Coal mine underground water flow-water quality coupling simulation method under mining disturbance condition

Abstract

The invention relates to the technical field of coal mine hydrogeology and environmental engineering, and discloses a coal mine groundwater flow-water quality coupling simulation method under mining disturbance condition, which comprises the following steps: step 1, mining disturbance area hydrogeology basic data acquisition: field investigation, drilling sampling, field test and long-term monitoring are carried out to obtain mining disturbance area hydrogeology basic data; the method comprises the following steps: acquiring landform, stratum lithology, geological structure, initial hydraulic gradient, porosity, initial permeability, initial pollutant concentration and hydrogeochemical parameters of a research area, and collecting a coal mining scheme, rock stratum movement observation data and fracture development characteristic parameters; according to the method, the dynamic hydrogeological parameter model is constructed, the water flow-water quality coupling control equation is established, numerical solution and accurate calibration are combined, and accurate simulation of the underground water flow field and the water quality migration process under the mining disturbance condition can be achieved.

Description

Technical Field

[0001] This invention relates to the field of coal mine hydrogeology and environmental engineering technology, and in particular to a method for simulating groundwater flow-water quality coupling under mining disturbance conditions. Background Technology

[0002] During coal mining, the rock strata deform and fracture due to mining stress, forming numerous mining-induced fissures. This leads to drastic changes in the hydrogeological conditions of the mining area. On the one hand, the development of mining-induced fissures alters the infiltration path and recharge-discharge relationship of groundwater, causing significant evolution of the groundwater flow field and increasing the risk of groundwater hazards. On the other hand, pollutants (such as heavy metals, sulfates, and nitrates) in the goaf, coal seam, and surrounding rock are transported and diffused by the water flow, polluting the groundwater environment of the mining area and its surroundings. Therefore, accurately simulating the groundwater flow field and water quality transport process under mining disturbance conditions is of great significance for ensuring safe coal mine production and protecting the water resources environment of the mining area.

[0003] Currently, there are many methods for simulating groundwater flow and quality in coal mines, both domestically and internationally. However, most of them have the following shortcomings: First, traditional simulation methods usually treat hydrogeological parameters (such as permeability and porosity) as constant values, ignoring the dynamic changes in parameters caused by mining disturbances, resulting in significant deviations between simulation results and actual conditions. Second, some coupled simulation methods simply superimpose the simulation results of the flow field and the water quality field without fully considering the dynamic coupling relationship between the two, failing to accurately reflect the impact of mining disturbances on the co-evolution of flow and water quality. Third, model parameter calibration often uses single monitoring data, resulting in low calibration accuracy, which is difficult to meet the needs of actual engineering. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides a method for simulating groundwater flow-water quality coupling under mining disturbance conditions in coal mines.

[0005] This invention is achieved using the following technical solution: a method for simulating groundwater flow-water quality coupling in coal mines under mining disturbance conditions, comprising the following steps: Step 1: Collection of basic hydrogeological data in the mining disturbance area, as detailed below: Through field surveys, borehole sampling, field tests and long-term monitoring, the study area was obtained as a whole, including topography, stratigraphy, geological structure, initial hydraulic gradient, porosity, initial permeability, initial concentration of pollutants and hydrogeochemical parameters. At the same time, coal mining schemes, rock strata movement observation data and fracture development characteristic parameters were collected. Step 2: Construct a dynamic evolution model of hydrogeological parameters caused by mining disturbance, as detailed below: Based on the changes in rock strata deformation, fracture aperture and connectivity caused by mining disturbance, a dynamic evolution function of key hydrogeological parameters such as permeability and porosity is established with the mining advance distance, goaf area and mining depth. The dynamic evolution function uses the mining disturbance coefficient as the core parameter to quantify the degree of influence of mining activities on hydrogeological parameters. Step 3: Establish the groundwater flow-water quality coupling control equations under mining disturbance conditions, as follows: Based on Darcy's law and the law of conservation of mass, and combined with the dynamic evolution model described in step two, the groundwater flow control equation under mining disturbance conditions is established. Step 4: Based on the convection-dispersion equation and the law of conservation of solute mass, considering the convection, dispersion, adsorption-desorption and chemical reaction processes of pollutants, and combined with the dynamic changes of the water flow field, establish the groundwater quality transport control equation. Step 5: Construct a groundwater flow-water quality coupled control equation system by leveraging the parameter coupling relationship between the flow field and the water quality field; Step Six: Numerical discretization and solution of the coupled control equations, as detailed below: The coupled control equations established in step three were discretized spatially and temporally using the finite difference method to construct a discretized set of algebraic equations. The set of algebraic equations was solved iteratively by combining the boundary conditions and initial conditions of the study area to obtain the distribution of groundwater flow field and water quality concentration at different mining stages. Step 7: Model calibration and validation, as detailed below: Using the field monitoring data obtained in step one, the key parameters of dynamic parameters, dispersion coefficient, and adsorption coefficient in the model are calibrated using the least squares method. The simulation accuracy of the model is verified by the validation group data. If the relative error between the simulated value and the measured value is greater than 10%, return to step two to adjust the dynamic evolution model of hydrogeological parameters until the accuracy requirements are met. Step 8: Simulation results output and analysis, as detailed below: Output dynamic curves of groundwater level changes, vector maps of water flow velocity, contour maps of pollutant concentration, and pollutant transport paths under different mining conditions. Analyze the impact of mining disturbances on the evolution of groundwater flow field and the transport patterns of pollutants, providing decision support for the prevention and control of groundwater hazards in coal mines and the protection of water resources.

[0006] As a further improvement to the above scheme, the coefficient of mining disturbance in step two... Calculated using the following formula: In the formula, , , These are the weights of the goaf area, the mining depth, and the rock strata movement distance, respectively. , This represents the current area of ​​the goaf (m²). Let D be the total mineable area of ​​the study region (m²), and D be the current mining depth (m). Let L be the maximum mining depth (m) in the study area, and L be the distance the rock strata moved (m). The limit of the rock strata's movement distance (m).

[0007] As a further improvement to the above scheme, the dynamic evolution function of permeability (k) in step two is as follows: In the formula, The initial permeability is (m²). This is the mining disturbance coefficient. The stress sensitivity coefficient was determined through indoor rock stress-permeability tests. Additional stress (Pa) caused by mining.

[0008] As a further improvement to the above scheme, the governing equation for groundwater flow under the mining disturbance conditions in step three is as follows: In the formula, , , The values ​​represent the dynamic permeability (m²) in the x, y, and z directions, respectively. The groundwater head (m) is the groundwater level. The source and sink terms are (m³ / (m²·s)), with negative values ​​for extraction pumping and positive values ​​for recharge. The water storage ratio is 1 / m. For time (s).

[0009] As a further improvement to the above scheme, the groundwater transport control equation in step four is as follows: In the formula, For dynamic porosity, The concentration of pollutants is expressed in mg / L. , , The values ​​represent the groundwater flow velocities (m / s) in the x, y, and z directions, respectively. , , These are the longitudinal dispersion coefficients (m² / s) in the x, y, and z directions, respectively. , , , , , , respectively, represent the cross-diffusion coefficient (m² / s), and R represents the pollutant source / sink term and chemical reaction term (mg / (m³·s)). Pollutant release is positive, and degradation or adsorption is negative.

[0010] As a further improvement to the above scheme, the boundary conditions in step six include the following: (1). Water flow boundary: The natural groundwater level outside the study area is the constant head boundary, the pumping point in the mining area is the constant flow boundary, and the impermeable rock layer is the impermeable boundary; (2). Water quality boundary: The pollutant concentration at the inlet of the study area is a constant concentration boundary, the outlet is a zero gradient boundary, and the water-isolated boundary is a zero pollutant flux boundary.

[0011] As a further improvement to the above scheme, the evaluation indicators for model calibration in step seven include the root mean square error (RMSE) and the coefficient of determination (R²), and their calculation formulas are as follows: In the formula, To monitor the number of data points, For the first Simulated pollutant concentrations (mg / L) at each monitoring point. For the first Measured pollutant concentrations (mg / L) at each monitoring point. The average value (mg / L) of the measured pollutant concentration. The model calibration is qualified when the concentration is ≤0.5mg / L and R²≥0.85.

[0012] As a further improvement to the above scheme, the on-site monitoring data in step seven includes groundwater level monitoring data and pollutant concentration monitoring data.

[0013] As a further improvement to the above scheme, the topographic features of the area include elevation undulations, slope aspect and gradient, and the distribution of the strata lithology includes the contact interface between the loose layer and the bedrock, and variations in the thickness of the rock layers.

[0014] As a further improvement to the above scheme, the development of the geological structure includes fault occurrence, fracture distribution density and orientation, and the initial concentration of pollutants includes, but is not limited to, heavy metals, sulfates and COD.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention constructs a dynamic hydrogeological parameter model, establishes a flow-water quality coupled control equation, and combines numerical solution and precise calibration to achieve accurate simulation of the groundwater flow field and water quality transport process under mining disturbance conditions. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a schematic diagram of the groundwater level contour lines for the extraction process of this invention; Figure 3 This is a schematic diagram of the water flow velocity vector for the present invention. Figure 4 This is a schematic diagram comparing the drawdown at different mining stages of the present invention. Detailed Implementation

[0017] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0018] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0019] Example 1: Step 1: Collection of basic hydrogeological data in the mining-disturbed area. Basic data for the study area is obtained through field geological surveys, borehole sampling, laboratory tests, field pumping tests, and long-term monitoring. This includes: (1) Topographic and geomorphological data: Digital elevation model (DEM) of the study area with a resolution of not less than 30m; (2). Stratigraphic lithological data: Stratigraphic thickness, lithological composition, porosity, and initial permeability were determined by core sampling and laboratory testing, with at least 3 parallel test samples set for each stratum; (3) Geological structural data: the location, strike, dip angle, aperture and connectivity of faults and fractures; (4) Hydrogeological data: Initial groundwater level, hydraulic gradient, recharge and discharge volume are monitored long-term using water level monitoring wells at a frequency of once per day and a monitoring period of no less than 3 months. (5) Mining data: coal mining methods, mining intensity, area and distribution of goaf, mining depth, and advance speed; (6) Water quality data: pollutants (such as Fe³+, Mn²+, SO₄²⁻) 2- The initial concentration and background value of COD were determined by water sampling combined with laboratory testing, and the testing methods complied with the "Groundwater Quality Standard" (GB / T 14848-2017). (7) Mining disturbance data: rock strata movement distance, fracture development degree, and additional stress distribution are monitored using rock strata movement observation stations and stress sensors, with an observation frequency of once per week.

[0020] Step 2: Construct a dynamic evolution model of hydrogeological parameters considering mining disturbance. Mining disturbance leads to rock deformation, increased fracture aperture, and enhanced connectivity, which in turn causes dynamic changes in hydrogeological parameters such as permeability and porosity. This step constructs a dynamic evolution model of hydrogeological parameters based on the mining disturbance coefficient, as follows: (1). Calculate the mining disturbance coefficient Mining disturbance coefficient To quantify the degree of disturbance to rock strata caused by mining activities, the following formula is used, taking into account the influence of goaf area, mining depth, and rock strata movement distance: In the formula, , , The analytic hierarchy process (AHP) is used to determine, for example, in a certain mining area. =0.4, =0.3, =0.3, This represents the current area of ​​the goaf (m²). Let D be the total mineable area of ​​the study region (m²), and D be the current mining depth (m). Let L be the maximum mining depth (m) in the study area, and L be the distance the rock strata moved (m). The limit of rock strata movement (m) is determined through indoor rock mechanics tests.

[0021] (2). Construct a dynamic evolution function for permeability. Permeability is a key parameter affecting the groundwater flow field, and its dynamic changes are closely related to the mining disturbance coefficient and additional stress. The specific evolution function is as follows: In the formula, The initial permeability is (m²). The β value is a stress sensitivity coefficient, determined through indoor rock stress-permeability tests. For example, the β value for sandstone is typically 1.2 × 10⁻⁶. -B ~5.6×10 -B Pa -1 , The additional stress caused by mining was calculated using FLAC3D numerical simulation software.

[0022] (3). Construct a dynamic evolution function for porosity. The change in porosity is positively correlated with the deformation of the rock strata. Combined with the mining disturbance coefficient, the evolution function is: In the formula, Initial porosity, The porosity sensitivity coefficient was determined through indoor experiments. The rock strain is calculated from the distance the rock strata moved.

[0023] Step 3: Establish the groundwater flow-water quality coupled control equations under mining disturbance conditions. Based on Darcy's law, the law of conservation of mass, and convection-dispersion theory, and combined with the dynamic hydrogeological parameter model constructed in Step 2, establish the flow-water quality coupled control equation system as follows: (1). Groundwater flow control equations, considering seepage in the x, y, and z directions and source and sink terms, are as follows: In the formula, , , The dynamic permeability (m²) in the x, y, and z directions are respectively, and are calculated by the evolution function in step 2 (2). The groundwater head (m) is the groundwater level. For source and sink terms, extraction and pumping are negative, and groundwater recharge occurs during... The value is positive, and the supply value is positive. The water storage ratio is 1 / m. For time (s).

[0024] (2). Groundwater transport control equations, considering the convection, dispersion, adsorption-desorption and chemical reaction processes of pollutants, and combined with the dynamic changes of the water flow field, the groundwater transport control equations are as follows: In the formula, , , Calculated using Darcy's law, i.e. , The dynamic viscosity coefficient of groundwater (Pa). , , These are the longitudinal dispersion coefficients in the x, y, and z directions, respectively. , , , , , These are the cross-dispersion coefficients, determined through indoor dispersion tests. R represents the pollutant source / sink term and the chemical reaction term, with positive for pollutant release and negative for degradation or adsorption. ,in The dry density of the rock is (kg / m³). The distribution coefficient is (m³ / kg).

[0025] (3) Water flow-water quality coupling relationship: The water flow field provides the driving force for the water quality field. The change in water flow velocity directly affects the convection and dispersion process of pollutants. At the same time, the presence of pollutants in the water quality field may change the permeability of rocks (such as chemical precipitation blocking fissures), thereby affecting the water flow field. This invention couples the two by dynamic permeability and water flow velocity to realize the simulation of the co-evolution of water flow and water quality.

[0026] Step 4: Numerical discretization and solution of the coupled control equations. The coupled control equations are discretized using the finite difference method to construct a system of algebraic equations. The system is then solved using boundary and initial conditions, as detailed below: (1) Spatial discretization: The study area is divided by a structured grid. The grid size is determined according to the range of the study area and the accuracy requirements. It is usually 10m×10m×5m~50m×50m×20m. The goaf and fracture development area are densified by a finer grid to improve the simulation accuracy.

[0027] (2). Time discretization: The time term is discretized using an implicit difference scheme. The time step is determined based on the mining advance speed and the water level change rate. The initial time step is set to 1 day. If the water level change rate is greater than 0.5 m / d, the time step is reduced to 0.5 days to ensure numerical stability.

[0028] (3). Boundary conditions and initial conditions: The initial conditions include: the initial groundwater level is obtained by interpolation of monitoring data, the initial pollutant concentration is the background value, and the initial permeability and porosity are measured values.

[0029] (4). Numerical solution: The discrete algebraic equation system is solved by the SOR iterative method. The convergence criteria of the iteration are that the water level difference between two adjacent iterations is less than 0.01m, the pollutant concentration difference is less than 0.01mg / L, and the number of iterations does not exceed 1000.

[0030] Step 5: Model calibration and verification. The model parameters are calibrated and verified using on-site monitoring data to ensure simulation accuracy. (1) Parameter calibration: 70% of the monitoring data were selected as the calibration group. The least squares method was used to adjust the dynamic parameters (α, β, γ), diffusion coefficient and adsorption coefficient (K_d) so that the error between the simulated value and the measured value met the accuracy requirements (RMSE≤0.5mg / L and R²≥0.85).

[0031] (2). Model validation: Select the remaining 30% of the monitoring data as the validation group, substitute the calibrated parameters into the model, calculate the RMSE and R² of the validation group. If the accuracy requirements are met, the model validation is qualified; otherwise, return to step 2 to adjust the parameter evolution model and recalibrate.

[0032] Step 6: Simulation Results Output and Analysis. Output groundwater level dynamic change curves, water flow velocity vector maps, pollutant concentration value lines, and transport path maps for different mining stages (e.g., initial, middle, and late stages of mining). Analyze the impact of mining disturbances on the evolution of the water flow field and pollutant transport, specifically including: (1). Water flow field analysis: range of water level drawdown in the goaf, changes in groundwater recharge path, and distribution of fissure water conduction channels.

[0033] (2) Water quality field analysis: peak location of pollutant concentration, migration speed, and range of influence, and identification of key pollution risk areas.

[0034] (3). Coupling analysis: the correlation between water flow velocity change and pollutant transport rate, and the effect of mining disturbance on the enhancement or weakening of coupling effect.

[0035] Example 2 Step 1: Overview of the study area. A coal mine is located in xxx city, mining the No. 3 coal seam at a depth of 300-500m, using strike-bound longwall retreat mining, with a mining intensity of 1.2×10⁻⁶. 6 The area of ​​the goaf gradually increases with the progress of mining. The study area is 5km × 3km. The strata mainly include Quaternary loose layers (thickness 20~50m), Permian sandstone (thickness 100~150m), and Carboniferous coal seams (thickness 5~8m). The groundwater type is mainly fracture pore water, and the main pollutant is Fe. 3 + and SO4 2- .

[0036] Step 2: Basic data collection. Key data obtained through field surveys, borehole sampling, and long-term monitoring are as follows: (1) Initial permeability Sandstone is 2.5 × 10 -14 m², coal seam size 1.2×10 -13 m²; (2). Initial porosity Sandstone is 0.15, coal seam is 0.22; (3) Initial groundwater level: 950~980m, hydraulic gradient is 0.005; (4). Initial concentration of pollutants: Fe³⁺ is 0.8 mg / L, SO₄²⁻ is 0.8 mg / L. 2- It is 120 mg / L; (5). Mining parameters: The mining advance speed is 10m / cycle, the maximum mining depth is 500m, and the total mineable area is 12km²; (6) Monitoring data: 15 water level monitoring wells and 8 water quality monitoring points were set up, with a monitoring cycle of 6 months.

[0037] Step 3: Dynamic parameter model construction, as detailed below: (1) The mining disturbance coefficient was determined using the analytic hierarchy process. =0.4, =0.3, =0.3, the area of ​​the goaf in a certain mining stage is S=3.6km², the mining depth is D=420m, the rock strata movement distance is L=12m, and the ultimate movement distance is... =25m, substituting into the formula: (2). Dynamic evolution of permeability, stress sensitivity coefficient of sandstone β=3.2×10 -8 Pa -1 Additional stress =8×10 6 Pa, substituting into the formula: Step 4: Establish and solve the coupled control equations, as detailed below: (1). Equation establishment: Based on the dynamic permeability obtained in step 3, establish the water flow control equation and the water quality transport control equation, where the source and sink term Q = -500 m³ / (m²·s) (water pumping from the goaf), and the adsorption distribution coefficient... =0.02m³ / kg, dry density of rock =2650 kg / m³; (2). Numerical discretization and solution: The study area was divided into structured grids with a grid size of 20m×20m×10m. The grid in the goaf was fined to 10m×10m×5m. The time step was set to 1 day. Boundary conditions: the outer perimeter was a constant head boundary (960m), the goaf was a constant flow boundary (Q=-500m³ / (m²·s)), and the impermeable rock layer was an impermeable boundary. Water quality boundary: constant concentration boundary at the inlet (Fe³+ 0.8 mg / L, SO₄²⁻). 2- (120 mg / L), with the outlet at a zero gradient boundary, the SOR iterative method was used to solve the problem. The convergence criteria for the iteration were that the water level difference was less than 0.01 m and the concentration difference was less than 0.01 mg / L.

[0038] Step 5: Model Calibration and Validation: Monitoring data from the first four months were selected as the calibration group, and data from the last two months were selected as the validation group. After calibration, the simulated RMSE for Fe³⁺ was 0.32 mg / L, and R² was 0.89; SO₄²⁻ was 0.32 mg / L. 2- The simulated RMSE was 4.8 mg / L and R² was 0.91, which met the accuracy requirements, and the model was successfully validated.

[0039] Step 6: Simulation Result Analysis and Display, as detailed below: (1) Water flow field: A significant water level drawdown cone is formed in the goaf. The area of ​​the drawdown cone gradually expands as mining progresses. The water level drawdown reaches its maximum (35m) in the middle of mining. The fractured area becomes the main water-conducting channel. (2). Water quality field: Fe³⁺ and SO₄²⁻ 2- The pollutants migrate along the direction of water flow, and the disturbance caused by mining increases the diffusion range of pollutants. In the later stage of mining, the maximum concentration of Fe³+ reached 3.2 mg / L, which exceeds the Class III standard (≤0.3 mg / L) of the Groundwater Quality Standard (GB / T 14848-2017). It mainly affects the groundwater within 500m around the mining area. (3) Coupling effect: Mining disturbance leads to increased permeability and faster water flow, which in turn promotes the convective transport of pollutants, and the peak time of pollutant concentration appears about 15 days earlier.

[0040] The groundwater flow-water quality coupling simulation method proposed in this invention under mining disturbance conditions can accurately reflect the impact of mining disturbance on the flow field and water quality field, and can provide a scientific basis for the prevention and control of groundwater hazards and the treatment of groundwater pollution in coal mines. It is recommended to set up anti-seepage curtains around the goaf area to reduce the spread of pollutants.

[0041] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A method for simulating groundwater flow-water quality coupling under mining disturbance conditions in coal mines, characterized in that, Includes the following steps: Step 1: Collection of basic hydrogeological data in the mining disturbance area, as detailed below: Through field surveys, borehole sampling, field tests and long-term monitoring, the study area was obtained as a whole, including topography, stratigraphy, geological structure, initial hydraulic gradient, porosity, initial permeability, initial concentration of pollutants and hydrogeochemical parameters. At the same time, coal mining schemes, rock strata movement observation data and fracture development characteristic parameters were collected. Step 2: Construct a dynamic evolution model of hydrogeological parameters caused by mining disturbance, as detailed below: Based on the changes in rock strata deformation, fracture aperture and connectivity caused by mining disturbance, a dynamic evolution function of key hydrogeological parameters such as permeability and porosity is established with the mining advance distance, goaf area and mining depth. The dynamic evolution function uses the mining disturbance coefficient as the core parameter to quantify the degree of influence of mining activities on hydrogeological parameters. Step 3: Establish the groundwater flow-water quality coupling control equations under mining disturbance conditions, as follows: Based on Darcy's law and the law of conservation of mass, and combined with the dynamic evolution model described in step two, the groundwater flow control equation under mining disturbance conditions is established. Step 4: Based on the convection-dispersion equation and the law of conservation of solute mass, considering the convection, dispersion, adsorption-desorption and chemical reaction processes of pollutants, and combined with the dynamic changes of the water flow field, establish the groundwater quality transport control equation. Step 5: Construct a groundwater flow-water quality coupled control equation system by leveraging the parameter coupling relationship between the flow field and the water quality field; Step Six: Numerical discretization and solution of the coupled control equations, as detailed below: The coupled control equations established in step three were discretized spatially and temporally using the finite difference method to construct a discretized set of algebraic equations. The set of algebraic equations was solved iteratively by combining the boundary conditions and initial conditions of the study area to obtain the distribution of groundwater flow field and water quality concentration at different mining stages. Step 7: Model calibration and validation, as detailed below: Using the field monitoring data obtained in step one, the key parameters of dynamic parameters, dispersion coefficient, and adsorption coefficient in the model are calibrated using the least squares method. The simulation accuracy of the model is verified by the validation group data. If the relative error between the simulated value and the measured value is greater than 10%, return to step two to adjust the dynamic evolution model of hydrogeological parameters until the accuracy requirements are met. Step 8: Simulation results output and analysis, as detailed below: Output dynamic curves of groundwater level changes, vector maps of water flow velocity, contour maps of pollutant concentration, and pollutant transport paths under different mining conditions. Analyze the impact of mining disturbances on the evolution of groundwater flow field and the transport patterns of pollutants, providing decision support for the prevention and control of groundwater hazards in coal mines and the protection of water resources.

2. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The coefficient of mining disturbance in step two Calculated using the following formula: In the formula, , , These are the weights of the goaf area, the mining depth, and the rock strata movement distance, respectively. , This represents the current area of ​​the goaf (m²). Let D be the total mineable area of ​​the study region (m²), and D be the current mining depth (m). Let L be the maximum mining depth (m) in the study area, and L be the distance the rock strata moved (m). The limit of the rock strata's movement distance (m).

3. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The dynamic evolution function of permeability (k) in step two is as follows: In the formula, The initial permeability is (m²). This is the mining disturbance coefficient. The stress sensitivity coefficient was determined through indoor rock stress-permeability tests. Additional stress (Pa) caused by mining.

4. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The governing equations for groundwater flow under the mining disturbance conditions in step three are as follows: In the formula, , , The values ​​represent the dynamic permeability (m²) in the x, y, and z directions, respectively. The groundwater head (m) is the groundwater level. The source and sink terms are (m³ / (m²·s)), with negative values ​​for extraction pumping and positive values ​​for recharge. The water storage ratio is 1 / m. For time (s).

5. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The groundwater transport control equation in step four is as follows: In the formula, For dynamic porosity, The concentration of pollutants is expressed in mg / L. , , The values ​​represent the groundwater flow velocities (m / s) in the x, y, and z directions, respectively. , , These are the longitudinal dispersion coefficients (m² / s) in the x, y, and z directions, respectively. , , , , , , respectively, represent the cross-diffusion coefficient (m² / s), and R represents the pollutant source / sink term and chemical reaction term (mg / (m³·s)). Pollutant release is positive, and degradation or adsorption is negative.

6. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The boundary conditions in step six include the following: (1). Water flow boundary: The natural groundwater level outside the study area is the constant head boundary, the pumping point in the mining area is the constant flow boundary, and the impermeable rock layer is the impermeable boundary; (2). Water quality boundary: The pollutant concentration at the inlet of the study area is a constant concentration boundary, the outlet is a zero gradient boundary, and the water-isolated boundary is a zero pollutant flux boundary.

7. The coal mine groundwater flow-water quality coupling simulation method under mining disturbance conditions as described in claim 1, characterized in that, The evaluation metrics for model calibration in step seven include the root mean square error (RMSE) and the coefficient of determination (R²), which are calculated using the following formulas: In the formula, To monitor the number of data points, For the first Simulated pollutant concentrations (mg / L) at each monitoring point. For the first Measured pollutant concentrations (mg / L) at each monitoring point. The average value (mg / L) of the measured pollutant concentration. The model calibration is qualified when the concentration is ≤0.5mg / L and R²≥0.

85.

8. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The on-site monitoring data in step seven includes groundwater level monitoring data and pollutant concentration monitoring data.

9. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The topographic features of the area include elevation undulations and slope aspect and gradient, and the distribution of the strata and lithology includes the contact interface between the loose layer and the bedrock and variations in the thickness of the rock layers.

10. The method for coupled simulation of groundwater flow and water quality in coal mines under mining disturbance conditions as described in claim 1, characterized in that, The development of the geological structure includes fault orientation, fracture distribution density and orientation, and the initial concentration of pollutants includes, but is not limited to, heavy metals, sulfates and COD.

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