Mine water depth sealing layer site selection evaluation method and system based on multi-parameter coupling

Abstract

The invention relates to a mine water depth sealing layer site selection evaluation method and system based on multi-parameter coupling. The method comprises the following steps: acquiring geological, hydrological and engineering parameters through geological exploration equipment and the like, and carrying out wavelet transform noise reduction and Kriging interpolation processing; determining a key parameter set and weight by using an analytic hierarchy process, constructing a comprehensive evaluation model coupling the analytic hierarchy process and fuzzy mathematics, and generating a membership matrix and a multi-field coupling numerical model; and calculating a fitness index through spatial overlay analysis, generating a sealing suitability partition map, outputting an evaluation report containing risk early warning, and generating an interactive three-dimensional site selection decision interface. The system comprises a data acquisition and preprocessing module, a parameter processing module, a weight distribution module and the like. The simulation precision and the risk positioning efficiency are improved, and a comprehensive evaluation scheme is provided for site selection of the sealing layer of the water depth of the mine.

Description

Technical Field

[0001] The present invention relates to the technical field of site selection for deep water storage layers in mines based on multi-parameter coupling, and specifically to a site selection and evaluation method and system for deep water storage layers in mines based on multi-parameter coupling. Background Art

[0002] With the growing demand for carbon storage technology in deep mine water storage layer site selection, current methods rely heavily on independent analysis of geological structures or hydrological parameters, lacking a comprehensive multi-parameter coupled assessment system. This makes it difficult to fully reflect the sealing, stability, and capacity adaptability of the storage layer. Geostress monitoring data is severely affected by noise, and the spatial distribution of permeability is poorly modeled. This leads to significant deviations in the simulation results of the seepage and stress fields, making it impossible to couple the dynamic changes of engineering parameters in real time. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for site selection and evaluation of deep water storage layers in mines based on multi-parameter coupling, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for site selection and evaluation of deep water storage layers in mines based on multi-parameter coupling, comprising the following steps:

[0005] S1. Using geological exploration equipment, distributed fiber optic sensor arrays, and 3D seismic exploration technology, obtain geological parameters, hydrological parameters, and engineering parameters of the target area. The geological parameters include geological structural stability, geostress distribution characteristics, rock permeability threshold, and rock porosity; the hydrological parameters include groundwater runoff rate, water quality composition, and hydraulic gradient; and the engineering parameters include mine drainage volume, sealing layer pressure, and sealing layer burial depth.

[0006] Performing wavelet transform noise reduction processing on the ground stress monitoring data in the geological parameters, and generating a permeability spatial distribution contour map by Kriging interpolation method;

[0007] S2. Establish a multi-level evaluation index system based on the analytic hierarchy process to determine the key parameter set that affects the location of the storage layer. The key parameter set includes geological structural stability, hydrogeological sealing, ground stress distribution characteristics, rock layer permeability threshold, and storage capacity estimation parameters;

[0008] Assigning preset weight values ​​to the key parameters, where:

[0009] The weight of geological structure stability is 30%-40%;

[0010] The weight of hydrogeological closure is 25%-35%;

[0011] The weight of the ground stress distribution characteristics is 15%-25%;

[0012] The rock formation permeability threshold and storage capacity estimation parameters are weighted at 5%-10% each;

[0013] S3. Construct a comprehensive evaluation model that couples the analytic hierarchy process and fuzzy mathematics, and use the membership function to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate to generate the membership matrix of each evaluation index;

[0014] Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model;

[0015] S4. Perform spatial overlay analysis on the three-dimensional geological modeling data and the multi-parameter evaluation results to calculate the storage layer suitability index of the target area, where the suitability index comprehensively reflects the storage capacity coefficient, the sealing index, and the long-term stability index;

[0016] S5. Generate a storage suitability zoning map with graded coloring features based on the fitness index, where dark blue areas represent preferred target areas with a storage suitability grade ≥ IV;

[0017] Output an assessment report containing the spatial coordinates of the storage layer, estimated storage capacity, and risk warning indicators, including predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information;

[0018] An interactive three-dimensional site selection decision interface is generated through a virtual reality engine, which displays the optimized layout plan of injection well locations in real time and marks high-risk areas.

[0019] Furthermore, the weight allocation analytic hierarchy process in S2 includes the following sub-steps:

[0020] S201, constructing a judgment matrix including the key parameters, wherein the judgment matrix is ​​generated by an expert scoring method or a historical data statistics method;

[0021] S202, performing a consistency check on the judgment matrix, and when the consistency ratio CR ≤ 0.1, calculating the weight value using the eigenvector method;

[0022] S203. When CR>0.1, recalculate the weight value by adjusting the judgment matrix element value until the consistency test requirement is met.

[0023] Furthermore, the membership function in S3 adopts an S-shaped curve function, and its expression is:

[0024]

[0025] Wherein, k is the slope coefficient of the curve, c is the threshold inflection point, and the values ​​of k and c are determined by fitting the historical observation data of the degree of development of rock fractures by the least squares method.

[0026] Furthermore, the multi-field coupling numerical model in S3 includes the following constraints:

[0027] The Mohr-Coulomb yield criterion is used for the analysis of the in-situ stress field. The criterion parameters include the internal friction angle and cohesion c;

[0028] The seepage field simulation is based on Darcy's law, and the permeability parameters of the Darcy's law are dynamically updated through the permeability spatial distribution contour map in S1;

[0029] The calculation of material migration and diffusion adopts the convection-diffusion equation, which includes the diffusion coefficient D and the convection velocity v. The D and v are adjusted in real time according to the groundwater runoff rate in the hydrological parameters.

[0030] Furthermore, the risk warning indicators in S5 also include the following parameters:

[0031] The predicted value of fault activation probability is calculated using a fault activity classification model based on a support vector machine (SVM). The input parameters of the model include ground stress gradient, rock fracture toughness and historical earthquake activity data.

[0032] The CO2 leakage rate simulation value is numerically simulated using COMSOL multi-physics field coupling simulation software, combined with the permeability of the top and bottom plates of the storage layer and the storage pressure gradient.

[0033] Furthermore, the spatial overlay analysis in S4 adopts the GIS raster data overlay analysis method, which specifically includes:

[0034] Converting the three-dimensional geological modeling data into a raster data layer with a resolution of no less than 5m×5m×10m;

[0035] Resampling the multi-parameter evaluation results to ensure consistency with the spatial resolution of the raster data layer;

[0036] The weighted overlay operation generates a storage suitability index grid map, and the weighted coefficient is consistent with the weight value of the key parameter.

[0037] A mine water deep storage layer site selection and evaluation system based on multi-parameter coupling, characterized by including: a data acquisition and preprocessing module, a parameter processing and weight distribution module, a multi-parameter coupling analysis module, a spatial analysis and evaluation module, and a visualization output module

[0038] Data acquisition and preprocessing module:

[0039] Integrate geological radar, borehole detectors and distributed fiber optic sensors to collect geological parameters, hydrological parameters and engineering parameters of the target area;

[0040] It includes a wavelet transform noise reduction algorithm unit and a Kriging interpolation module, which are used to reduce the noise of the ground stress data and generate the permeability spatial distribution contour map;

[0041] Parameter processing and weight distribution module:

[0042] A multi-level evaluation index system is constructed based on the hierarchical analysis method to determine the key parameter set and assign weight values. The weight assignment rule is as follows:

[0043] The weight of geological structure stability is 30%-40%;

[0044] The weight of hydrogeological closure is 25%-35%;

[0045] The weight of the ground stress distribution characteristics is 15%-25%;

[0046] The rock formation permeability threshold and storage capacity estimation parameters are weighted at 5%-10% each;

[0047] Multi-parameter coupling analysis module:

[0048] It includes a fuzzy comprehensive evaluation unit, which is used to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate through the membership function and generate a membership matrix;

[0049] Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model;

[0050] Spatial Analysis and Evaluation Module:

[0051] The three-dimensional geological modeling data is coupled with the multi-parameter evaluation results through the spatial overlay analysis method to calculate the storage layer suitability index, which includes the storage capacity coefficient, the sealing index and the long-term stability index;

[0052] Visual output module:

[0053] Generate a storage suitability zoning map with graded coloring features, where the dark blue area represents the preferred target area with storage suitability grade ≥ IV;

[0054] Output an assessment report containing the spatial coordinates of the storage layer, estimated storage capacity, and risk warning indicators, including predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information;

[0055] An interactive three-dimensional site selection decision interface is generated through a virtual reality engine to display the optimized layout plan of injection well locations in real time, and is connected to the GIS spatial database to mark high-risk areas.

[0056] Furthermore, the data acquisition and preprocessing module also includes: a distributed fiber optic sensor array, configured to be arranged along the mine tunnel to monitor rock microseismic events and temperature field changes in real time; a geological radar data processing unit, used to invert the reflected wave data of the geological radar to generate a three-dimensional point cloud model of the rock stratification interface.

[0057] Furthermore, the multi-parameter coupling analysis module also includes:

[0058] A dynamic weight adjustment submodule, configured to dynamically modify the weight values ​​of the key parameters according to the real-time engineering parameters acquired in S1;

[0059] The sensitivity analysis submodule evaluates the sensitivity ranking of various parameters to the storage layer suitability index through Monte Carlo simulation.

[0060] Furthermore, the visual output module integrates the following functions:

[0061] The user interface supports inputting storage capacity thresholds, risk level thresholds, and economic cost parameters, and dynamically screening eligible storage layer areas;

[0062] The three-dimensional site selection map and the actual mine scene are superimposed through AR technology to achieve real-scene marking and risk warning of the storage layer location.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] Real-time monitoring of rock formation microseismicity and temperature fields using a distributed fiber optic sensor array, combined with three-dimensional point cloud modeling using geological radar, allows for millimeter-level precision in rock layer interfaces, addressing the insufficient resolution of traditional exploration data. Fuzzy processing is performed on the degree of fracture development and groundwater runoff rate based on an S-type membership function. The k and c parameters are determined by fitting historical data using the least squares method, resulting in a 92% or greater fit between the fuzzy evaluation results and the measured data. A three-dimensional decision-making interface constructed using a virtual reality engine supports real-time optimization of injection well locations. Combined with AR technology, the storage layer is annotated in real-world scenes, increasing the efficiency of locating risk areas by 60%. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0066] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] See also Figure 1 The present invention provides a technical solution: a method for site selection and evaluation of deep water storage layers in mines based on multi-parameter coupling, comprising the following steps:

[0068] S1. Using geological exploration equipment, distributed fiber optic sensor arrays, and 3D seismic exploration technology, obtain geological parameters, hydrological parameters, and engineering parameters of the target area. Geological parameters include geological structural stability, geostress distribution characteristics, rock permeability threshold, and rock porosity; hydrological parameters include groundwater runoff rate, water quality composition, and hydraulic gradient; and engineering parameters include mine drainage volume, sealing layer pressure, and sealing layer burial depth.

[0069] The ground stress monitoring data in the geological parameters are subjected to wavelet transform noise reduction processing, and the permeability spatial distribution contour map is generated by Kriging interpolation method;

[0070] S2. Establish a multi-level evaluation index system based on the analytic hierarchy process to determine the key parameter set that affects the storage layer site selection. The key parameter set includes geological structural stability, hydrogeological sealing, ground stress distribution characteristics, rock layer permeability threshold and storage capacity estimation parameters;

[0071] Assign preset weight values ​​to key parameters, where:

[0072] The weight of geological structure stability is 30%-40%;

[0073] The weight of hydrogeological closure is 25%-35%;

[0074] The weight of the ground stress distribution characteristics is 15%-25%;

[0075] The rock formation permeability threshold and storage capacity estimation parameters have a weight of 5%-10% each.

[0076] The AHP method for weight allocation in S2 includes the following sub-steps:

[0077] S201, constructing a judgment matrix including key parameters, where the judgment matrix is ​​generated by an expert scoring method or a historical data statistical method;

[0078] Expert scoring method: Organize 5-7 industry experts (covering the fields of geological engineering, hydrogeology, and mining engineering) to use a 1-9 scale to score the relative importance of key parameters in pairs.

[0079] Historical data statistical method: Collect storage layer site selection data for the target area over the past 10 years, count the frequency of influence of various parameters on the storage effect, and convert them into judgment matrix elements.

[0080] S202, performing consistency check on the judgment matrix, and when the consistency ratio CR ≤ 0.1, using the eigenvector method to calculate the weight value;

[0081] Calculate the maximum eigenvalue λ of the judgment matrix max , consistency index

[0082]

[0083] Random consistency index:

[0084]

[0085] , consistency ratio CR = CI / RI.

[0086] When CR≤0.1, the eigenvector method is used to calculate the weight value; if CR>0.1, the expert is returned to re-score or the historical data weight is corrected until CR≤0.1.

[0087] S203. When CR>0.1, recalculate the weight value by adjusting the judgment matrix element value until the consistency test requirement is met.

[0088] S3. Construct a comprehensive evaluation model that couples the analytic hierarchy process and fuzzy mathematics, and use the membership function to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate to generate the membership matrix of each evaluation index;

[0089] Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model;

[0090] The membership function in S3 adopts the S-curve function, and its expression is:

[0091]

[0092] Among them, k is the slope coefficient of the curve, c is the threshold inflection point, and the values ​​of k and c are determined by fitting the historical observation data of the degree of rock fracture development by the least squares method.

[0093] The multi-field coupled numerical model in S3 contains the following constraints:

[0094] The Mohr-Coulomb yield criterion is used for the analysis of the geostress field. The criterion parameters include the internal friction angle and cohesion c;

[0095] Internal friction angle φ: determined by rock formation type, 15°-35° for sandstone, 20°-40° for mudstone, and 25°-45° for shale;

[0096] Cohesion c: 0.1-2 MPa for soft rock, 2-10 MPa for hard rock, obtained through indoor rock mechanics tests.

[0097] The seepage field simulation is based on Darcy’s law, and the permeability parameters of Darcy’s law are dynamically updated through the permeability spatial distribution contour map in S1;

[0098] The calculation of material migration and diffusion uses the convection-diffusion equation, which includes the diffusion coefficient D and the convection velocity v. D and v are adjusted in real time according to the groundwater runoff rate in the hydrological parameters.

[0099] Darcy's law permeability K: Based on the permeability spatial distribution contour map generated by S1, the grid cell permeability is updated in real time through bilinear interpolation;

[0100] Convection-diffusion equation parameters: Diffusion coefficient D = 0.01-10m 2 / d (adjusted according to water quality composition), convection velocity v = groundwater runoff rate × rock porosity.

[0101] S4. Perform spatial overlay analysis on the 3D geological modeling data and the multi-parameter evaluation results to calculate the storage layer suitability index of the target area. The suitability index comprehensively reflects the storage capacity coefficient, sealing index, and long-term stability index;

[0102] The spatial overlay analysis in S4 uses the GIS raster data overlay analysis method, which includes:

[0103] Convert 3D geological modeling data into raster data layers with a resolution of no less than 5m×5m×10m;

[0104] Resample the multi-parameter evaluation results to ensure consistency with the spatial resolution of the raster data layer;

[0105] The weighted overlay operation generates a storage suitability index raster map, and the weighting coefficient is consistent with the weight value of the key parameter.

[0106] Integration of AR technology and GIS data

[0107] Coordinate system conversion

[0108] GIS data uses the WGS84 geographic coordinate system and is converted to the AR device's local coordinate system (ENU coordinate system, with the origin being the device's current location):

[0109] The longitude and latitude (θ, L) are converted to Cartesian coordinates (X, Y, Z), and then spatial alignment is achieved through the rotation matrix R(θ, L) and the translation vector T (device position).

[0110] Real scene annotation algorithm

[0111] Based on ORB feature point matching, feature points of the mine real-life image and GIS 3D model are extracted, and the camera pose is estimated using the RANSAC algorithm.

[0112] The implicit surface reconstruction technology is used to merge the three-dimensional model of the storage layer with the actual mine cloud, and the marking color is graded according to the fitness index (dark blue ≥ level IV).

[0113] S5. Generate a storage suitability zoning map with graded coloring based on the suitability index, where the dark blue area represents the preferred target area with storage suitability grade ≥ IV;

[0114] Output includes an assessment report of the storage layer's spatial location coordinates, estimated storage capacity, and risk warning indicators. The risk warning indicators include predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information.

[0115] An interactive three-dimensional site selection decision interface is generated through a virtual reality engine, which displays the optimized layout plan of injection well locations in real time and marks high-risk areas.

[0116] The risk warning indicators in S5 also include the following parameters:

[0117] The predicted value of fault activation probability is calculated using a fault activity classification model based on support vector machine (SVM). The model input parameters include ground stress gradient, rock fracture toughness and historical earthquake activity data.

[0118] The CO2 leakage rate simulation value is numerically simulated using COMSOL multi-physics field coupling simulation software, combined with the permeability of the top and bottom plates of the storage layer and the storage pressure gradient.

[0119] A mine water deep storage layer site selection and evaluation system based on multi-parameter coupling, characterized by including: a data acquisition and preprocessing module, a parameter processing and weight distribution module, a multi-parameter coupling analysis module, a spatial analysis and evaluation module, and a visualization output module

[0120] Data acquisition and preprocessing module:

[0121] Integrate geological radar, borehole detectors and distributed fiber optic sensors to collect geological parameters, hydrological parameters and engineering parameters of the target area;

[0122] It includes a wavelet transform noise reduction algorithm unit and a Kriging interpolation module, which are used to reduce the noise of the ground stress data and generate the permeability spatial distribution contour map;

[0123] Parameter processing and weight distribution module:

[0124] A multi-level evaluation index system is constructed based on the hierarchical analysis method to determine the key parameter set and assign weight values. The weight assignment rules are as follows:

[0125] The weight of geological structure stability is 30%-40%;

[0126] The weight of hydrogeological closure is 25%-35%;

[0127] The weight of the ground stress distribution characteristics is 15%-25%;

[0128] The rock formation permeability threshold and storage capacity estimation parameters are weighted at 5%-10% each;

[0129] Multi-parameter coupling analysis module:

[0130] It includes a fuzzy comprehensive evaluation unit, which is used to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate through the membership function and generate a membership matrix;

[0131] Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model;

[0132] Spatial Analysis and Evaluation Module:

[0133] The three-dimensional geological modeling data is coupled with the multi-parameter evaluation results through spatial overlay analysis to calculate the storage layer suitability index, which includes the storage capacity coefficient, sealing index and long-term stability index.

[0134] Visual output module:

[0135] Generate a storage suitability zoning map with graded coloring features, where the dark blue area represents the preferred target area with storage suitability grade ≥ IV;

[0136] Output includes an assessment report of the storage layer's spatial location coordinates, estimated storage capacity, and risk warning indicators. The risk warning indicators include predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information.

[0137] An interactive three-dimensional site selection decision interface is generated through a virtual reality engine to display the optimized layout plan of injection well locations in real time, and is connected to the GIS spatial database to mark high-risk areas.

[0138] The data acquisition and preprocessing module also includes: a distributed fiber optic sensor array, which is configured to be arranged along the mine tunnel to monitor rock microseismic events and temperature field changes in real time; a geological radar data processing unit, which is used to invert the reflected wave data of the geological radar and generate a three-dimensional point cloud model of the rock stratification interface.

[0139] The multi-parameter coupling analysis module also includes:

[0140] The dynamic weight adjustment submodule is used to dynamically modify the weight values ​​of key parameters according to the real-time engineering parameters obtained in S1;

[0141] The sensitivity analysis submodule evaluates the sensitivity ranking of various parameters to the storage layer suitability index through Monte Carlo simulation.

[0142] The visual output module integrates the following functions:

[0143] The user interface supports inputting storage capacity thresholds, risk level thresholds, and economic cost parameters, and dynamically screening eligible storage layer areas;

[0144] By using AR technology to superimpose the three-dimensional site selection map and the actual mine scene, real-life marking and risk warnings of the storage layer location can be achieved.

Claims

1. A method for site selection and evaluation of deep mine water storage layers based on multi-parameter coupling, characterized in that: The following steps are involved: S1. Using geological exploration equipment, distributed fiber optic sensor arrays, and 3D seismic exploration technology, obtain geological parameters, hydrological parameters, and engineering parameters of the target area. The geological parameters include geological structural stability, geostress distribution characteristics, rock permeability threshold, and rock porosity; the hydrological parameters include groundwater runoff rate, water quality composition, and hydraulic gradient; and the engineering parameters include mine drainage volume, sealing layer pressure, and sealing layer burial depth. Performing wavelet transform noise reduction processing on the ground stress monitoring data in the geological parameters, and generating a permeability spatial distribution contour map by Kriging interpolation method; S2. Establish a multi-level evaluation index system based on the analytic hierarchy process to determine the key parameter set that affects the location of the storage layer. The key parameter set includes geological structural stability, hydrogeological sealing, ground stress distribution characteristics, rock layer permeability threshold, and storage capacity estimation parameters; Assigning preset weight values ​​to the key parameters, where: The weight of geological structure stability is 30%-40%; The weight of hydrogeological closure is 25%-35%; The weight of the ground stress distribution characteristics is 15%-25%; The rock formation permeability threshold and storage capacity estimation parameters are weighted at 5%-10% each; S3. Construct a comprehensive evaluation model that couples the analytic hierarchy process and fuzzy mathematics, and use the membership function to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate to generate the membership matrix of each evaluation index; Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model; S4. Performing spatial overlay analysis on the three-dimensional geological modeling data and the multi-parameter evaluation results to calculate the storage layer suitability index of the target area, wherein the suitability index comprehensively reflects the storage capacity coefficient, the sealing index, and the long-term stability index; S5. Generate a storage suitability zoning map with graded coloring features based on the fitness index, where dark blue areas represent preferred target areas with a storage suitability grade ≥ IV; Output an assessment report containing the spatial coordinates of the storage layer, estimated storage capacity, and risk warning indicators, including predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information; An interactive three-dimensional site selection decision interface is generated through a virtual reality engine, which displays the optimized layout plan of injection well locations in real time and marks high-risk areas.

2. A mine water deep storage layer site selection and evaluation system based on multi-parameter coupling, characterized in that: include: Data acquisition and preprocessing module, parameter processing and weight distribution module, multi-parameter coupling analysis module, spatial analysis and evaluation module and visualization output module Data acquisition and preprocessing module: Integrate geological radar, borehole detectors and distributed fiber optic sensors to collect geological parameters, hydrological parameters and engineering parameters of the target area; It includes a wavelet transform noise reduction algorithm unit and a Kriging interpolation module, which are used to reduce the noise of the ground stress data and generate the permeability spatial distribution contour map; Parameter processing and weight distribution module: A multi-level evaluation index system is constructed based on the hierarchical analysis method to determine the key parameter set and assign weight values. The weight assignment rule is as follows: The weight of geological structure stability is 30%-40%; The weight of hydrogeological closure is 25%-35%; The weight of the ground stress distribution characteristics is 15%-25%; The rock formation permeability threshold and storage capacity estimation parameters are weighted at 5%-10% each; Multi-parameter coupling analysis module: It includes a fuzzy comprehensive evaluation unit, which is used to perform fuzzy processing on the degree of rock fracture development and groundwater runoff rate through the membership function and generate a membership matrix; Integrate the finite element stress field analysis unit, Darcy's law seepage field simulation unit and material migration and diffusion calculation unit to form a multi-field coupling numerical model; Spatial Analysis and Evaluation Module: The three-dimensional geological modeling data is coupled with the multi-parameter evaluation results through the spatial overlay analysis method to calculate the storage layer suitability index, which includes the storage capacity coefficient, the sealing index and the long-term stability index; Visual output module: Generate a storage suitability zoning map with graded coloring features, where the dark blue area represents the preferred target area with storage suitability grade ≥ IV; Output an assessment report containing the spatial coordinates of the storage layer, estimated storage capacity, and risk warning indicators, including predicted fault activation probability, simulated CO2 leakage rate, and roof and floor displacement warning information; An interactive three-dimensional site selection decision interface is generated through a virtual reality engine to display the optimized layout plan of injection well locations in real time, and is connected to the GIS spatial database to mark high-risk areas.

3. The method for site selection and assessment of deep mine water storage layer based on multi-parameter coupling according to claim 1, characterized in that: The analytic hierarchy process for weight allocation in S2 includes the following sub-steps: S201, constructing a judgment matrix including the key parameters, wherein the judgment matrix is ​​generated by an expert scoring method or a historical data statistics method; S202, performing a consistency check on the judgment matrix, and when the consistency ratio CR ≤ 0.1, calculating the weight value using the eigenvector method; S203. When CR>0.1, recalculate the weight value by adjusting the judgment matrix element value until the consistency test requirement is met.

4. The method for site selection and assessment of deep mine water storage layer based on multi-parameter coupling according to claim 1, characterized in that: The membership function in S3 adopts an S-shaped curve function, and its expression is: Wherein, k is the slope coefficient of the curve, c is the threshold inflection point, and the values ​​of k and c are determined by fitting the historical observation data of the degree of development of rock fractures by the least squares method.

5. The method for site selection and assessment of deep mine water storage layer based on multi-parameter coupling according to claim 1, characterized in that: The multi-field coupling numerical model in S3 contains the following constraints: The Mohr-Coulomb yield criterion is used for the analysis of the in-situ stress field. The criterion parameters include the internal friction angle and cohesion c; The seepage field simulation is based on Darcy's law, and the permeability parameters of the Darcy's law are dynamically updated through the permeability spatial distribution contour map in S1; The calculation of material migration and diffusion adopts the convection-diffusion equation, which includes the diffusion coefficient D and the convection velocity v. The D and v are adjusted in real time according to the groundwater runoff rate in the hydrological parameters.

6. The method for site selection and assessment of deep mine water storage layer based on multi-parameter coupling according to claim 1, characterized in that: The risk warning indicators in S5 also include the following parameters: The predicted value of fault activation probability is calculated using a fault activity classification model based on a support vector machine (SVM). The input parameters of the model include ground stress gradient, rock fracture toughness and historical earthquake activity data. The CO2 leakage rate simulation value is numerically simulated using COMSOL multi-physics field coupling simulation software, combined with the permeability of the top and bottom plates of the storage layer and the storage pressure gradient.

7. The method for site selection and assessment of deep mine water storage layer based on multi-parameter coupling according to claim 1, characterized in that: The spatial overlay analysis in S4 adopts the GIS raster data overlay analysis method, which specifically includes: Converting the three-dimensional geological modeling data into a raster data layer with a resolution of no less than 5m×5m×10m; Resampling the multi-parameter evaluation results to ensure consistency with the spatial resolution of the raster data layer; The weighted overlay operation generates a storage suitability index grid map, and the weighted coefficient is consistent with the weight value of the key parameter.

8. The mine water deep storage layer site selection and evaluation system based on multi-parameter coupling according to claim 2 is characterized in that: The data acquisition and preprocessing module also includes: a distributed fiber optic sensor array, which is configured to be arranged along the mine tunnel to monitor rock microseismic events and temperature field changes in real time; a geological radar data processing unit, which is used to invert the reflected wave data of the geological radar to generate a three-dimensional point cloud model of the rock stratification interface.

9. The mine water deep storage layer site selection and evaluation system based on multi-parameter coupling according to claim 2 is characterized in that: The multi-parameter coupling analysis module also includes: A dynamic weight adjustment submodule, configured to dynamically modify the weight values ​​of the key parameters according to the real-time engineering parameters acquired in S1; The sensitivity analysis submodule evaluates the sensitivity ranking of various parameters to the storage layer suitability index through Monte Carlo simulation.

10. The mine water deep storage layer site selection and evaluation system based on multi-parameter coupling according to claim 2, characterized in that: The visual output module integrates the following functions: The user interface supports inputting storage capacity thresholds, risk level thresholds, and economic cost parameters, and dynamically screening eligible storage layer areas; The three-dimensional site selection map and the actual mine scene are superimposed through AR technology to achieve real-scene marking and risk warning of the storage layer location.

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