An intelligent mineral resource exploration and evaluation system

Abstract

The present invention provides an intelligent mineral resource exploration and evaluation system, which relates to the field of data processing technology, and includes: a data preprocessing module, which is used to preprocess data to obtain preprocessed data; a stress analysis module, which is used to analyze the rock physical properties and geological structural stress state of the exploration area according to the preprocessed data to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated; a resource reserve estimation module, which is used to estimate the mineral resource reserves according to the dynamic adjustment coefficient; a resource evaluation module, which is used to evaluate the mineral resource reserves to obtain the final evaluation result. The present invention realizes the accurate analysis of the rock physical properties and the geological structural stress state, thereby greatly improving the accuracy and reliability of the mineral resource exploration and evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent mineral resource exploration and evaluation system. Background Art

[0002] The existing intelligent mineral resource exploration and evaluation systems still have certain difficulties in dealing with complex geological conditions, dynamically adjusting evaluation parameters, and improving the real-time and accuracy of evaluation results.

[0003] For example, geological conditions are complex and changeable, including multiple factors such as stratum structure, rock type, and fault structure, which have a significant impact on the distribution and reserves of mineral resources. Some existing systems are difficult to fully and accurately consider these complex factors, resulting in biased assessment results.

[0004] For example, in the process of mineral resource exploration and evaluation, it is necessary to dynamically adjust the evaluation parameters according to the actual situation. However, some existing systems lack a flexible and efficient parameter adjustment mechanism, resulting in the inability of the evaluation results to reflect the changes in geological conditions in a timely manner. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an intelligent mineral resource exploration and evaluation system, which realizes the accurate analysis of rock physical properties and geological structure stress state, thereby greatly improving the accuracy and reliability of mineral resource exploration and evaluation.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, an intelligent mineral resource exploration and evaluation system is provided, the system comprising:

[0008] Data collection module, used to collect data related to geological and mineral exploration from various data sources;

[0009] A data preprocessing module, used for preprocessing data to obtain preprocessed data;

[0010] The stress analysis module is used to analyze the rock physical properties and geological structural stress state of the exploration area based on the preprocessed data to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated;

[0011] Resource reserve estimation module, used to estimate mineral resource reserves based on dynamic adjustment coefficients;

[0012] The resource assessment module is used to assess the mineral resource reserves to obtain the final assessment results.

[0013] Furthermore, based on the preprocessed data, the rock physical properties and geological structural stress state of the exploration area are analyzed to obtain analysis results, including:

[0014] Screening out data sets related to rock physical properties and geological structural stress states from preprocessed data;

[0015] Based on the data sets related to rock physical properties, calculate the statistics of various rock physical properties, and identify the areas with abnormal changes in physical properties by comparing the rock physical property data of different regions;

[0016] Analyze the geological structural stress state in the survey area using seismic data and terrain deformation monitoring data;

[0017] Through geological stress field simulation, the geological structural stress state of the exploration area is analyzed to obtain the analysis results of the stress state;

[0018] According to the analysis results of the stress state, the corresponding stress release area is identified;

[0019] The results of the rock physical property analysis and the geological structure stress state analysis are analyzed to obtain the correlation between the rock physical properties and the geological structure stress state.

[0020] Furthermore, according to the correlation between rock physical properties and geological structural stress state, the dynamic adjustment coefficient is calculated, including:

[0021] Based on the correlation between rock physical properties and geological structural stress state, Calculate dynamic adjustment coefficient;

[0022] in, ; ; ; The exploration area The measured density of the points; is the base density; The exploration area The measured conductivity of the point; is the average conductivity of the survey area; The exploration area Measurement of geological stress at points; is the average stress value in the survey area; , and is the weight coefficient; is an index of the amount of change in rock density; is the total amount of rock density variation; is an index of rock conductivity; is the total of the rock conductivity; It is an index of the amount of change in geological stress; is the total amount of geological stress change.

[0023] Furthermore, the mineral resource reserves are estimated based on the dynamic adjustment coefficient, including:

[0024] Arrange exploration sections within the exploration area so that the exploration sections reflect the morphology and distribution characteristics of the ore body;

[0025] According to the morphology and distribution characteristics of the ore body, each exploration section is measured to obtain measurement data, which includes the area of ​​the section, the thickness of the ore body and the grade information;

[0026] Based on the measurement data and the distribution of ore bodies in each section, the area of ​​the ore body on the section is calculated;

[0027] The ore block reserves between two adjacent sections are calculated by using the ore body area on two adjacent sections and the distance between the sections;

[0028] Mineral resource reserves are calculated based on the dynamic adjustment coefficient and the block reserves.

[0029] Furthermore, the mineral resource reserves are calculated based on the dynamic adjustment coefficient and the ore block reserves, including:

[0030] According to the dynamic adjustment coefficient and the reserves of the ore block, Calculate mineral resource reserves;

[0031] in, Indicates the total reserves of mineral resources; Indicates The ore body area of ​​each block; Indicates The thickness of the ore body of each block; Indicates The ore density of each block; Indicates The grade of the ore block; Indicates the number of the ore block; Indicates the total number of blocks.

[0032] Furthermore, data related to geological and mineral exploration include topographic data, drilling data, geophysical data, geochemical data and remote sensing image data.

[0033] Furthermore, data sets related to rock physical properties and geological structural stress states include rock density, magnetism, and electrical conductivity, as well as seismic data and topographic deformation data.

[0034] In the second aspect, an intelligent mineral resource exploration and evaluation method is applied to the system, comprising:

[0035] Collect data related to geological and mineral exploration from various data sources;

[0036] Preprocessing the data to obtain preprocessed data;

[0037] According to the pre-processed data, the rock physical properties and geological structural stress state of the exploration area are analyzed to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated;

[0038] Estimate mineral resource reserves based on dynamic adjustment coefficients;

[0039] Evaluate the mineral resource reserves to obtain the final evaluation results.

[0040] According to a third aspect, a computing device includes:

[0041] one or more processors;

[0042] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0043] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0044] The above scheme of the present invention includes at least the following beneficial effects.

[0045] The present invention can efficiently collect data related to geological and mineral exploration from multiple data sources through the data acquisition module. This diversified data acquisition method not only improves the efficiency of data acquisition, but also can more comprehensively obtain geological information of the exploration area.

[0046] The present invention can perform pre-processing operations such as cleaning, integration and standardization on the collected raw data. The optimization of this process can improve the quality of the data.

[0047] The stress analysis module of the present invention can analyze the rock physical properties and geological structural stress state of the exploration area and reveal the correlation between the two. This enhanced analysis capability helps to more accurately understand the geological characteristics of the exploration area.

[0048] According to the correlation between rock physical properties and geological structural stress state, the present invention can calculate the dynamic adjustment coefficient and apply it to the resource reserve estimation process. This dynamic adjustment mechanism makes the resource reserve estimation more flexible and accurate, and can better meet the assessment needs under different geological conditions.

[0049] Through the comprehensive application of the resource reserve estimation module and the resource assessment module, the present invention can realize the rapid and accurate assessment of mineral resource reserves. This assessment method not only improves the accuracy of the assessment, but also greatly improves the efficiency of the assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of an intelligent mineral resource exploration and evaluation system provided by an embodiment of the present invention.

[0051] Figure 2 It is a flow chart of an intelligent mineral resource exploration and evaluation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] like Figure 1 As shown, an embodiment of the present invention provides an intelligent mineral resource exploration and evaluation system, comprising:

[0054] The data acquisition module is used to collect data related to geological and mineral exploration from various data sources. The data related to geological and mineral exploration include topographic data, drilling data, geophysical data, geochemical data and remote sensing image data;

[0055] A data preprocessing module, used for preprocessing data to obtain preprocessed data;

[0056] The stress analysis module is used to analyze the rock physical properties and geological structural stress state of the exploration area based on the preprocessed data to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated;

[0057] Resource reserve estimation module, used to estimate mineral resource reserves based on dynamic adjustment coefficients;

[0058] The resource assessment module is used to assess the mineral resource reserves to obtain the final assessment results.

[0059] In an embodiment of the present invention, the data acquisition module can comprehensively and efficiently collect data related to geological and mineral exploration from a variety of data sources (such as terrain data, drilling data, geophysical data, geochemical data, and remote sensing image data). This diversified data acquisition method ensures the richness and diversity of data. The data preprocessing module can perform preprocessing operations such as cleaning, integration, and standardization on the collected raw data, effectively remove noise and outliers in the data, and improve data quality. The preprocessed data is more standardized and unified, which is convenient for subsequent modules to perform efficient data analysis and processing, and further ensures the accuracy and efficiency of mineral resource exploration and evaluation. The stress analysis module can deeply analyze the rock physical properties and geological structural stress state of the exploration area and reveal the correlation between the two. This in-depth analysis helps to more accurately understand the geological characteristics and the distribution law of mineral resources; at the same time, the dynamic adjustment coefficient based on correlation calculation can more truly reflect the impact of geological conditions on mineral resources, further improving the accuracy of the evaluation. The resource reserve estimation module uses dynamic adjustment coefficients to estimate mineral resource reserves, fully considering the influence of complex factors such as geological structure and rock physical properties. This estimation method is more flexible and scientific, and can more accurately predict the actual reserves of mineral resources. The resource assessment module conducts a comprehensive assessment of mineral resource reserves and obtains the final assessment results. By comprehensively considering multiple factors, this module can provide a more comprehensive and objective assessment report. At the same time, the accuracy and reliability of the assessment results have also been significantly improved.

[0060] The specific implementation process of the data acquisition module is as follows:

[0061] Determine the data source:

[0062] Topographic data: obtained from geological survey and mapping departments or public topographic databases.

[0063] Drillhole data: collected from geological survey teams or historical survey reports.

[0064] Geophysical data: collected on-site through geophysical exploration methods (such as gravity exploration, magnetic exploration, etc.) or obtained through relevant institutions.

[0065] Geochemical data: obtained through laboratory analysis of soil, rock and other samples, or queried from geochemical databases.

[0066] Remote sensing image data: downloaded from satellite remote sensing platforms.

[0067] Integrate the data collected from different data sources to ensure data consistency and integrity; check whether there are missing values, outliers or duplicate values ​​in the data, and perform corresponding processing. For missing values, choose to fill (such as mean filling, interpolation filling, etc.) or delete according to the characteristics and importance of the data. For outliers, use statistical methods (such as Z-score, IQR, etc.) to identify them and correct or eliminate them according to the actual situation. Convert data from different data sources to the same format and unit for subsequent analysis and processing. For categorical data, perform encoding conversion (such as one-hot encoding, label encoding, etc.), and for continuous data, perform scaling (such as normalization, standardization, etc.) to eliminate dimensional differences; integrate the cleaned and converted data to ensure that the data between different data sources can correspond correctly.

[0068] In a preferred embodiment of the present invention, the rock physical properties and geological structural stress state of the exploration area are analyzed based on the preprocessed data to obtain analysis results, including:

[0069] Data sets related to rock physical properties and geological structural stress states are screened out from the preprocessed data. The data sets related to rock physical properties and geological structural stress states include rock density, magnetism and conductivity, as well as seismic data and terrain deformation data, including:

[0070] Open the overall preprocessed dataset and first browse the labels (column names or field names) of the data; identify fields related to rock physical properties, such as "density", "magnetism", "conductivity", etc.; similarly, identify fields related to geological tectonic stress states, which may include "earthquake event time", "earthquake magnitude", "earthquake focal depth", "GPS deformation data", "InSAR deformation data", etc.; use the filtering function of data processing software (such as Python's pandas library, Excel, etc.) to filter out the corresponding data columns according to the field names identified in the previous step; for each filtered data column, check its data type and format to ensure that they are suitable for subsequent analysis.

[0071] Perform a preliminary accuracy check on the selected data subsets to check whether there are obvious erroneous values ​​(such as impossible physical property values, unreasonable values ​​in seismic data, etc.), and verify the rationality of the data by comparing with the original data records or relevant literature. Check whether there are missing values ​​(null values ​​or NaN values) in the data subset; use statistical methods (such as Z-score, IQR, etc.) to identify outliers in the data, and for outliers, determine whether they are valid data points. If the anomaly is caused by measurement error or data entry error, make corrections; if it is a real extreme case, decide whether to keep it based on analysis needs. Ensure that the formats of all data subsets are unified, such as date format, numerical format, etc. For data that requires unit conversion (such as units of physical properties such as density, magnetism, conductivity, etc.), convert them to ensure that all data use the same metric; integrate the data subsets that have been quality checked and cleaned into a new data set.

[0072] Based on the data set related to rock physical properties, the statistics of various rock physical properties are calculated. By comparing the rock physical property data of different regions, the areas with abnormal changes in physical properties can be identified, including:

[0073] Import the selected rock physical property data set (including density, magnetism, and conductivity) into statistical analysis software (such as Python's pandas and numpy libraries, Excel, SPSS, etc.); ensure that each column of the data set correctly corresponds to the corresponding physical property and that the data type is suitable for statistical analysis.

[0074] For each physical property (density, magnetism, conductivity), calculate the mean, standard deviation, maximum value, minimum value and other statistics, which will help understand the overall distribution and fluctuation range of the data. Export the calculated statistics to a new data table or file for subsequent use with spatial analysis techniques.

[0075] Open GIS software (such as ArcGIS, QGIS, etc.), create a new project, import the rock physical property data set and related geospatial data (such as the boundaries of the exploration area, coordinate points, etc.) into the GIS project; ensure that the rock physical property data is correctly associated with the geospatial data so that each physical property value can correspond to a specific location in space; use the interpolation or rasterization function of the GIS software to convert the point-like physical property data into a continuous spatial distribution, and you can choose to use different interpolation methods (such as inverse distance weighting, Kriging interpolation, etc.) to generate spatial distribution according to actual needs; add the previously calculated statistics (mean, standard deviation, etc.) as attribute data to the spatial distribution; set different colors or symbols to represent different statistical values, so as to intuitively display the distribution of physical properties in space; use the spatial analysis tools of GIS software (such as focal statistics, cluster analysis, etc.) to identify abnormal areas that have obvious differences or mutations compared with the surrounding areas. These abnormal areas are manifested as high or low value clusters of physical property values; mark the identified abnormal areas, and use different colors, lines or labels to highlight these areas.

[0076] Analyze the geological structure stress state in the survey area using seismic data and terrain deformation monitoring data, including:

[0077] Open the earthquake data analysis software or Python environment and import the pandas library; read the earthquake dataset file to ensure that the data format is correct and load it into the software or DataFrame; check the key information columns in the dataset, including time, location (latitude and longitude), magnitude, and focal depth, to ensure that the data in these columns are complete and correct.

[0078] Open the GIS software, create a new map project, import the location information (latitude and longitude) in the earthquake data set into the GIS software, use the point annotation function of the GIS software to mark the location of the earthquake event one by one according to the longitude and latitude, and adjust the color or size of the annotation point according to the magnitude. Observe the marked earthquake event points and analyze their density and distribution. According to the degree of aggregation of earthquake events, preliminarily divide the earthquake active area and relatively quiet area; combine regional geological data and historical earthquake records to further verify and refine the division of these areas; use the magnitude information in the earthquake data set to count the number of earthquake events in different magnitude ranges. Analyze the energy release characteristics of earthquake activity, such as b value (slope in Gutenberg-Richter relationship). According to the source depth data, understand the distribution of earthquake events in different crustal layers; combine geological structure data and geophysical exploration data to infer the crustal structure characteristics and possible stress concentration areas.

[0079] Obtain terrain deformation monitoring data such as GPS observation data and InSAR data from relevant institutions or databases; convert and organize the data to make them compatible with subsequent analysis software. Filter the GPS observation data to remove noise caused by atmospheric interference, multipath effects, etc. Perform phase unwrapping and terrain correction on InSAR data to extract surface deformation information, and check and correct possible data outliers or errors.

[0080] The preprocessed GPS and InSAR data are used to calculate the deformation rate and direction of the surface. The long-term deformation trend and periodic change characteristics of the surface are identified through time series analysis methods (such as least squares fitting and Kalman filtering). Combined with geological data and deformation patterns, the causes of deformation are analyzed, such as crustal deformation caused by plate movement, fault activity, and groundwater exploitation.

[0081] Load earthquake event distribution data and terrain deformation data in GIS software at the same time; find the correlation between earthquake events and surface deformation through comparative analysis; identify the overlapping parts of earthquake active areas and significant deformation areas, which may be key areas of stress concentration and crustal instability. Comprehensively consider the distribution, magnitude, focal depth and surface deformation characteristics of earthquake events (such as deformation rate, direction, etc.); combine the regional geological structure background knowledge (such as fault direction, plate boundary, etc.), and preliminarily infer the geological structure stress state in the exploration area. For example, if the earthquake events are dense and large in magnitude, accompanied by obvious surface compression deformation, it may indicate that the area is in a compressive stress state; on the contrary, if the earthquake events are sparse and the surface has tension deformation, it may indicate that the tensile stress state is dominant.

[0082] Through geological stress field simulation, the geological structural stress state of the exploration area is analyzed to obtain the analysis results of the stress state, including:

[0083] Collect geological data of the survey area, including stratigraphic structure, rock type, fault distribution, structural characteristics, etc. Extract information directly related to the analysis of geological structural stress state from the collated data, such as the physical properties of rock formations (elastic modulus, Poisson's ratio, etc.), the strike, dip and inclination of faults, and the distribution and magnitude of historical earthquake activities, etc. Convert the extracted information into a format suitable for boundary element analysis, such as establishing a data file or database.

[0084] Open the boundary element analysis software and create a new project file; set the basic parameters of the project, such as unit system, coordinate system, etc.; import the previously extracted and formatted geological information data into the software to ensure the accuracy and completeness of the data for subsequent modeling.

[0085] According to the stratigraphic distribution information in the geological data, drawing tools (such as point, line, and surface tools) are used in the software to draw the stratigraphic interface layer by layer to ensure that the topological relationship between the interfaces is correct without overlap or omission.

[0086] Assign corresponding physical properties to each stratum, such as density, elastic modulus, Poisson's ratio, etc. These properties will affect the stress distribution and deformation behavior in subsequent simulation calculations. According to the actual stratum thickness data, adjust the thickness of each stratum in the model to ensure that the scale of the model in the vertical direction is consistent with the actual situation.

[0087] According to the strike, dip and inclination information of the fault in the geological data, the position of the fault is determined in the model, and the spatial form of the fault can be represented by the fault plane or fault line. The fault is given corresponding physical properties, such as the friction coefficient and bonding strength of the fault plane, which will affect the sliding and deformation behavior of the fault in the simulation calculation, define the contact relationship of the rock mass on both sides of the fault, such as bonding, sliding, etc., and set the friction conditions on the contact surface and the possible sliding direction.

[0088] According to the morphological characteristics of folds in geological data (such as wavelength, amplitude, etc.), the morphology of folds is determined in the model; corresponding physical properties are assigned to the fold area to reflect its deformation and stress characteristics, and the impact of folds on surrounding strata, such as stress concentration and deformation coordination, is considered.

[0089] When meshing the model, the mesh is refined in key areas (such as near faults and at stratum interfaces) to improve the calculation accuracy and resolution of these areas. The accuracy and reliability of the model are verified by comparing it with actual geological data. After completing the above steps, a three-dimensional geological model containing key geological features such as major strata, faults, and folds can be obtained.

[0090] Grid the established geological model, select the corresponding grid type and size, and generate a grid system for numerical calculation. For the surface, set it as a free boundary; for the deep underground, it can be set as a fixed boundary or impose certain constraints according to the actual situation. For faults, set the corresponding contact conditions and friction coefficients according to their activity; for the contact surface of the rock layer, consider its bonding strength, friction angle and other parameters. According to the analyzed geological structural stress state, assign an initial stress value to each grid point in the model, which can be achieved by importing predefined stress field data or setting it directly in the software. So that the initial stress state is consistent with the actual geological conditions in the exploration area to reflect the long-term stress accumulation of the crust.

[0091] In the boundary element analysis software, according to the analysis requirements and computing resources, set the solution parameters such as calculation accuracy, number of iterations, and convergence criteria. Start the simulation calculation process and monitor the convergence and error changes in the calculation process in real time. Ensure that the calculation results meet the preset accuracy requirements. After the calculation is completed, save the calculation results such as stress values ​​and displacement values ​​of each grid point. According to the calculation results, identify high stress areas and low stress areas. High stress areas usually correspond to areas with strong geological tectonic activities and possible earthquakes; while low stress areas may indicate a relatively stable geological environment. Combined with geological data and simulation results, comprehensively analyze the geological tectonic stress state of the exploration area and its impact on crustal stability and engineering safety.

[0092] According to the analysis results of the stress state, the corresponding stress release area is identified, including:

[0093] Import the simulation results of the geological stress field calculated by the boundary element analysis software, observe and analyze the stress distribution data, and pay attention to areas with abnormally high or low stress values; identify stress concentration areas, which usually show significantly higher stress values ​​than the surrounding areas, which may be a sign of active crustal movement or tectonic locking; similarly, identify stress release areas, which have relatively low stress values, which may indicate recent stress release events, such as earthquakes. Quantitatively analyze the simulation results and set corresponding stress thresholds to define stress concentration and stress release areas. The thresholds can be determined based on historical data, empirical values, or statistical analysis methods.

[0094] Collect and analyze seismic data in the survey area, including information such as the time, location, and magnitude of the earthquake. Compare the seismic data with the results of the geological stress field simulation to find the correlation between seismic events and stress release areas. Pay attention to seismic events that occur in or near the stress release area, which may verify the stress release state of the area. Use satellite remote sensing, radar interferometry (InSAR) and other technologies to obtain topographic deformation data in the survey area. Analyze these data to identify areas with obvious surface deformation. These deformations may be related to changes in the stress state deep in the crust. Combine the topographic deformation data with the results of the geological stress field simulation to further verify and determine the location and scope of the stress release area. Mark the identified stress release area. The mark may include the area boundary, name, and related data (such as stress value range, seismic event information, etc.); analyze the potential impact of the stress release area on mineral resource exploration. For example, stress release may lead to ore body destruction, ore fluid migration, etc., thereby affecting the distribution and quality of mineral resources. Assess the risk of geological disasters (such as earthquakes, landslides, mudslides, etc.) in the stress release area. The disaster risk level of each region is comprehensively judged based on factors such as stress status, terrain conditions, and historical disaster records.

[0095] The results of rock physical property analysis and geological structure stress state analysis are analyzed to obtain the correlation between rock physical properties and geological structure stress state, including:

[0096] The rock physical property analysis results (such as density, magnetism, conductivity, etc.) and geological structure stress state analysis results (such as stress distribution, stress concentration and release areas, etc.) obtained in the previous steps are sorted out to make the two types of data consistent in space and scale, which is convenient for subsequent correlation analysis.

[0097] Using the Geographic Information System (GIS), a database containing rock physical properties and geological structure stress state data is established, and each data point in the database is assigned corresponding attributes, including location coordinates, rock physical property parameters, stress state parameters, etc.

[0098] Select appropriate statistical analysis methods, such as the Pearson correlation coefficient, to analyze the correlation between rock physical properties and geological structural stress states. Use the Pearson correlation coefficient to calculate the Pearson correlation coefficient between rock physical properties (such as density, magnetism, conductivity) and geological structural stress states (such as stress value, stress change rate, etc.); analyze the value of the Pearson correlation coefficient to determine the strength and direction of the correlation. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and a larger absolute value indicates a stronger correlation. Based on the calculation results of the correlation coefficient, screen out rock physical properties that are closely related to changes in geological structural stress states, and you can set a correlation threshold, such as (r is the Pearson correlation coefficient) to determine the significantly correlated properties. For the significantly correlated rock physical properties screened out, the possible causes and physical mechanisms between them and the changes in the stress state of geological structures are further analyzed. For example, changes in the stress state may lead to adjustments in the internal structure of the rock, thereby affecting its physical properties (such as density changes, magnetic enhancement, etc.). According to the results of the correlation analysis, combined with the known distribution laws of mineral resources, the types of mineral resources and enrichment areas that may exist in the exploration area are predicted. For example, certain specific combinations of rock physical properties may indicate a specific mineralization environment or mineralization, thereby guiding the exploration of mineral resources. Using the results of the correlation analysis, the risk of geological disasters in the exploration area is assessed, with special attention paid to those areas with abnormal rock physical properties that are closely related to changes in stress states. Combined with factors such as topography, meteorology and hydrology, the risk level of geological disasters in each region is comprehensively judged, and corresponding disaster prevention and mitigation suggestions are put forward. For example, in areas where stress is concentrated and rock physical properties are obviously abnormal, earthquake monitoring and early warning work may need to be strengthened.

[0099] In an embodiment of the present invention, a data set related to rock physical properties and geological structural stress state is screened out from the preprocessed data, including rock density, magnetism and conductivity, as well as seismic data and terrain deformation data. By accurately screening the relevant data, the accuracy and pertinence of subsequent analysis can be ensured, unnecessary data interference can be avoided, and the analysis efficiency can be improved. Through the statistical analysis of rock physical properties, the physical characteristics of rocks in the exploration area can be more clearly understood, and the identification of abnormal areas can help to discover potential mineral resources or geological risks. Seismic data and terrain deformation data are important indicators reflecting the geological structural stress state. Through the analysis of these data, the distribution of geological stress in the exploration area can be preliminarily understood. Geological stress field simulation can reveal the geological stress state of the exploration area more deeply, and help to discover potential geological structures such as faults and folds. Stress release areas are usually areas where mineral resources are rich or geological disasters are prone to occur. Accurately identifying these areas is of great significance for the development of mineral resources and the prevention of geological disasters. By analyzing the correlation between rock physical properties and geological structural stress states, the geological characteristics of the exploration area can be more deeply understood.

[0100] In a preferred embodiment of the present invention, the dynamic adjustment coefficient is calculated based on the correlation between rock physical properties and geological structural stress state, including:

[0101] Based on the correlation between rock physical properties and geological structural stress state, Calculate dynamic adjustment coefficient;

[0102] in, ; ; ; The exploration area The measured density of the points; is the base density; The exploration area The measured conductivity of the point; is the average conductivity of the survey area; The exploration area Measurement of geological stress at points; is the average stress value in the survey area; , and is the weight coefficient; is an index of the amount of change in rock density; is the total amount of rock density variation; is an index of rock conductivity; is the total of the rock conductivity; It is an index of the amount of change in geological stress; is the total amount of geological stress change.

[0103] In an embodiment of the present invention, by integrating data of multiple rock physical properties (such as density, conductivity) and geological structural stress states, the dynamic adjustment coefficient can more comprehensively reflect the geological characteristics of the exploration area. This comprehensive analysis method can reduce the uncertainty in the prediction of a single parameter, thereby improving the prediction accuracy of mineral resource distribution and geological disaster risks. In the process of mineral resource exploration, the allocation of resources is crucial. By calculating the dynamic adjustment coefficient, potential rich ore areas and high-risk areas for geological disasters can be identified, thereby optimizing the allocation of exploration resources. This helps to concentrate limited resources in areas where results are most likely to be achieved, thereby improving exploration efficiency and economic benefits. The application of the dynamic adjustment coefficient helps to timely discover and assess geological disaster risks.

[0104] In a preferred embodiment of the present invention, estimating the mineral resource reserves according to the dynamic adjustment coefficient includes:

[0105] Arrange exploration sections in the exploration area so that the exploration sections reflect the shape and distribution characteristics of the ore body, including:

[0106] Conduct preliminary geological surveys in the exploration area to understand the geological structure, rock layer distribution, mineralization information, etc. of the area. According to the geological survey results, select locations that can reflect the morphology and distribution characteristics of the ore body as the location of the exploration section. These locations are located in areas with obvious signs of mineralization or important geological structures. Determine the size, shape and direction of each exploration section to ensure that it can effectively reveal the morphology of the ore body. Mark the specific location of the exploration section on the spot and record its geographical coordinates, specifications and dimensions in detail.

[0107] According to the morphology and distribution characteristics of the ore body, each exploration section is measured to obtain measurement data. The measurement data includes the area of ​​the section, the thickness of the ore body and the grade information, including:

[0108] Each exploration section is measured, and the area of ​​the section, the thickness of the ore body, and the grade information are recorded. The area can be calculated by measuring the length and width of the section, the thickness of the ore body can be determined by drilling or geological radar, and the grade information is obtained by sampling and testing; the measured data is sorted to ensure the accuracy and completeness of the data; based on the measured data and the distribution of the ore body in each section, the area of ​​the ore body on the section is calculated.

[0109] Calculate the block reserves between two adjacent sections by using the ore body area and the distance between the sections; Calculate the mineral resource reserves based on the dynamic adjustment coefficient and the block reserves, including: Calculate the mineral resource reserves; among which, Indicates the total reserves of mineral resources; Indicates The ore body area of ​​each block; Indicates The thickness of the ore body of each block; Indicates The ore density of each block; Indicates The grade of the ore block; Indicates the number of the ore block; Indicates the total number of blocks, including:

[0110] Through the previous measurement steps, the ore body area of ​​the ore blocks on the two adjacent sections has been obtained. These area data are the basis for calculating the reserves of the ore block; estimating the thickness of the ore body, the thickness of the ore body It refers to the average thickness of the ore body between two adjacent sections, which is determined by geological exploration methods (such as drilling); with the area and thickness of the ore body, the volume of each block can be calculated, which is achieved by multiplying the area by the thickness. In order to convert from volume to reserves (i.e. mass), it is necessary to know the density of the ore and taste Density indicates the mass of ore per unit volume, while grade indicates the percentage of useful components in the ore. Apply dynamic adjustment factor, dynamic adjustment factor It is a correction factor that takes into account a variety of geological factors and is used to adjust the preliminary calculated reserves of mineral blocks in order to improve the accuracy and reliability of reserve estimates.

[0111] In the embodiment of the present invention, by combining the dynamic adjustment coefficient and detailed exploration and measurement data, the method can more accurately estimate the reserves of mineral resources and reduce the uncertainty and error in traditional estimation methods. Accurate reserve estimation helps to formulate more reasonable resource development and utilization strategies. By arranging exploration sections in a targeted manner and using efficient measurement methods, the exploration cost can be reduced while ensuring accuracy and improving the efficiency of exploration work.

[0112] In a preferred embodiment of the present invention, the mineral resource reserves are evaluated to obtain the final evaluation results, which specifically includes:

[0113] According to geological exploration data and mineral deposit distribution maps, the mining area is divided into several representative areas. In each area, sampling points of different depths are determined to ensure that each depth layer is covered by samples; drilling or pit exploration methods are used to take samples at the determined sampling points to ensure that the collected samples are representative and avoid contamination or confusion. Each sample is uniquely identified, and the sampling point location, depth, date and other information are recorded to ensure that the sample remains in its original state during transportation and storage.

[0114] The samples are pre-processed by crushing and grinding according to the laboratory requirements, and chemical analysis is performed, such as X-ray fluorescence spectrometry (XRF) and atomic absorption spectrometry (AAS), to determine the element content in the ore, and physical tests are performed, such as density measurement and magnetic measurement, to understand the physical properties of the ore. The test results of each sample are recorded in detail, including data such as element content and physical properties.

[0115] Calculate the average grade of each element and the range of grade variation between different samples, analyze the content and distribution of harmful components (such as sulfur, phosphorus, etc.), compare with industry standards or data of similar deposits, and evaluate the quality grade of mineral resources.

[0116] Based on the ore grade and harmful component content, preliminarily judge the availability of resources; high-grade, low-harmful component ores usually have higher utilization value; collect geographical location information of the mining area, including longitude and latitude, altitude, etc., and analyze the climate type, rainfall, temperature and other climatic conditions of the mining area; examine the traffic conditions around the mining area, including the convenience of transportation facilities such as roads, railways, and waterways.

[0117] Gather all relevant data obtained during geological exploration, including but not limited to drilling data, core logging, geophysical and geochemical data, etc. Use geological modeling software (such as Surpac, Micromine, etc.) to build a three-dimensional geological model of the mining area based on the exploration data, and clearly mark the boundaries between the ore body and the surrounding rock, as well as the distribution of different ore bodies or mineralized zones in the model. Through the three-dimensional model, observe the overall shape of the ore deposit, identify the occurrence, extension direction and scale of the ore body, and analyze the occurrence state of the ore body, including the continuity, branching, thickness change, etc. of the ore body. Combine the geological background and mineralization laws to explain the geological significance of the ore deposit shape and occurrence state. Conduct on-site surveys of the mining area, observe and record the distribution, flow direction and water volume of surface water bodies. Find and mark possible groundwater outcrops, such as springs, wetlands, etc.; conduct hydrogeological drilling at key locations, install observation holes or pumping test holes, conduct pumping tests, measure groundwater inflow, water level changes and other parameters, collect groundwater samples, conduct water quality analysis, understand the chemical composition and pollution of water, organize data obtained from field surveys and drilling tests, and establish a hydrogeological database. Collect market price information on relevant mineral products, including historical prices and real-time prices; analyze market demand trends for mineral products, predict future demand changes, and evaluate the supply and demand of mineral products based on market prices and demand trends.

[0118] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent mineral resource exploration and evaluation system, characterized in that: The system comprises: Data collection module, used to collect data related to geological and mineral exploration from various data sources; A data preprocessing module, used for preprocessing data to obtain preprocessed data; The stress analysis module is used to analyze the rock physical properties and geological structural stress state of the exploration area based on the preprocessed data to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated; Resource reserve estimation module, used to estimate mineral resource reserves based on dynamic adjustment coefficients; Resource assessment module, used to assess mineral resource reserves to obtain the final assessment results; Based on the correlation between rock physical properties and geological structural stress state, the dynamic adjustment coefficient is calculated, including: Based on the correlation between rock physical properties and geological structural stress state, Calculate dynamic adjustment coefficient; in, ; is the measurement density of the i-th point in the survey area; is the base density; is the measured conductivity of the jth point in the survey area; is the average conductivity of the survey area; is the measured geological stress at the kth point in the survey area; is the average stress value in the survey area; is the weight coefficient; i is the index of rock density variation; n is the total number of rock density variation; j is the index of rock conductivity; m is the total number of rock conductivity; k is the index of geological stress variation; is the total amount of geological stress variation; Mineral resource reserves are calculated based on the dynamic adjustment coefficient and the block reserves, including: According to the dynamic adjustment coefficient and the reserves of the ore block, Calculate mineral resource reserves; Among them, R represents the total reserves of mineral resources; represents the ore body area of ​​the bth ore block; represents the thickness of the ore body of the bth ore block; Indicates the ore density of the b-th ore block; represents the grade of the bth ore block; b represents the number of the ore block; h represents the total number of ore blocks.

2. The intelligent mineral resource exploration and evaluation system according to claim 1 is characterized in that: Based on the pre-processed data, the rock physical properties and geological structural stress state of the exploration area are analyzed to obtain analysis results, including: Screening out data sets related to rock physical properties and geological structural stress states from preprocessed data; Based on the data sets related to rock physical properties, calculate the statistics of various rock physical properties, and identify the areas with abnormal changes in physical properties by comparing the rock physical property data of different regions; Analyze the geological structural stress state in the survey area using seismic data and terrain deformation monitoring data; Through geological stress field simulation, the geological structural stress state of the exploration area is analyzed to obtain the analysis results of the stress state; According to the analysis results of the stress state, the corresponding stress release area is identified; The results of the rock physical property analysis and the geological structure stress state analysis are analyzed to obtain the correlation between the rock physical properties and the geological structure stress state.

3. The intelligent mineral resource exploration and evaluation system according to claim 2 is characterized in that: Estimation of mineral resource reserves based on dynamic adjustment coefficients, including: Arrange exploration sections within the exploration area so that the exploration sections reflect the morphology and distribution characteristics of the ore body; According to the morphology and distribution characteristics of the ore body, each exploration section is measured to obtain measurement data, which includes the area of ​​the section, the thickness of the ore body and the grade information; Based on the measurement data and the distribution of ore bodies in each section, the area of ​​the ore body on the section is calculated; The ore block reserves between two adjacent sections are calculated by using the ore body area on two adjacent sections and the distance between the sections; Mineral resource reserves are calculated based on the dynamic adjustment coefficient and the block reserves.

4. The intelligent mineral resource exploration and evaluation system according to claim 1 is characterized in that: Data related to geological and mineral exploration include topographic data, drilling data, geophysical data, geochemical data and remote sensing image data.

5. The intelligent mineral resource exploration and evaluation system according to claim 2 is characterized in that: Data sets related to rock physical properties and geological structural stress states include rock density, magnetism, and electrical conductivity, as well as seismic data and topographic deformation data.

6. An intelligent mineral resource exploration and evaluation method, characterized in that: Applicable to the system according to any one of claims 1 to 5, comprising: Collect data related to geological and mineral exploration from various data sources; Preprocessing the data to obtain preprocessed data; According to the pre-processed data, the rock physical properties and geological structural stress state of the exploration area are analyzed to obtain the correlation between the rock physical properties and the geological structural stress state; according to the correlation between the rock physical properties and the geological structural stress state, the dynamic adjustment coefficient is calculated; Estimate mineral resource reserves based on dynamic adjustment coefficients; Evaluate the mineral resource reserves to obtain the final evaluation results.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to claim 6 when executed by a processor.

Images