A data extraction method and system for digital geological and mineral exploration

Abstract

The present invention discloses a data extraction method and system for digital geological and mineral exploration, which relate to the technical field of geological and mineral exploration. The method comprises: collecting original data of a geological and mineral exploration site based on spatial attitude perception geological detection equipment; pre-processing the collected original data of the geological and mineral exploration site based on spatial dimension, angular 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 the geological and mineral resources; and determining a mineral spatial distribution model of the exploration area according to the three-dimensional spatial characteristics of the geological and mineral resources; the spatial information of the geological structure includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces and three-dimensional arrangement information of ore bodies.

Description

Technical Field

[0001] The present 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 the geology and mineral resources of ore deposits based on regional geological surveys, in accordance with the needs of national economic and social development, and using geological science theories and a variety of exploration techniques and methods. Geological and mineral exploration can reveal information such as groundwater resources, soil quality, and vegetation distribution, playing a vital role in environmental protection and ecological restoration. It is also crucial for mineral reserve assessment and mining planning. By analyzing geological data, the resource volume and grade of a deposit can be estimated, providing a basis for development plans and economic assessments.

[0003] Geological and mineral exploration plays an indispensable and far-reaching role in national resource strategic planning, sustainable economic development, and the prosperity of the mining industry. It helps reduce the environmental impact of mineral development and provides critical support for sustainable development and utilization. Geological and mineral exploration can drive economic development, especially as industrialization continues to grow demand for mineral resources, and geological and mineral exploration is crucial to meeting this demand.

[0004] In related technologies, geological and mineral exploration data processing and analysis primarily rely on manual analysis. This approach suffers from the following drawbacks: manual data analysis requires significant time and human resources, limiting the speed and efficiency of data processing and making it difficult to meet the demands of large-scale, rapid exploration. Furthermore, as the volume of geological and mineral exploration data continues to increase, manual analysis methods struggle to cope with the demands of large-scale data processing, and the limitations of manual analysis become even more pronounced when the data volume is enormous.

[0005] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

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

[0007] The present application provides a data extraction method for digital geological and mineral exploration, comprising: collecting original data of a geological and mineral exploration site based on spatial posture perception geological detection equipment; wherein the original data of the geological and mineral exploration site carries corresponding geographic positioning information, detection angle information and time series information; pre-processing the collected original data of the geological and mineral exploration site based on 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 minerals; and determining a mineral spatial distribution model of the exploration area based on the three-dimensional spatial characteristics of geological minerals; wherein the key geological information includes spatial information of geological structure and rock mechanics information of geological bodies; the spatial information of the geological structure includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces 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 acquisition module, which is used to collect original data of the geological and mineral exploration site based on spatial posture perception geological detection equipment; 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, which is used to preprocess the collected original data of the geological and mineral exploration site based on spatial dimension, angle dimension and time dimension to obtain optimized data of the geological and mineral exploration site; a data extraction module, which is used to extract key geological information from the optimized data of the geological and mineral exploration site to generate three-dimensional spatial characteristics of geological minerals; and determine the mineral spatial distribution model of the exploration area based on the three-dimensional spatial characteristics of geological minerals; wherein the key geological information includes spatial information of geological structure and rock mechanics information of geological bodies; the spatial information of geological structure includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces and three-dimensional arrangement information of ore bodies.

[0009] Based on the embodiments provided in this application, the following are achieved: using spatial posture perception equipment to directly collect data at the exploration site, reducing errors that may occur in the manual transmission process of data and improving the original accuracy of the data; through the generation of three-dimensional spatial features, multi-dimensional analysis of data is achieved, 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 spatial information of geological structure and rock mechanics information, the spatial distribution of minerals can be determined more accurately; automated data collection and preprocessing reduce dependence on manual operations and reduce labor costs, especially in large-scale exploration projects, this cost saving is particularly obvious; optimized data processing allows exploration work to respond quickly, especially in urgent or time-sensitive exploration tasks, and exploration results can be provided quickly; through the establishment of three-dimensional spatial features and mineral spatial distribution models, a more intuitive and scientific basis is provided 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 and constitute a part of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0011] Figure 1 This is a flow chart of an optional data extraction method for digital geological and mineral exploration according to an embodiment of the present application;

[0012] Figure 2 This is a structural diagram of an optional digital geological and mineral exploration data extraction system according to an embodiment of the present application.

[0013] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] Alternatively, as Figure 1 As shown, the present application provides a data extraction method for digital geological and mineral exploration, comprising:

[0016] S101, collecting raw data from a geological and mineral exploration site using a geological detection device with spatial attitude perception; wherein the raw data from the geological and mineral exploration site carries corresponding geographic positioning information, detection angle information, and time series information;

[0017] S102, pre-processing the collected original data of the geological and mineral exploration site based on the spatial dimension, the angular dimension, and the time dimension to obtain optimized data of the geological and mineral exploration site;

[0018] S103, extracting 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; and determining a mineral spatial distribution model of the exploration area based on the three-dimensional spatial characteristics of the geological and mineral resources;

[0019] Among them, key geological information includes spatial information of geological structure and rock mechanics information of geological body; spatial information of geological structure includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surface and three-dimensional arrangement information of ore body.

[0020] Based on the embodiments provided in this application, the following are achieved: using spatial posture perception equipment to directly collect data at the exploration site, reducing errors that may occur in the manual transmission process of data and improving the original accuracy of the data; through the generation of three-dimensional spatial features, multi-dimensional analysis of data is achieved, 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 spatial information of geological structure and rock mechanics information, the spatial distribution of minerals can be determined more accurately; automated data collection and preprocessing reduce dependence on manual operations and reduce labor costs, especially in large-scale exploration projects, this cost saving is particularly obvious; optimized data processing allows exploration work to respond quickly, especially in urgent or time-sensitive exploration tasks, and exploration results can be provided quickly; through the establishment of three-dimensional spatial features and mineral spatial distribution models, a more intuitive and scientific basis is provided for mining decision-making, enhancing the accuracy and effectiveness of decision-making.

[0021] Alternatively, as Figure 2 As shown, the present application provides a data extraction system for digital geological and mineral exploration, comprising:

[0022] The data acquisition module 201 is used to collect raw data from the geological and mineral exploration site based on the spatial posture perception geological detection equipment; wherein the raw data from the geological and mineral exploration site carries corresponding geographic positioning information, detection angle information and time series information;

[0023] The data preprocessing module 202 is used to preprocess the original data collected from the geological and mineral exploration site based on the spatial dimension, the angular dimension and the time dimension to obtain optimized data of the geological and mineral exploration site;

[0024] The data extraction module 203 is used to extract 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; and to determine the mineral spatial distribution model of the exploration area based on the three-dimensional spatial characteristics of the geological and mineral resources;

[0025] Among them, key geological information includes spatial information of geological structure and rock mechanics information of geological body; spatial information of geological structure includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surface and three-dimensional arrangement information of ore body.

[0026] Furthermore, the data acquisition module collects raw data from the geological and mineral exploration site based on the spatial posture perception geological detection equipment, including:

[0027] Collect historical geological and mineral exploration data; historical geological and mineral exploration data includes geological structure information, mineral distribution information, and topographic and geomorphological information;

[0028] Generate a basic map of the exploration area using a geographic information system combined with historical geological and mineral exploration data; the basic map includes geological structure information, mineral deposit location information, and terrain elevation information;

[0029] Analyze the labels of historical geological and mineral exploration data and extract key parameters that affect mineral exploration; labels include success labels and failure labels; key parameters include rock type, mineralization intensity, and geological age;

[0030] Marking known geological features on the base map and enhancing the base map through remote sensing technology; known geological features include known faults, known folds, and known mineralized zones;

[0031] Evaluate the exploration risk and potential value of each sub-exploration area within the exploration area based on basic maps and historical geological and mineral exploration data;

[0032] Based on the assessment of the exploration risk and potential value of each sub-exploration area, sub-exploration tasks are assigned to each sub-exploration area, and the route of the spatial attitude perception geological exploration equipment is planned for each sub-exploration task. Sub-exploration tasks include drilling and sampling geophysical exploration.

[0033] Furthermore, the data preprocessing module preprocesses the raw data collected from the geological and mineral exploration site based on the spatial dimension, the angular dimension, and the temporal dimension to obtain optimized data of the geological and mineral exploration site, including:

[0034] According to the spatial data in the original data of the geological and mineral exploration site, the geological data in the original data of the geological and mineral exploration site are processed using the Kriging interpolation algorithm to generate a continuous spatial prediction model;

[0035] Among them, the Kriging interpolation algorithm is used to predict the geological parameter value at an unknown location 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;

[0036] ;

[0037] in, In an unknown geographical location Estimated values of geological parameters; At the known sample point location Actual values of geological parameters; corresponds to the sample point position The weight coefficient of It is calculated based on spatial autocorrelation and spatial variability of geological parameters; are region-specific adjustment factors used to adjust the estimates of geological parameters to accommodate local variations in geology; Determined based on geological principles;

[0038] Based on the detection angle information carried by the raw data from the geological and mineral exploration site, the geological data in the raw data of the geological and mineral exploration site is processed using a beamforming algorithm to obtain angle-enhanced geological data. The beamforming algorithm enhances the signal from a specific angle by forming a beam pointing in a specific direction.

[0039] By optimizing the continuous spatial prediction model based on the angular enhancement of geological data, we can enhance the spatial signal of specific geological parameters. For example, if the signal at a certain angle is strong, we can weight the geological parameter estimate at that angle in the continuous spatial prediction model to reflect the signal strength. Based on the processing results of the detection angle information, we can optimize the continuous spatial prediction model. This means integrating the angle information into the spatial model to improve the accuracy of geological parameter estimation, especially in areas where the signal source has a strong directionality.

[0040] Based on the time series information carried by the raw data from the geological and mineral exploration site, the SARIMA model is used to process the geological data in the raw data from the geological and mineral exploration site to obtain time series identification geological data. 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 different time points in the same geographical location.

[0041] Based on the optimized continuous spatial prediction model, angle-enhanced geological data, and time-series identification geological data, the optimized data of the geological and mineral exploration site are determined.

[0042] 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 features of the geological and mineral resources, including:

[0043] Based on the optimized data of geological and mineral exploration sites, the relationship between rock mechanical parameters and geological and mineral distribution is analyzed using a physical constraint neural network to extract rock mechanical information of the geological body;

[0044] The rock mechanics parameters include strength parameters, deformation parameters, failure parameters, porosity parameters, and wave velocity parameters; the physical constraint neural network includes a physical constraint layer; the relationships among 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;

[0045] Strength parameters include uniaxial compressive strength, tensile strength and shear strength; deformation parameters include elastic modulus and shear modulus; failure parameters include internal friction angle and cohesion; porosity parameters include porosity and permeability; wave velocity parameters include longitudinal wave velocity and shear wave velocity.

[0046] Differential equations can include:

[0047] ;

[0048] in, 、 Represents the spatial coordinates of 、 、 Components, which are coordinate axes in three-dimensional space and can correspond to the x, y, and z axes; is the stress tensor, which represents the internal force of the rock in different directions; Represents time; is the strain tensor, which represents the deformation of the rock; is the energy storage function of rock, which is related to the strength and deformation parameters of rock; is the pore pressure, which is related to the porosity parameters; is the density of the rock, which is related to the wave velocity parameter; is the velocity vector, indicating the moving speed of the rock; is the coupling coefficient, which describes the influence of rock failure parameters on the stress-strain relationship;

[0049] Construct a three-dimensional geological model based on optimized data from geological and mineral exploration sites;

[0050] The three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fracture surfaces and the three-dimensional arrangement information of the ore bodies are extracted from the constructed three-dimensional geological model.

[0051] Furthermore, the physical constraint neural network includes an input layer, a hidden layer, and an output layer; wherein the input layer is used to receive optimization data from the geological and mineral exploration site; the hidden layer includes a geological feature extraction layer, a spatiotemporal feature fusion layer, and a detection angle processing layer; and the output layer is used to output the relationship between the predicted rock mechanical parameters and the geological and mineral distribution;

[0052] Among them, the geological feature extraction layer is an adaptive pooling layer, which dynamically adjusts the pooling window size according to the spatial distribution characteristics of the optimized data at the geological and mineral exploration site to extract key geological features;

[0053] The spatiotemporal feature fusion layer combines long short-term memory networks and gated recurrent units to process the time series characteristics of optimization data from geological and mineral exploration sites, and integrates spatial information to capture the changing trends of key geological features over time and space.

[0054] The detection angle processing layer uses a rotation-invariant network structure to capture the characteristic changes of key geological features at different detection angles. Rotation-invariant network structures, such as Rotation Equivariant Convolutional Networks, can maintain feature consistency at different angles, thereby more accurately extracting geological information related to the detection angle.

[0055] 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; data fitting error includes the difference between the predicted value and the actual value measured by mean square error; the penalty term for physical constraint violation includes the sum of squares of the residuals based on the physical equation, which penalizes the situation where the output of the physical constraint neural network violates the laws of physics; angle information consistency loss is used to maintain the consistency of the physical constraint neural network when processing different detection angles.

[0056] Furthermore, the detection angle processing layer performs feature extraction based on the following formula:

[0057] ;

[0058] in, Is in geographical location and detection angle The feature extraction function under is used to represent the geographical location of key geological features. and detection angle The characteristic intensity under is the number of detection angles, indicating the total number of different angles; The index variable for detecting angle; It is The weight coefficient of each detection angle is used to indicate the importance of the feature at that angle; It is The location of key geological features at each detection angle; It's geographical location and the location of key geological features The Euclidean distance between It 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 the feature point on the surrounding area. It's Dirac Function to ensure that feature extraction is at an angle Next; when equal When Dirac The function value is 1 if the function is true, and 0 otherwise.

[0059] Furthermore, a three-dimensional geological model is constructed based on the optimized data of the geological and mineral exploration site; and three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces, and three-dimensional arrangement information of ore bodies are extracted from the constructed three-dimensional geological model, including:

[0060] Use GIS software to map the optimized data of the geological and mineral exploration site into three-dimensional space through coordinate conversion; the GIS software can be ArcMap;

[0061] Initialize a 3D geological model integrating geological structure and properties using 3D geological modeling software; the 3D geological model includes strata, rock masses, faults, low-resistivity anomalies, and ore bodies;

[0062] Based on the optimized data of geological and mineral exploration sites, combined with three-dimensional spatial analysis algorithms, the geological profile feature point information and ore body feature point information are extracted;

[0063] Fitting geological profile feature point information and ore body feature point information to construct fracture surface and ore body;

[0064] Model the strata and adjust the topological relationship between the strata and the fracture surface;

[0065] Based on the ore-controlling factor indicators, the topological relationship between the ore body and the fracture surface is adjusted to complete the construction of the three-dimensional geological model. Among them, the ore-controlling factor indicators include the fracture surface distance field factor, the fracture surface trend factor, and the fracture surface slope factor.

[0066] Based on the constructed three-dimensional geological model, the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fracture surface and the three-dimensional arrangement information of the ore body are extracted; among them, the three-dimensional arrangement information of the strata includes the continuity, angle, inclination and thickness change of the strata; the three-dimensional arrangement information of the fracture surface includes the extension length, inclination and dislocation of the fracture surface; the three-dimensional arrangement information of the ore body includes the shape, size, extension direction and grade distribution of the ore body.

[0067] Furthermore, the raw data from the geological and mineral exploration site include multiple types of the following data: geological data, geophysical data, geochemical data, remote sensing data, mineral data, geophysical and geochemical data, sampling data, drilling data, map data, and geological cataloging data for special mineral exploration;

[0068] Among them, remote sensing data, geophysical and geochemical exploration data, drilling data and map data contain spatial data from the original data of geological and mineral exploration sites.

[0069] Furthermore, the data extraction system for digital geological and mineral exploration also includes:

[0070] The exploration site data visualization module is used to convert the original data and optimized data of the geological and mineral exploration site into graphics, charts or 3D models using a graphics rendering algorithm; it receives user rotation, scaling and slicing operations on the graphics, charts or 3D models to change the display angle of the graphics, charts or 3D models;

[0071] Among them, the data visualization module integrates a dynamic timeline, which is used to assist in displaying the original data of the geological and mineral exploration site and optimize the trend of data changes over time.

[0072] It should be noted that in this application, the embodiments implemented on the data extraction system side of digital geological and mineral exploration can be referenced with the embodiments implemented on the data extraction method side of digital geological and mineral exploration, and this application will not repeat them one by one.

[0073] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A data extraction method for digital geological and mineral exploration, characterized in that: include: The original data of the geological and mineral exploration site is collected by the geological detection equipment based on the spatial posture perception; wherein the original data of the geological and mineral exploration site carries the corresponding geographic positioning information, detection angle information and time series information; Based on the spatial dimension, angle dimension and time dimension, the original data collected from the geological and mineral exploration site are preprocessed to obtain optimized data of the geological and mineral exploration site; Extracting key geological information from optimized data at a geological and mineral exploration site to generate three-dimensional spatial features of geological and mineral resources; and determining a mineral spatial distribution model for an exploration area based on the three-dimensional spatial features of geological and mineral resources; extracting key geological information from optimized data at a geological and mineral exploration site to generate three-dimensional spatial features of geological and mineral resources, including: Based on the optimized data of the geological and mineral exploration site, a physical constraint neural network is used to analyze the relationship between rock mechanical parameters and geological and mineral distribution to extract rock mechanical information of the geological body; The rock mechanics parameters include strength parameters, deformation parameters, failure parameters, porosity parameters, and wave velocity parameters; the physical constraint 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; constructing a three-dimensional geological model based on the optimized data of the geological and mineral exploration site; extracting three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces and three-dimensional arrangement information of ore bodies from the constructed three-dimensional geological model; The key geological information includes spatial information of geological structures and rock mechanics information of geological bodies; the spatial information of geological structures includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces and three-dimensional arrangement information of ore bodies.

2. A data extraction system for digital geological and mineral exploration, wherein the system implements the method according to claim 1, characterized in that: include: A data acquisition module is used to collect raw data from geological and mineral exploration sites based on spatial posture perception geological detection equipment; wherein the raw data from the geological and mineral exploration sites carries corresponding geographic positioning information, detection angle information and time series information; The data preprocessing module is used to preprocess the original data collected from the geological and mineral exploration site based on the spatial dimension, angular dimension and time dimension to obtain optimized data of the geological and mineral exploration site; A data extraction module is used to extract 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; and to determine the mineral spatial distribution model of the exploration area based on the three-dimensional spatial characteristics of the geological and mineral resources; The key geological information includes spatial information of geological structures and rock mechanics information of geological bodies; the spatial information of geological structures includes three-dimensional arrangement information of strata, three-dimensional arrangement information of fracture surfaces and three-dimensional arrangement information of ore bodies.

3. The data extraction system for digital geological and mineral exploration according to claim 2 is characterized in that: The data acquisition module collects raw data from the geological and mineral exploration site based on the spatial posture perception geological detection equipment, including: Collect historical geological and mineral exploration data; wherein the historical geological and mineral exploration data includes geological structure information, mineral distribution information, and topographic and geomorphological information; Using a geographic information system, combined with the historical geological and mineral exploration data, to generate a basic map of the exploration area; wherein the basic map includes geological structure information, mineral deposit location information, and terrain elevation information; Analyze the labels of the historical geological and mineral exploration data to extract 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 base map and enhancing the base map using remote sensing technology; wherein the known geological feature points include known faults, known folds, and known mineralized zones; Based on the basic map and the historical geological and mineral exploration data, evaluating the exploration risk and potential value 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, a sub-exploration task is allocated to each sub-exploration area, and a travel route of the spatial posture perception geological exploration equipment is planned for each sub-exploration task.

4. The data extraction system for digital geological and mineral exploration according to claim 2 is characterized in that: The data preprocessing module preprocesses the original data collected from the geological and mineral exploration site based on the spatial dimension, the angular dimension, and the time dimension to obtain optimized data of the geological and mineral exploration site, including: Processing the geological data in the original data of the geological and mineral exploration site using a Kriging interpolation algorithm based on the spatial data in the original data of the geological and mineral exploration site to generate a continuous spatial prediction model; The Kriging interpolation algorithm is used to predict the geological parameter value at an unknown location based on the spatial autocorrelation of 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; ； in, In an unknown geographical location Estimated values of geological parameters; At the known sample point location Actual values of geological parameters; corresponds to the sample point position The weight coefficient of It is calculated based on spatial autocorrelation and spatial variability of geological parameters; are region-specific adjustment factors used to adjust the estimates of geological parameters to accommodate local variations in geology; Based on the detection angle information carried by the raw data from the geological and mineral exploration site, the geological data in the raw data from the geological and mineral exploration site is processed using a beamforming algorithm to obtain angle-enhanced geological data; wherein the beamforming algorithm enhances signals from a specific angle by forming a beam pointing in a specific direction; Optimizing the continuous spatial prediction model combination based on the angularly enhanced geological data; According to the time series information carried by the raw data of the geological and mineral exploration site, the geological data in the raw data of the geological and mineral exploration site is processed using the SARIMA model to obtain time series identification geological data; wherein the SARIMA model is used to identify and predict seasonality and trends in the 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 are determined.

5. The data extraction system for digital geological and mineral exploration according to claim 4 is characterized in that: The physical constraint neural network includes an input layer, a hidden layer, and an output layer; wherein 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 spatiotemporal feature fusion layer, and a detection angle processing layer; and the output layer is used to output the relationship between the predicted rock mechanical parameters and the geological and mineral distribution; The geological feature extraction layer is an adaptive pooling layer, which 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 spatiotemporal feature fusion layer combines a long short-term memory network and a gated recurrent unit to process the time series characteristics of the optimization 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 characteristic 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 by mean square error; the penalty term for physical constraint violation includes the sum of squares of the residuals based on the physical equation, which penalizes the situation where the output of the physical constraint neural network violates the laws of physics; the angle information consistency loss is used to maintain the consistency of the physical constraint neural network when processing different detection angles.

6. The data extraction system for digital geological and mineral exploration according to claim 5 is characterized in that: The detection angle processing layer performs feature extraction based on the following formula: ； in, Is in geographical location and detection angle The feature extraction function under is used to represent the geographical location of key geological features. and detection angle The characteristic intensity under is the number of detection angles, indicating the total number of different angles; The index variable for detecting angle; It is The weight coefficient of each detection angle is used to indicate the importance of the feature at that angle; It is The location of key geological features at each detection angle; It's geographical location and the location of key geological features The Euclidean distance between It 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 the feature point on the surrounding area. It's Dirac Function to ensure that feature extraction is at an angle Next; when equal When Dirac The function value is 1 if the function is true, and 0 otherwise.

7. The data extraction system for digital geological and mineral exploration according to claim 4 is characterized in that: The method comprises: constructing a three-dimensional geological model based on the optimized data of the geological and mineral exploration site; extracting the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fracture surface, and the three-dimensional arrangement information of the ore body from the constructed three-dimensional geological model; and Mapping the optimized data of the geological and mineral exploration site into three-dimensional space through coordinate conversion using GIS software; Initializing a 3D geological model integrating geological structure and properties based on 3D geological modeling software; wherein the 3D geological model includes strata, rock masses, faults, low-resistivity anomalies, and ore bodies; Based on the optimized data of the geological and mineral exploration site, combined with a three-dimensional spatial analysis algorithm, the geological profile feature point information and the ore body feature point information are extracted; Fitting the geological profile feature point information and the ore body feature point information to construct the fracture surface and the ore body; Modeling the stratum and adjusting the topological relationship between the stratum and the fracture surface; Based on the ore-controlling factor indicators, the topological relationship between the ore body and the fracture surface is adjusted to complete the construction of the three-dimensional geological model; wherein the ore-controlling factor indicators include the fracture surface distance field factor, the fracture surface trend fluctuation factor, and the fracture surface slope factor; Based on the constructed three-dimensional geological model, the three-dimensional arrangement information of the strata, the three-dimensional arrangement information of the fracture surface and the three-dimensional arrangement information of the ore body are extracted; wherein the three-dimensional arrangement information of the strata includes the continuity, angle, inclination and thickness change of the strata; the three-dimensional arrangement information of the fracture surface includes the extension length, inclination and dislocation of the fracture surface; the three-dimensional arrangement information of the ore body includes the shape, size, extension direction and grade distribution of the ore body.

8. The data extraction system for digital geological and mineral exploration according to claim 2 is characterized in that: The original data of the geological and mineral exploration site include multiple types of the following data: geological data, geophysical data, geochemical data, remote sensing data, mineral data, geophysical and geochemical data, sampling data, drilling data, map data and geological cataloging data of special mineral exploration; The remote sensing data, the geophysical and geochemical exploration data, the drilling data and the map data contain spatial data in the original data of the geological and mineral exploration site.

9. The data extraction system for digital geological and mineral exploration according to claim 2 is characterized in that: The digital geological and mineral exploration data extraction system also includes: The exploration site data visualization module is used to convert the original data and optimized data of the geological and mineral exploration site into graphics, charts or three-dimensional models using a graphics rendering algorithm; receive user rotation operations, scaling operations and slicing operations on the graphics, charts or three-dimensional models, and change the display angle of the graphics, charts or three-dimensional models; The data visualization module integrates a dynamic timeline, which is used to assist in displaying the original data of the geological and mineral exploration site and optimize the trend of data changes over time.

Images