[0112] formula The benefit of [1] is that it emphasizes the importance of data points with higher geological composition index through the square sum weighted method, and effectively balances the contribution of each data point in resource richness assessment through the averaging of the number of overall data points, which is especially important in mining areas with complex geological structures. The geological composition index for each data point is obtained through geological sample analysis and historical data records; parameters is the weighting coefficient of geological exploration indicators, which is obtained based on the historical exploration effectiveness and the importance score of geological data; parameter is the total number of data points, directly obtained from the data set.

[0113] Calculation process: Assume that there are 10 data points in a specific mineral area, and the geological composition index of each point and geological exploration index weighting coefficient They are as follows:

[0114] ;

[0115] ;

[0116] The simplified calculation process is as follows:

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] The results show that the average resource richness score of the mineral area is 0.782, which indicates that the area has moderate resource potential and is a worthy target for mining companies to further explore.

[0122] Based on the resource richness score of each area, the distribution characteristics of mineral resources are quantitatively analyzed to evaluate the mining potential and economic value of the resources. This process includes sorting the resource richness scores in each mineral area and conducting a detailed geological structure analysis of high-scoring areas to determine the mining priority and investment value of these areas. Areas with high resource richness will be marked as high-potential areas. These areas will be recommended to the management team for further detailed exploration. The resulting resource distribution forecast results provide a scientific basis for mining development and provide important information for investment decisions.

[0123] The steps to obtain the resource distribution prediction map are:

[0124] Extract the resource richness score and geographic coordinates of each mineral area from the resource distribution prediction results and compile them into a basic data set;

[0125] Import the basic data set into the GIS software, set the map scale, color gradient and legend of the GIS software, and generate a resource distribution prediction map.

[0126] Specifically, the resource richness score and geographical coordinates of each mineral area are extracted from the resource distribution prediction results. This step first involves accessing the prediction results stored in the database. By writing SQL query statements, records containing resource richness and its corresponding geographical coordinates are selected to ensure that the extracted data accurately corresponds to each mineral area. The query operation requires special attention to the accuracy and execution efficiency of the SQL statement to avoid redundancy and errors in the data extraction process. At the same time, the extracted data must be subject to necessary data verification and format adjustment to meet the input requirements of subsequent processing steps and organized into a basic data set.

[0127] Import the basic data set into the GIS software. This step includes loading the organized basic data set through the data import tool of the GIS software. Before importing, it is necessary to set the appropriate data format and coordinate system to ensure that the data can be correctly mapped to the geographic space. Then, set the visual parameters of the map in the GIS software, such as scale, color gradient and legend. These parameters are set based on pre-defined visual standards to ensure that the information display of the map is both beautiful and practical. By adjusting the color gradient to reflect the levels of different resource richness, the readability of the map and the effectiveness of information transmission are enhanced. After completing these settings, use the drawing function of the GIS software to generate the final resource distribution prediction map, which clearly shows the distribution of each mineral resource and its richness level.

[0128] The present invention provides a geological and mineral exploration data extraction system, comprising:

[0129] A data preprocessing module acquires rock sample data, performs standardization on the rock sample data, removes noise and interference in the rock sample data, performs trend analysis on the rock sample data, and generates standardized and trend analysis data;

[0130] The geological anomaly identification module calculates the anomaly score of each data point based on the standardized and trend analysis data, identifies the abnormal data points, simulates and cross-validates the anomaly detection results, generates geological anomaly detection results, and performs cluster analysis on the geological anomaly detection results to obtain clustered anomaly results;

[0131] The mineral resource positioning module, based on clustering anomaly results, performs centroid analysis on each resource positioning area, locates the concentrated areas of mineral resources, predicts the resource richness of each concentrated area of mineral resources, analyzes the distribution characteristics and potential of mineral resources in the concentrated area, and visualizes the data to generate a resource distribution prediction map.

[0132] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for extracting geological and mineral exploration data, characterized in that: The following steps are involved: Acquiring rock sample data, performing standardization processing on the rock sample data, removing noise and interference in the rock sample data, performing trend analysis on the rock sample data, identifying geological anomalies in the rock sample data, and generating time-dependent analysis results; Based on the time-dependence analysis result, calculating the anomaly score of each geological anomaly data point in the time-dependence analysis result, identifying the anomaly data point, obtaining an anomaly detection result, simulating and cross-validating the anomaly detection result, and generating a complete anomaly result; Performing clustering processing on the complete anomaly results, dividing the complete anomaly results into multiple resource positioning areas, obtaining a mineral area division result, performing centroid analysis on each resource positioning area in the mineral area division result, locating the concentrated area of mineral resources, and generating a mineral area positioning result; Based on the mineral area positioning results, predict the resource richness of each mineral resource concentration area, analyze the distribution characteristics and potential of mineral resources in the concentration area, obtain resource distribution prediction results, and visualize the data based on the resource distribution prediction results to generate a resource distribution prediction map; The steps for obtaining the abnormality detection results are: Based on the time-dependent analysis results, extracting the anomaly score of each data point to obtain a basic anomaly score dataset; Each data point in the basic anomaly score dataset is processed using the following formula: ； in, is the weighted anomaly score of the i-th data point, is the anomaly score of the k-th data point, is the average of the anomaly scores, is the total number of data points; Based on the weighted anomaly scores, identifying geological anomaly data points and generating anomaly detection results; The steps for obtaining the mineral area positioning result are: Based on the mineral area division result, extracting geological data points of each resource positioning area to obtain a geological data point set; Based on the set of geological data points, the average geographical location of each data point is calculated to determine the centroid coordinates of each resource location area. The calculation formula is: ； and ； in, and are the positions of the centroid of the resource location area in x and y coordinates, respectively. and is the geographic coordinate of the i-th data point, Score the importance of the geological features of the i-th data point, is the total number of data points; Based on the centroid coordinates, determine the concentrated area of mineral resources and generate a mineral area positioning result; The steps for obtaining the resource distribution prediction result are: Based on the mineral area positioning results, extract the centroid coordinates, geological composition, exploration records and geographical features of each positioning area to obtain a basic data set; Based on the basic data set, the resource richness score is calculated using the following formula: ； in, Score the resource richness of the j-th mineral region, is the geological composition index of the i-th data point, is the weighted coefficient of geological exploration index of the data point, is the total number of data points; Based on the resource richness score of each region, the distribution characteristics of mineral resources are quantitatively analyzed to determine the mining potential and economic value of mineral resources and obtain resource distribution prediction results.

2. The method for extracting geological and mineral exploration data according to claim 1, wherein: The steps for obtaining the time-dependent analysis results are: Acquiring rock sample data, and performing standardization processing on the rock sample data to remove noise and interference, thereby obtaining standardized rock sample data; Based on the standardized rock sample data, trend analysis is performed on the standardized rock sample data to identify geological anomalies. The calculation formula is: ； in, is the anomaly score of the i-th data point, is the value of the i-th data point, is the mean of the sample data, is the standard deviation of the sample data; According to the anomaly scores, anomaly points are identified and integrated to generate a time-dependent analysis result of the rock sample data.

3. The method for extracting geological and mineral exploration data according to claim 1, wherein: The steps for obtaining the complete abnormal result are: Based on the anomaly detection results, a simulation test environment is deployed, and simulation runs are performed using past geological data sets to obtain simulation verification results; Based on the simulation verification results, a cross-validation process is designed, and the consistency and repeatability of the anomaly detection results are verified in batches using historical data to generate cross-validation results; According to the cross-validation results, the detected anomalies are sorted to form a complete anomaly result.

4. The method for extracting geological and mineral exploration data according to claim 1, wherein: The steps for obtaining the mineral area division result are as follows: Based on the complete abnormal results, the abnormal data are grouped, abnormal points with similar geological characteristics are identified and summarized, and resource clustering grouping results are obtained; Analyzing the resource clustering grouping results, determining the geological similarity and spatial continuity of abnormal data points within each cluster, and generating refined resource clustering analysis results; Based on the refined resource cluster analysis results, resource location areas are defined and divided, and the boundaries and characteristics of each resource location area are determined to obtain mineral area division results.

5. A geological and mineral exploration data extraction system according to the geological and mineral exploration data extraction method according to any one of claims 1 to 4, characterized in that: include: A data preprocessing module acquires rock sample data, performs standardization on the rock sample data, removes noise and interference in the rock sample data, performs trend analysis on the rock sample data, and generates standardized and trend analysis data; The geological anomaly identification module calculates the anomaly score of each data point based on the standardized and trend analysis data, identifies the abnormal data points, simulates and cross-validates the anomaly detection results, generates geological anomaly detection results, and performs cluster analysis on the geological anomaly detection results to obtain clustered anomaly results; The mineral resource positioning module, based on clustering anomaly results, performs centroid analysis on each resource positioning area, locates the concentrated areas of mineral resources, predicts the resource richness of each concentrated area of mineral resources, analyzes the distribution characteristics and potential of mineral resources in the concentrated area, and visualizes the data to generate a resource distribution prediction map.

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