Data extraction method and system for digital geological mineral exploration

Abstract

The invention discloses a data extraction method and system for digital geological mineral exploration, and relates to the technical field of geological mineral exploration, and the method comprises the steps: collecting the original data of a geological mineral exploration site based on a spatial attitude sensing geological detection device; based on the spatial dimension, the angle dimension and the time dimension, preprocessing the collected original data of the geological mineral exploration site to obtain optimized data of the geological mineral exploration site; key geological information is extracted from the optimization data of the geological mineral exploration site to generate geological mineral three-dimensional space features; determining a mineral product space distribution model of the exploration area according to the geological mineral product three-dimensional space characteristics; the spatial information of the geologic structure comprises three-dimensional arrangement information of a stratum, three-dimensional arrangement information of a fracture surface and three-dimensional arrangement information of an ore body.

Description

Technical Field

[0001] This application relates to the technical field of geological and mineral exploration, and in particular, to a data extraction method and system for digital geological and mineral exploration. Background Art

[0002] Geological and mineral exploration refers to the process of investigating ore deposit geology and mineral resources based on regional geological surveys, according to the needs of national economic and social development, applying geological science theories, and using a variety of exploration techniques and methods. Geological and mineral exploration can reveal information such as underground water resources, soil quality, and vegetation distribution, which is of great significance for environmental protection and ecological restoration. At the same time, it is decisive for the reserve assessment and mining planning of ore deposits. By analyzing geological data, the resource volume and grade of ore deposits can be estimated, providing a basis for development plans and economic evaluations.

[0003] Geological and mineral exploration plays an indispensable and profound role in the national resource strategic planning, economic sustainable development, and the prosperity of the mining industry. It helps to reduce the environmental damage caused by mineral development and provides key support for sustainable development and utilization. Geological and mineral exploration can provide impetus for economic development, especially in the process of industrialization, where the demand for mineral resources continues to grow, and geological and mineral exploration becomes the key to meeting these demands.

[0004] In related technologies, the processing and analysis of geological and mineral exploration data mainly rely on the method of manual data analysis. This method has the following defects: Manual data analysis requires a large amount of time and human resources, which limits the speed and efficiency of data processing and is difficult to meet the needs of large-scale and rapid exploration; With the continuous increase in the amount of geological and mineral exploration data, the manual analysis method is difficult to handle the processing requirements of large-scale data. Especially when the data volume is huge, the limitations of manual analysis are more obvious.

[0005] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0006] The embodiments of this application provide a data extraction method and system for digital geological and mineral exploration to solve the above technical problems.

[0007] The present application provides a method for extracting data in digital geological and mineral exploration, including: collecting original data of the geological and mineral exploration site based on a geological detection device with spatial attitude perception; wherein, the original data of the geological and mineral exploration site carries corresponding geographic positioning information, detection angle information and time series information; preprocessing the collected original data of the geological and mineral exploration site based on the spatial dimension, angle dimension and time dimension to obtain optimized data of the geological and mineral exploration site; extracting key geological information from the optimized data of the geological and mineral exploration site to generate three-dimensional spatial characteristics of geological and minerals; and determining a mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of geological and minerals; wherein, the key geological information includes spatial information of geological structures and rock mechanics information of geological bodies; the spatial information of the geological structures includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fault planes and three-dimensional arrangement information of ore bodies.

[0008] The present application provides a data extraction system for digital geological and mineral exploration, including: a data collection module for collecting original data of the geological and mineral exploration site based on a geological detection device with spatial attitude perception; wherein, the original data of the geological and mineral exploration site carries corresponding geographic positioning information, detection angle information and time series information; a data preprocessing module for preprocessing the collected original data of the geological and mineral exploration site based on the spatial dimension, angle dimension and time dimension to obtain optimized data of the geological and mineral exploration site; a data extraction module for extracting key geological information from the optimized data of the geological and mineral exploration site to generate three-dimensional spatial characteristics of geological and minerals; and determining a mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of geological and minerals; wherein, the key geological information includes spatial information of geological structures and rock mechanics information of geological bodies; the spatial information of the geological structures includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fault planes and three-dimensional arrangement information of ore bodies.

[0009] Based on the embodiments provided in the present application, the following has been achieved: directly collecting data at the exploration site using a spatial attitude perception device, reducing the possible errors during the manual transfer of data, and improving the original accuracy of the data; through the generation of three-dimensional spatial features, realizing multi-dimensional analysis of the data, making the data processing process more scientific and systematic, and enhancing the overall quality of data processing; by extracting and analyzing key geological information, including the spatial information of geological structures and rock mechanics information, the spatial distribution of minerals can be determined more accurately; automated data collection and preprocessing reduce the dependence on manual operations and lower the labor cost, especially in large-scale exploration projects, where this cost savings is particularly obvious; optimizing data processing enables the exploration work to respond quickly, especially in emergency or time-sensitive exploration tasks, and can quickly provide exploration results; by establishing a three-dimensional spatial feature and a mineral spatial distribution model, it provides a more intuitive and scientific basis for mining decision-making, enhancing the accuracy and effectiveness of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and the illustrative embodiments and descriptions thereof are used to explain this application and do not constitute an improper limitation of this application. In the drawings: Figure 1 is a flowchart of an optional data extraction method for digital geological and mineral exploration according to an embodiment of the present application; Figure 2 is a structural diagram of an optional data extraction system for digital geological and mineral exploration according to an embodiment of the present application.

[0011] The realization of the objectives of the present invention, its functional characteristics, and advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] Optionally, as Figure 1 shown, the present application provides a data extraction method for digital geological and mineral exploration, including: S101, collecting the original data of the geological and mineral exploration site based on a spatial attitude perception geological exploration device; wherein, the original data of the geological and mineral exploration site carries corresponding geographic location information, detection angle information, and time series information; S102, preprocess the original data collected at the geological and mineral exploration site based on the spatial dimension, angular dimension, and time dimension to obtain the optimized data of the geological and mineral exploration site; S103, extract key geological information from the optimized data of the geological and mineral exploration site to generate the three-dimensional spatial characteristics of geology and minerals; and determine the mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of geology and minerals; Among them, the key geological information includes the spatial information of the geological structure and the rock mechanics information of the geological body; the spatial information of the geological structure includes the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault plane, and the three-dimensional arrangement information of the ore body.

[0014] Based on the embodiments provided in the present application, the following are achieved: directly collecting data at the exploration site using a spatial attitude sensing device, reducing the possible errors during the manual transfer of data, and improving the original accuracy of the data; through the generation of three-dimensional spatial characteristics, realizing multi-dimensional analysis of the data, making the data processing process more scientific and systematic, and improving the overall quality of data processing; through the extraction and analysis of key geological information, including the spatial information of the geological structure and the rock mechanics information, the spatial distribution of minerals can be determined more accurately; automated data collection and preprocessing reduce the dependence on manual operations and lower the labor cost. Especially in large-scale exploration projects, this cost saving is particularly obvious; the processing of optimized data enables the exploration work to respond quickly, especially in emergency or time-sensitive exploration tasks, and can quickly provide exploration results; through the establishment of three-dimensional spatial characteristics and the mineral spatial distribution model, it provides a more intuitive and scientific basis for mining decision-making, enhancing the accuracy and effectiveness of decision-making.

[0015] Optionally, as Figure 2 shown, the present application provides a data extraction system for digital geological and mineral exploration, including: A data collection module 201, configured to collect the original data of the geological and mineral exploration site based on a spatial attitude sensing geological exploration device; among them, the original data of the geological and mineral exploration site carries corresponding geographic location information, detection angle information, and time series information; A data preprocessing module 202, configured to preprocess the original data collected at the geological and mineral exploration site based on the spatial dimension, angular dimension, and time dimension to obtain the optimized data of the geological and mineral exploration site; A data extraction module 203, configured to extract key geological information from the optimized data of the geological and mineral exploration site to generate the three-dimensional spatial characteristics of geology and minerals; and determine the mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of geology and minerals; Among them, the key geological information includes the spatial information of the geological structure and the rock mechanics information of the geological body; the spatial information of the geological structure includes the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies.

[0016] Furthermore, the data acquisition module collects the original data of the geological and mineral exploration site based on the spatially aware geological exploration equipment, including: Collect historical geological and mineral exploration data; among them, the historical geological and mineral exploration data includes geological structure information, mineral distribution information, and topographic and geomorphic information; Using a geographic information system, generate a base map of the exploration area in combination with the historical geological and mineral exploration data; among them, the base map contains geological structure information, ore deposit location information, and topographic elevation information; Analyze the labels of the historical geological and mineral exploration data, and extract the key parameters affecting mineral exploration; among them, the labels include success labels and failure labels; the key parameters include rock type, mineralization intensity, and geological age; Mark the known geological feature points on the base map, and enhance the base map through remote sensing technology; among them, the known geological feature points include known faults, known folds, and known mineralized zones; Based on the base map and the historical geological and mineral exploration data, evaluate the exploration risks and potential values of each sub-exploration area included in the exploration area; According to the evaluation results of the exploration risks and potential values of each sub-exploration area included in the exploration area, allocate sub-exploration tasks to each sub-exploration area, and plan the travel routes of the spatially aware geological exploration equipment for each sub-exploration task. The sub-exploration tasks include drilling and sampling geophysical exploration.

[0017] Furthermore, the data preprocessing module preprocesses the original data of the geological and mineral exploration site collected based on the spatial dimension, angular dimension, and time dimension to obtain the optimized data of the geological and mineral exploration site, including: According to the spatial data in the original data of the geological and mineral exploration site, use the Kriging interpolation algorithm to process the geological data in the original data of the geological and mineral exploration site to generate a continuous spatial prediction model; Among them, the Kriging interpolation algorithm is used to predict the geological parameter values at unknown locations based on the spatial autocorrelation of the known sample point locations; the continuous spatial prediction model is used to determine an estimated value of a geological parameter for each geographical location in the exploration area; ; Among them, is the estimated value of the geological parameter at the unknown geographical location ; is the actual value of the geological parameter at the known sample point location ; is the weight coefficient corresponding to the sample point location ; is calculated based on spatial autocorrelation and the spatial variability of geological parameters; is a region-specific adjustment factor used to adjust the estimated value of the geological parameter to adapt to local geological changes; determined based on geological principles; According to the detection angle information carried by the original data of the geological and mineral exploration site, the beamforming algorithm is used to process the geological data in the original data of the geological and mineral exploration site to obtain angle-enhanced geological data; among them, the beamforming algorithm enhances the signal from a specific angle by forming a beam pointing in a specific direction; Based on the angle-enhanced geological data, the continuous spatial prediction model is optimized to enhance the spatial signal of specific geological parameters; for example, if the signal at a certain angle is strong, we can weight the estimated value of the geological parameter at that angle in the continuous spatial prediction model to reflect the signal strength; the continuous spatial prediction model is optimized according to the processing result of the detection angle information. This means integrating the angle information into the spatial model to improve the accuracy of geological parameter estimation, especially in areas with strong signal source directionality; According to the time series information carried by the original data of the geological and mineral exploration site, the SARIMA model is used to process the geological data in the original data of the geological and mineral exploration site to obtain time series-identified geological data; among them, the SARIMA model is used to identify and predict seasonality and trends in time series data; the geological data can be the measured values of geological parameters at the same geographical location at different time points; Based on the optimized continuous spatial prediction model, angle-enhanced geological data, and time series-identified geological data, the optimized data of the geological and mineral exploration site is determined.

[0018] Furthermore, the data extraction module extracts key geological information from the optimized data of the geological and mineral exploration site to generate three-dimensional spatial characteristics of the geological and mineral resources, including: Based on the optimized data of the geological and mineral exploration site, a physical-constrained neural network is used to analyze the relationship between rock mechanics parameters and the distribution of geological and mineral resources to extract rock mechanics information of geological bodies; Among them, the rock mechanics parameters include strength parameters, deformation parameters, failure parameters, porosity parameters, and wave velocity parameters; the physical-constrained neural network includes a physical constraint layer; the relationships between the strength parameters, deformation parameters, failure parameters, porosity parameters, and wave velocity parameters are embedded in the physical constraint layer in the form of differential equations; The strength parameters include uniaxial compressive strength, tensile strength, and shear strength; the deformation parameters include elastic modulus and shear modulus; the failure parameters include internal friction angle and cohesion; the porosity parameters include porosity and permeability; the wave velocity parameters include longitudinal wave velocity and transverse wave velocity.

[0019] The differential equation can include: ; Among them, , respectively represent the th, th, th components of the spatial coordinates, which are the coordinate axes in three-dimensional space and can correspond to the x, y, and z axes; is the stress tensor, representing the internal forces of the rock in different directions; represents time; is the strain tensor, representing the deformation of the rock; is the energy storage function of the rock, related to the strength parameters and deformation parameters of the rock; is the pore pressure, related to the porosity parameters; is the density of the rock, related to the wave velocity parameters; is the velocity vector, representing the moving speed of the rock; is the coupling coefficient, describing the influence of the failure parameters of the rock on the stress-strain relationship; Construct a three-dimensional geological model based on the optimized data of the geological and mineral exploration site; Extract the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies from the constructed three-dimensional geological model.

[0020] Furthermore, the physical constraint neural network includes an input layer, a hidden layer, and an output layer; among them, the input layer is used to receive the optimized data of the geological and mineral exploration site; the hidden layer includes a geological feature extraction layer, a spatio-temporal feature fusion layer, and a detection angle processing layer; the output layer is used to output the relationship between the predicted rock mechanics parameters and the geological and mineral distribution; Among them, the geological feature extraction layer is an adaptive pooling layer, and the adaptive pooling layer dynamically adjusts the pooling window size according to the spatial distribution characteristics of the optimized data of the geological and mineral exploration site to extract key geological features; The spatio-temporal feature fusion layer combines long short-term memory networks and gated recurrent units to process the time series characteristics of the optimized data of the geological and mineral exploration site and fuse spatial information to capture the changing trends of key geological features over time and space; The detection angle processing layer uses a rotation-invariant network structure to capture the feature changes of key geological features at different detection angles; the rotation-invariant network structure, such as Rotation Equivariant Convolutional Networks; this network structure can maintain the consistency of features at different angles, thus more accurately extracting geological information related to the detection angle; Among them, the composite loss function adopted by the physics-constrained neural network consists of the following three parts: data fitting error, penalty term for physical constraint violation, and angle information consistency loss; the data fitting error includes the difference between the predicted value and the actual value measured using the mean square error; the penalty term for physical constraint violation includes the sum of squares based on the physical equation residuals, which penalizes the output of the physics-constrained neural network for violating physical laws; the angle information consistency loss is used to maintain the consistency of the physics-constrained neural network when processing different detection angles.

[0021] Furthermore, the detection angle processing layer performs feature extraction based on the following formula: ; where is the feature extraction function at geographical location and detection angle , which is used to represent the feature intensity of key geological features at geographical location and detection angle ; is the number of detection angles, representing the total number of different angles; is the index variable of the detection angle; is the weight coefficient of the th detection angle, which is used to represent the importance of features at this angle; is the position of the key geological feature at the th detection angle; is the Euclidean distance between geographical location and the position of the key geological feature ; is the standard deviation parameter, which is used to control the locality of feature extraction. The locality of feature extraction is the influence range of feature points on the surrounding area; is the Dirac function, which is used to ensure that feature extraction is performed at angle ; when is equal to , the function value of the Dirac function is 1, otherwise it is 0.

[0022] Further, a three-dimensional geological model is constructed based on the optimized data of the geological and mineral exploration site; the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies are extracted from the constructed three-dimensional geological model, including: The optimized data of the geological and mineral exploration site is mapped to a three-dimensional space through coordinate transformation by using GIS software; the GIS software can be ArcMap; Based on three-dimensional geological modeling software, the three-dimensional geological model integrating geological structure and attributes is initialized; among them, the three-dimensional geological model includes strata, rock masses, faults, low-resistance anomalies, and ore bodies; Based on the optimized data of the geological and mineral exploration site, combined with three-dimensional spatial analysis algorithms, the information of characteristic points of geological profiles and the information of characteristic points of ore bodies are extracted; The information of characteristic points of geological profiles and the information of characteristic points of ore bodies are fitted to construct fault planes and ore bodies; The strata are modeled, and the topological relationship between the strata and the fault planes is adjusted; Based on the ore-controlling factor indexes, the topological relationship between the ore bodies and the fault planes is adjusted to complete the construction of the three-dimensional geological model; among them, the ore-controlling factor indexes include the distance field factor of the fault plane, the trend undulation factor of the fault plane, and the slope factor of the fault plane; According to the constructed three-dimensional geological model, the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies are extracted; among them, the three-dimensional arrangement information of the strata includes the continuity, angle, dip angle, and thickness change of the strata; the three-dimensional arrangement information of the fault planes includes the extension length, dip angle, and dislocation amount of the fault planes; the three-dimensional arrangement information of the ore bodies includes the shape, size, extension direction, and grade distribution of the ore bodies.

[0023] Further, the original data of the geological and mineral exploration site includes multiple types of the following data: geological data, geophysical data, geochemical data, remote sensing data, mineral data, geophysical and geochemical exploration data, sampling data, borehole data, map data, and geological logging data for the exploration of special ore types; Among them, the remote sensing data, geophysical and geochemical exploration data, borehole data, and map data contain the spatial data in the original data of the geological and mineral exploration site.

[0024] Further, the data extraction system for digital geological and mineral exploration further includes: An exploration site data visualization module, which is used to convert the original data and optimized data of the geological and mineral exploration site into graphics, charts, or three-dimensional models by using a graphics rendering algorithm; receive the rotation operation, zoom operation, and slicing operation of the user on the graphics, charts, or three-dimensional models, and change the display angle of the graphics, charts, or three-dimensional models; Among them, the data visualization module integrates a dynamic timeline, which is used to assist in displaying the original data at the geological and mineral exploration site and the trend of optimized data changing over time.

[0025] It should be noted that in this application, the embodiments implemented on the data extraction system side of the digital geological and mineral exploration can be referred to each other with the embodiments implemented on the data extraction method side of the digital geological and mineral exploration, and this application will not elaborate on them one by one.

[0026] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for extracting data in digital geological and mineral exploration, characterized in that, Including: Collecting the original data of the geological and mineral exploration site by a geological exploration device based on spatial attitude perception; wherein, the original data of the geological and mineral exploration site carries corresponding geographic location information, detection angle information and time series information; Preprocessing the collected original data of the geological and mineral exploration site based on the spatial dimension, angle dimension and time dimension to obtain the optimized data of the geological and mineral exploration site; Extracting key geological information from the optimized data of the geological and mineral exploration site to generate the three-dimensional spatial characteristics of the geology and minerals; and determining the mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of the geology and minerals; Wherein, the key geological information includes the spatial information of the geological structure and the rock mechanics information of the geological body; the spatial information of the geological structure includes the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault surface and the three-dimensional arrangement information of the ore body.

2. A data extraction system for digital geological and mineral exploration, the system implementing the method as described in claim 1, characterized in that, Including: A data collection module for collecting the original data of the geological and mineral exploration site by a geological exploration device based on spatial attitude perception; wherein, the original data of the geological and mineral exploration site carries corresponding geographic location information, detection angle information and time series information; A data preprocessing module for preprocessing the collected original data of the geological and mineral exploration site based on the spatial dimension, angle dimension and time dimension to obtain the optimized data of the geological and mineral exploration site; A data extraction module for extracting key geological information from the optimized data of the geological and mineral exploration site to generate the three-dimensional spatial characteristics of the geology and minerals; and determining the mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of the geology and minerals; Wherein, the key geological information includes the spatial information of the geological structure and the rock mechanics information of the geological body; the spatial information of the geological structure includes the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault surface and the three-dimensional arrangement information of the ore body.

3. The data extraction system for digital geological and mineral exploration according to claim 2, characterized in that, The data collection module collects the original data of the geological and mineral exploration site by a geological exploration device based on spatial attitude perception, including: Collecting historical geological and mineral exploration data; wherein, the historical geological and mineral exploration data includes geological structure information, mineral distribution information and topographic and geomorphic information; Using a geographic information system to generate a basic map of the exploration area in combination with the historical geological and mineral exploration data; wherein, the basic map contains geological structure information, deposit location information and topographic elevation information; Analyzing the labels of the historical geological and mineral exploration data and extracting key parameters affecting mineral exploration; wherein, the labels include success labels and failure labels; the key parameters include rock type, mineralization intensity and geological age; Marking known geological feature points on the basic map and enhancing the basic map by remote sensing technology; wherein, the known geological feature points include known faults, known folds and known mineralized zones; Evaluating the exploration risks and potential values of each sub-exploration area included in the exploration area based on the basic map and the historical geological and mineral exploration data; Based on the evaluation results of the exploration risks and potential values of each sub-exploration area included in the exploration area, sub-exploration tasks are assigned to each sub-exploration area, and the travel routes of the space attitude perception geological exploration equipment are planned for each sub-exploration task.

4. The data extraction system for digital geological and mineral exploration according to claim 2, characterized in that, The data preprocessing module preprocesses the original data of the geological and mineral exploration site collected based on the spatial dimension, angular dimension, and time dimension to obtain the optimized data of the geological and mineral exploration site, including: According to the spatial data in the original data of the geological and mineral exploration site, the Kriging interpolation algorithm is used to process the geological data in the original data of the geological and mineral exploration site to generate a continuous spatial prediction model; Among them, the Kriging interpolation algorithm is used to predict the geological parameter values at unknown positions based on the spatial autocorrelation of the known sample point positions; the continuous spatial prediction model is used to determine an estimated value of a geological parameter for each geographical position in the exploration area; ； Among them, is the estimated value of geological parameters at an unknown geographical location ; is the actual value of geological parameters at the known sample point location ; is the weight coefficient corresponding to the sample point location ; is calculated based on spatial autocorrelation and the spatial variability of geological parameters; is a region-specific adjustment factor used to adjust the estimated value of geological parameters to adapt to local geological changes; According to the detection angle information carried by the original data of the geological and mineral exploration site, the beamforming algorithm is used to process the geological data in the original data of the geological and mineral exploration site to obtain angle-enhanced geological data; among them, the beamforming algorithm enhances the signal from a specific angle by forming a beam pointing in a specific direction; According to the angle-enhanced geological data, the continuous spatial prediction model is optimized in combination; According to the time series information carried by the original data of the geological and mineral exploration site, the SARIMA model is used to process the geological data in the original data of the geological and mineral exploration site to obtain time series identification geological data; among them, the SARIMA model is used to identify and predict the seasonality and trends in time series data; Based on the optimized continuous spatial prediction model, the angle-enhanced geological data, and the time series identification geological data, the optimized data of the geological and mineral exploration site is determined.

5. The data extraction system for digital geological and mineral exploration according to claim 4, wherein The data extraction module extracts key geological information from the optimized data of the geological and mineral exploration site to generate three-dimensional spatial characteristics of the geological and minerals, including: Based on the optimized data of the geological and mineral exploration site, the physical constrained neural network is used to analyze the relationship between the rock mechanics parameters and the geological and mineral distribution to extract the rock mechanics information of the geological body; Among them, the rock mechanics parameters include strength parameters, deformation parameters, failure parameters, porosity parameters, and wave velocity parameters; the physical constrained neural network includes a physical constraint layer; the relationship between the strength parameters, the deformation parameters, the failure parameters, the porosity parameters, and the wave velocity parameters is embedded in the physical constraint layer in the form of a differential equation; According to the optimized data of the geological and mineral exploration site, a three-dimensional geological model is constructed; The three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies are extracted from the constructed three-dimensional geological model.

6. The data extraction system for digital geological and mineral exploration according to claim 5, wherein The physical constraint neural network includes an input layer, a hidden layer, and an output layer. Among them, the input layer is used to receive the optimized data of the geological and mineral exploration site; the hidden layer includes a geological feature extraction layer, a spatio-temporal feature fusion layer, and a detection angle processing layer; the output layer is used to output the relationship between the predicted rock mechanics parameters and the geological and mineral distribution. Among them, the geological feature extraction layer is an adaptive pooling layer, and the adaptive pooling layer dynamically adjusts the pooling window size according to the spatial distribution characteristics of the optimized data of the geological and mineral exploration site to extract key geological features. The spatio-temporal feature fusion layer combines a long short-term memory network and a gated recurrent unit to process the time series characteristics of the optimized data of the geological and mineral exploration site, and fuses spatial information to capture the changing trends of the key geological features over time and space. The detection angle processing layer uses a rotation-invariant network structure to capture the feature changes of the key geological features at different detection angles. Among them, the composite loss function adopted by the physical constraint neural network consists of the following three parts: data fitting error, penalty term for physical constraint violation, and angle information consistency loss. The data fitting error includes the difference between the predicted value and the actual value measured using the mean square error. The penalty term for physical constraint violation includes the sum of squares based on the physical equation residuals, which penalizes the situation where the output of the physical constraint neural network violates physical laws. The angle information consistency loss is used to maintain the consistency of the physical constraint neural network when processing different detection angles.

7. The data extraction system for digital geological and mineral exploration according to claim 6, characterized in that The detection angle processing layer performs feature extraction based on the following formula: ； Among them, is a feature extraction function at a geographical location and a detection angle for representing the feature intensity of key geological features at a geographical location and a detection angle ; is the number of detection angles, representing the total number of different angles; is an index variable of the detection angle; is the th weight coefficient of the detection angle, used to represent the importance of features at this angle; is the position of the key geological feature at the th detection angle; is the Euclidean distance between the geographical location and the position of the key geological feature ; is a standard deviation parameter for controlling the locality of feature extraction, and the locality of feature extraction is the influence range of feature points on the surrounding area; is the Dirac function for ensuring that feature extraction is performed at an angle ; when is equal to , the function value of the Dirac function is 1, otherwise it is 0.

8. The data extraction system for digital geological and mineral exploration according to claim 5, characterized in that Based on the optimized data of the geological and mineral exploration site, a three-dimensional geological model is constructed. Extracting the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault planes, and the three-dimensional arrangement information of the ore bodies from the constructed three-dimensional geological model includes: Using GIS software to map the optimized data of the geological and mineral exploration site to three-dimensional space through coordinate transformation. Based on three-dimensional geological modeling software, initialize a three-dimensional geological model that integrates geological structure and attributes. Among them, the three-dimensional geological model includes strata, rock masses, faults, low-resistance anomalies, and ore bodies. Based on the optimized data of the geological and mineral exploration site, combined with three-dimensional spatial analysis algorithms, extract geological profile feature point information and ore body feature point information. Fit the geological profile feature point information and the ore body feature point information to construct the fault plane and the ore body. Model the strata and adjust the topological relationship between the strata and the fault plane. Based on the ore-controlling factor indicators, adjust the topological relationship between the ore body and the fault plane to complete the construction of the three-dimensional geological model. Among them, the ore-controlling factor indicators include the fault plane distance field factor, the fault plane trend undulation factor, and the fault plane slope factor. Extract the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fault surfaces, and the three-dimensional arrangement information of the ore bodies according to the constructed three-dimensional geological model; wherein, the three-dimensional arrangement information of the strata includes the continuity, angle, dip angle, and thickness variation of the strata; the three-dimensional arrangement information of the fault surfaces includes the extension length, dip angle, and displacement amount of the fault surfaces; the three-dimensional arrangement information of the ore bodies includes the shape, size, extension direction, and grade distribution of the ore bodies.

9. The data extraction system for digital geological and mineral exploration according to claim 2, wherein The original data at the geological and mineral exploration site includes multiple types of the following data: geological data, geophysical data, geochemical data, remote sensing data, mineral data, geophysical and geochemical exploration data, sampling data, borehole data, map data, and geological logging data for the exploration of special mineral species; Among them, the remote sensing data, the geophysical and geochemical exploration data, the borehole data, and the map data contain the spatial data in the original data at the geological and mineral exploration site.

10. The data extraction system for digital geological and mineral exploration according to claim 2, wherein The data extraction system for digital geological and mineral exploration further includes: An exploration site data visualization module, which is used to convert the original data and optimized data at the geological and mineral exploration site into graphics, charts, or three-dimensional models by using a graphics rendering algorithm; receive the rotation operation, zoom operation, and slicing operation of the user on the graphics, charts, or three-dimensional models, and change the display angle of the graphics, charts, or three-dimensional models; Among them, the data visualization module integrates a dynamic time axis, and the dynamic time axis is used to assist in displaying the trend of the original data and optimized data at the geological and mineral exploration site changing with time.

Images