Geological mineral analysis system and method

Abstract

The invention discloses a geological mineral analysis system and method, and the method comprises the steps: obtaining the geological profile data of a target mining area, and constructing a three-dimensional geological model according to the geological profile data; detecting a permeability coefficient matrix and heavy metal adsorption characteristic parameters of soil of each soil layer in the three-dimensional geologic model, and dividing the soil of the target mining area into multiple layers of heterostructure soil layers; establishing a pollution migration model corresponding to each heterostructure soil layer; and solving the pollution migration model corresponding to each heterogeneous structure soil layer by using a finite difference method, iteratively calculating the heavy metal flux layer by layer, and predicting the heavy metal concentration spatial distribution of the underground water at the target depth according to the output flux of the bottommost soil layer in combination with the underground water flowing direction and the dilution effect. The prediction effect of heavy metal pollution migration is improved by dividing industrial and mining area soil into multiple layers of heterostructure soil layers.

Description

Technical Field

[0001] The present invention relates to the technical field of geological and mineral analysis, and particularly relates to a geological and mineral analysis system and method. Background Art

[0002] Soil heavy metal pollution in industrial and mining areas is a major environmental problem associated with activities such as mineral resource development, smelting, and waste residue stacking. Heavy metals enter the soil through dry and wet deposition, surface runoff, sewage irrigation, etc., and accumulate in the soil. Eventually, they threaten the safety of groundwater through infiltration and migration, and even harm human health through the food chain. Currently, the assessment and prediction technologies for heavy metal pollution risks in industrial and mining areas mainly rely on large-area field surveys, fixed-point observations, or setting up monitoring wells to take samples regularly. However, the input pathways of soil heavy metals are complex, and the spatial heterogeneity of soil physical and chemical properties is significant. Existing prediction models mostly assume that the soil is a homogeneous medium, ignoring the influence of heterogeneous structures such as fractures and preferential flow channels on the migration process, and underestimating the risk of rapid infiltration of pollutants. Summary of the Invention

[0003] The purpose of the present invention is to provide a geological and mineral analysis system and method, which can improve the prediction effect of heavy metal pollution migration by dividing the soil in industrial and mining areas into multi-layer heterogeneous structure soil layers.

[0004] To achieve the above purpose, the present application provides the following solutions: On the one hand, the present application provides a geological and mineral analysis method, including the following steps: S1. Obtain the geological profile data of the target mining area, and construct a three-dimensional geological model according to the geological profile data; S2. Detect the permeability coefficient matrix and heavy metal adsorption characteristic parameters of the soil in each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multi-layer heterogeneous structure soil layers; S3. Establish a pollution migration model corresponding to each multi-layer heterogeneous structure soil layer; S4. Use the finite difference method to solve the pollution migration models corresponding to each heterogeneous structure soil layer, and iteratively calculate the heavy metal flux layer by layer. According to the output flux of the bottommost soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of the heavy metal concentration in the groundwater at the target depth.

[0005] In some specific embodiments, the geological profile data includes the stratigraphic structure information in the vertical direction of the target mining area, including borehole core data, geological radar scanning data, resistivity imaging data, and soil parameters. The borehole core data includes the stratigraphic type, thickness, and porosity in the vertical distribution; the geological radar scanning data includes shallow geological structure data; the resistivity imaging data includes deep geological structure data; the soil parameters include soil pH value, organic matter content, and initial heavy metal concentration.

[0006] In some specific embodiments, the specific process of step S2 is as follows: S21. Clean and fuse the geological profile data, construct a three-dimensional geological model, calibrate the physical and chemical properties of different stratum types in the three-dimensional geological model, and mark heterogeneous geology. The physical and chemical properties include the permeability coefficient matrix and heavy metal adsorption characteristic parameters. S22. Preliminarily stratify the soil in the target mining area according to the stratum types in the three-dimensional geological model, and divide it into multiple main layers. S23. Further subdivide the main layers according to the physical and chemical properties and heterogeneous geology marks of each main layer to obtain multiple heterogeneous structure soil layers.

[0007] In some specific embodiments, the process of constructing a three-dimensional geological model is as follows: Determine the vertical soil layer sequence according to the borehole core data, and at the same time use resistivity imaging data to correct the deep soil layer, and vertically stratify the soil in the target mining area. Horizontally expand the borehole core data to the target mining area through Kriging interpolation to generate a continuous three-dimensional soil layer type distribution. Fuse the vertical distribution and the three-dimensional soil layer type distribution to obtain a three-dimensional geological model.

[0008] In some specific embodiments, the heterogeneous geology marks include preferential flow channel marks and high pollution risk area marks. The specific process is as follows: Locate the deep high-conductivity area in each soil layer according to the resistivity imaging data, and locate the shallow fracture position in each soil layer according to the ground penetrating radar data; fuse the deep high-conductivity area and the shallow fracture position, and mark this migration path as a preferential flow channel. Query the physical and chemical properties of each soil layer. When the physical and chemical properties exceed the set threshold, mark this soil layer as a high pollution risk area.

[0009] In some specific embodiments, the process of obtaining the permeability coefficient matrix is as follows: Use a double-ring infiltrometer to measure the vertical permeability coefficient of each soil layer, combine the pumping test to obtain the horizontal permeability coefficient, form an anisotropic permeability tensor according to the vertical permeability coefficient and the horizontal permeability coefficient, and construct the permeability coefficient matrix of each soil layer. If the current soil layer is a fracture zone, amplify and correct the permeability coefficient of the soil layer where the fracture zone is located, and mark the corresponding permeability coefficient matrix of this soil layer as a preferential flow channel.

[0010] In some specific embodiments, use batch adsorption experiments to measure the adsorption model parameters of each soil layer, establish a regression model between the adsorption model parameters and soil parameters through multiple regression analysis, calibrate the heavy metal adsorption capacity of each soil layer, and obtain the heavy metal adsorption characteristic parameters of each soil layer.

[0011] In some specific embodiments, the method for constructing the pollution migration model is as follows: According to Darcy's law, a convection model is constructed to describe the migration of pollutants with the overall flow of the fluid; According to Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion process of pollutants from a high-concentration area to a low-concentration area. According to the Freundlich model, an adsorption model is constructed to describe the process of pollutants being adsorbed and fixed by the soil; The convection model, the diffusion model and the adsorption model are coupled to obtain the pollution migration model.

[0012] In a second aspect, the present application provides a geological and mineral analysis system, including: One or more processors; A storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a geological and mineral analysis method as described in the first aspect.

[0013] The beneficial effects of the present invention are as follows: A three-dimensional geological model is constructed by combining ground penetrating radar and resistivity imaging data, the soil layer boundary is dynamically divided and the permeability coefficient matrix is corrected to accurately depict preferential migration channels such as fracture zones, and then the finite difference method is used to discretize the migration equation in space and time, and the heavy metal flux is calculated layer by layer through iteration, improving the accuracy and practicality of the prediction of the heavy metal infiltration risk in industrial and mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of a geological and mineral analysis method provided by an embodiment of the present invention; Figure 2 It is a flowchart for dividing a multi-layer heterogeneous soil structure provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0016] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and values set forth in these embodiments do not limit the scope of the present invention.

[0017] Meanwhile, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0018] In addition, for clarity and conciseness, descriptions of well-known structures, functions, and configurations may be omitted. Those of ordinary skill in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0019] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered as part of the authorization specification.

[0020] In all examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0021] Before introducing the specific embodiments of the present application, the concepts to be involved are described first: Embodiment 1 As Figure 1 shown, this embodiment provides a geological and mineral analysis method, which specifically includes the following steps: S1. Obtain the geological profile data of the target mining area and construct a three-dimensional geological model based on the geological profile data; The geological profile data includes the formation structure information in the vertical direction of the target mining area, and the formation structure information includes: Drilling core data, providing the formation types (such as fill, clay, bedrock) distributed vertically, thickness, porosity, and permeability coefficient; Ground penetrating radar scanning data, identifying shallow layer (0 - 30m) geological structure data (such as heterogeneous structures like fractures and karst caves); Resistivity imaging data: revealing deep layer (>30m) geological structure data (such as aquifer boundaries and pollution plume diffusion paths); The soil parameters include soil pH value, soil organic matter content (SOM), and initial heavy metal concentration.

[0022] For the convenience of subsequent model establishment, data cleaning and normalization operations are performed on the geological profile data, where data cleaning includes: Outlier removal: For example, extremely high permeability coefficient values caused by drilling disturbances in the borehole.

[0023] Missing value filling: For un-sampled areas, Kriging interpolation is used to estimate the parameters.

[0024] Spatial interpolation: Three-dimensional Kriging interpolation: Convert discrete borehole data (such as the thickness of the clay layer) into a continuous three-dimensional grid model.

[0025] Trend surface analysis: According to the terrain elevation data, correct the spatial distribution trend of the soil layer thickness.

[0026] Data normalization: Unit unification: For example, unify the permeability coefficient to m / s and the heavy metal concentration to mg / kg.

[0027] Coordinate system alignment: Convert the ground penetrating radar scan data, resistivity imaging data, and borehole data to the same geographic coordinate system (such as WGS84). S2. Detect the permeability coefficient matrix and heavy metal adsorption characteristic parameters of the soil in each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multi-layer heterogeneous structure soil layers; Such as Figure 2 As shown, the specific process of step S2 is as follows: S21. Clean and fuse the geological profile data, construct a three-dimensional geological model, and calibrate the physical and chemical properties and heterogeneous geological markers of different stratigraphic types in the three-dimensional geological model. The physical and chemical properties include the permeability coefficient matrix and heavy metal adsorption characteristic parameters; 1. The process of constructing a three-dimensional geological model is as follows: Step 1: Determine the vertical soil layer sequence according to the borehole core data, and at the same time use the resistivity imaging data to correct the deep soil layer, and vertically stratify the soil in the target mining area; Step 2: Horizontally expand the target mining area according to the borehole core data by Kriging interpolation to generate a continuous three-dimensional soil layer type distribution; combine the fracture positions scanned by the ground penetrating radar scan data and mark the high-permeability preferential flow channels in the model; Step 3: Integrate the vertical distribution and the three-dimensional soil layer type distribution to obtain a three-dimensional geological model.

[0028] 2. The heterogeneous geological markers include preferential flow channel markers and high-pollution-risk area markers. The specific process is as follows: Locate the deep high-conductivity areas in each soil layer according to the resistivity imaging data, and locate the shallow fracture positions in each soil layer according to the ground penetrating radar data; fuse the deep high-conductivity areas and the shallow fracture positions, and mark this migration path as a preferential flow channel; Query the physical and chemical properties of each soil layer. When the physical and chemical properties exceed the set threshold, mark this soil layer as a high-pollution-risk area.

[0029] Among them, the permeability coefficient matrix and heavy metal adsorption characteristic parameters provide a quantitative basis for heterogeneous marking. Set the marking rules based on the parameter values (such as permeability coefficient, adsorption capacity) (such as the vertical permeability coefficient Kv > 1×10 −4m / s is the high-permeability zone), Parameter outlier marking: Identify the abnormal area in the parameter distribution through statistical analysis (such as Z-score): High-permeability zone: The permeability coefficient is significantly higher than that of the surrounding units; Low-adsorption zone: The adsorption characteristic parameters are lower than the threshold (such as the adsorption parameter Kf < 1.0 L / kg).

[0030] Multi-parameter coupling: Combine permeability and adsorption capacity (such as high permeability + low adsorption) to mark the high-risk pollution area.

[0031] 3. The process of obtaining the permeability coefficient matrix is as follows: Use a double-ring infiltrometer to measure the vertical permeability coefficient of each soil layer ( Kv ), and combine pumping tests to obtain the horizontal permeability coefficient ( Kh ), and form an anisotropic permeability tensor according to the vertical and horizontal permeability coefficients to construct the permeability coefficient matrix of each soil layer; If the current soil layer is a fracture zone, amplify and correct the permeability coefficient of the soil layer where the fracture zone is located, and mark the corresponding permeability coefficient matrix of the soil layer as a preferential flow channel.

[0032] 4. Use batch adsorption experiments to measure the parameters of the adsorption model (such as the Freundlich model) of each soil layer, establish a regression model between the adsorption model parameters and soil parameters through multiple regression analysis, calibrate the adsorption capacity of each soil layer for heavy metals, and obtain the heavy metal adsorption characteristic parameters of each soil layer.

[0033] S22. Preliminarily stratify the soil of the target mining area according to the stratigraphic types in the three-dimensional geological model, and divide it into multiple main layers; S23. Further subdivide the main layers according to the physical and chemical properties and heterogeneous geological marks of each main layer to obtain multi-layer heterogeneous soil layers.

[0034] Specifically, when dividing: Parameter mutation criterion: When the difference in permeability coefficient between adjacent main layers exceeds the threshold, divide a new soil layer; Cluster analysis: Use the K-means algorithm to group the main layers with similar soil parameters (such as clay content, pH, etc.) into the same layer; Trend function fitting: Describe the spatial variation law of the soil layer thickness through polynomial or exponential functions.

[0035] Treatment of abnormal geological structures: Separate the fracture zone or fault area into layers and assign a high permeability coefficient to characterize the preferential flow.

[0036] S3. Establish a pollution migration model corresponding to each layer of heterogeneous soil layer; The method for constructing the pollution migration model is: According to Darcy's law, a convection model is constructed to describe the migration of pollutants with the overall flow of the fluid; According to Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion process of pollutants from high-concentration areas to low-concentration areas According to the Freundlich model, an adsorption model is constructed to describe the process of pollutants being adsorbed and fixed by soil; the adsorption model adopts an improved Freundlich model, and the adsorption capacity is jointly calibrated by the soil organic matter content and pH value; the expression of the improved Freundlich model is: In the formula, q is the adsorption amount (mg / kg), Kf is the adsorption intensity coefficient, Ce is the liquid-phase equilibrium concentration (mg / L), n is the nonlinear index, α is the organic matter correction factor, SOM is the soil organic matter content (%), where Kf and α are determined by fitting batch adsorption experiments.

[0037] The convection model, diffusion model and adsorption model are coupled to obtain a pollution migration model.

[0038] S4. Use the finite difference method to solve the pollution migration model corresponding to each heterogeneous soil layer, and iteratively calculate the heavy metal flux layer by layer. According to the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of heavy metal concentration in groundwater at the target depth.

[0039] When predicting, 1. First, conduct spatio-temporal grid division on the target mining area: Divide the vertical depth of each soil layer into several layers (such as 0.5 meters per layer) to form a spatial grid.

[0040] Divide the prediction time period (such as 10 years) into time steps (such as one step per month) to form a time grid. Assign independent parameters to the soil of each heterogeneous soil layer: permeability coefficient, porosity, adsorption parameters, etc.

[0041] 2. Discretize the migration equation The output of the convection model is used as the convection term of the migration model. Process the convection term: Use the "upwind scheme" to discretize the influence of the water flow direction to avoid numerical oscillation. For example, when the water flow is downward, the concentration of the current layer is affected by the upper layer; Process the diffusion term: Use the central difference method to approximate the concentration gradient and calculate the diffusion flux between adjacent spatial grids.

[0042] Process the adsorption term: According to the adsorption model (such as the Freundlich equation), dynamically associate the solid-phase adsorption amount with the liquid-phase concentration, and update the adsorption amount at each time step.

[0043] 3. Layer-by-layer iterative calculation process Step 1. Set initial conditions: the liquid phase concentration of each layer (such as the initial concentration of the surface pollution source) and the solid phase adsorption amount are both zero.

[0044] Step 2. Time step loop: calculate layer by layer for each time step (such as the 1st month, the 2nd month...): 2.1 Top layer calculation: update the heavy metal flux at the top layer boundary according to rainfall or surface input. Calculate the convection-diffusion-adsorption process of the top layer to obtain the concentration change of this layer.

[0045] 2.2 Pass downward layer by layer: use the output flux of the upper layer (such as the lower boundary flux of the fill layer) as the input flux of the lower layer (such as the upper boundary of the clay layer). Calculate the concentration distribution of each layer in turn, considering the permeability coefficient and adsorption capacity of this layer.

[0046] 2.3 Adsorption amount update: update the solid phase adsorption amount through the adsorption model according to the liquid phase concentration of the current time step.

[0047] Step 3. Flux output: record the heavy metal flux at the lower boundary of each layer as the input basis for groundwater pollution.

[0048] 4. Predict the spatial distribution of heavy metal concentrations in groundwater at the target depth 4.1. Determine the pollution source input: obtain the heavy metal output flux of the bottom layer of soil, that is, the mass of heavy metals entering the groundwater layer per unit time and per unit area. This flux reflects the release intensity of the pollution source over time 4.2. Construct a groundwater flow field model Based on geological exploration data (such as permeability coefficient, aquifer thickness, hydraulic gradient), establish a groundwater flow model to determine the water flow direction and velocity at the target depth. For example, highly permeable sand layers may form fast-flowing channels, while clay layers restrict water flow.

[0049] 4.3. Simulate the pollutant migration path Use the heavy metal flux as the initial pollution source, and combine the groundwater flow velocity and direction to simulate the migration process of pollutants in groundwater: Convection-dominated migration: pollutants diffuse along the water flow direction, forming a pollution plume along the water flow path.

[0050] Dispersion and diffusion effects: pollutants diffuse to the surrounding low-concentration areas due to the concentration gradient, forming a concentration gradient distribution.

[0051] Adsorption correction: if the aquifer medium (such as clay or organic matter) has an adsorption effect on heavy metals, the model needs to be adjusted to reflect the concentration attenuation caused by the solid-liquid distribution.

[0052] 4.4 Quantify the dilution effect Calculate the diluted concentration based on the groundwater flow rate (flow velocity × cross-sectional area) and the pollutant input rate: High-flow areas: A large amount of clean water is mixed, significantly reducing the pollutant concentration.

[0053] Low-flow areas: The dilution capacity is limited, which may lead to local concentration accumulation.

[0054] For example, if the groundwater flow rate in a certain area is 1000 m³ / day and the heavy metal input rate is 10 kg / day, the theoretical dilution concentration is 10 mg / L (which needs to be corrected by combining actual adsorption and diffusion).

[0055] 4.5 Dynamic time stepping and spatial interpolation Time iteration: Gradually update the pollutant migration status hour by hour, day by day, or month by month to simulate the long-term cumulative effect.

[0056] Spatial interpolation: Generate a continuous spatial concentration distribution map from the discrete model outputs (such as monitoring point data) through inverse distance weighting (IDW) or Kriging interpolation method.

[0057] S5. Quantify the parameter uncertainty based on Monte Carlo simulation, generate a pollution risk probability cloud map, and visualize the high-risk migration paths through a three-dimensional geographic information system.

[0058] Based on the migration model in S4, calculate the spatial distribution of heavy metal concentrations in the groundwater at the target depth (deterministic prediction). These data (such as concentration gradients, diffusion ranges) are directly used as the benchmark scenarios for the Monte Carlo simulation in S5. Moreover, the key parameters in the migration model (such as permeability coefficient, adsorption intensity coefficient, hydrodynamic parameters) are assigned probability distributions (such as normal distribution, triangular distribution). The Monte Carlo simulation generates multiple sets of possible concentration distribution results by randomly sampling these parameters. The combination of Monte Carlo simulation and three-dimensional geography information realizes the visualization of the risk probability cloud map and migration paths for the first time, providing an intuitive basis for prevention and control decisions.

[0059] Example 2 This example provides a geological and mineral analysis system, including: One or more processors; A storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a geological and mineral analysis method in Example 1.

[0060] As mentioned above, these are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Based on the technical essence of the present invention, any simple modifications, equivalent replacements, and improvements made to the above embodiments within the spirit and principles of the present invention still fall within the protection scope of the technical solutions of the present invention.

Claims

1. A geological and mineral analysis method, characterized in that, It includes the following steps: S1. Obtain the geological profile data of the target mining area and construct a three-dimensional geological model based on the geological profile data; S2. Detect the permeability coefficient matrix and heavy metal adsorption characteristic parameters of the soil in each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multiple heterogeneous structure soil layers; S3. Establish a pollution migration model corresponding to each heterogeneous structure soil layer; S4. Use the finite difference method to solve the pollution migration model corresponding to each heterogeneous structure soil layer, and iteratively calculate the heavy metal flux layer by layer. According to the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of heavy metal concentration in the groundwater at the target depth.

2. The geological and mineral analysis method according to claim 1, characterized in that, The geological profile data includes the stratigraphic structure information in the vertical direction of the target mining area, including borehole core data, geological radar scanning data, resistivity imaging data, and soil parameters. The borehole core data includes the stratigraphic type, thickness, and porosity in the vertical distribution; the geological radar scanning data includes the shallow geological structure data; the resistivity imaging data includes the deep geological structure data; the soil parameters include the soil pH value, organic matter content, and initial heavy metal concentration.

3. The geological and mineral analysis method according to claim 2, wherein The specific process of step S2 is as follows: S21. Clean and fuse the geological profile data, construct a three-dimensional geological model, and calibrate the physical and chemical properties and heterogeneous geological marks of different stratigraphic types in the three-dimensional geological model. The physical and chemical properties include the permeability coefficient matrix and heavy metal adsorption characteristic parameters; S22. Preliminarily layer the soil in the target mining area according to the stratigraphic type in the three-dimensional geological model, and divide it into multiple main layers; S23. Further subdivide the main layers according to the physical and chemical properties and heterogeneous geological marks of each main layer to obtain multiple heterogeneous structure soil layers.

4. A geological and mineral analysis method according to claim 3, characterized in that, The process of constructing the three-dimensional geological model is as follows: Determine the vertical soil layer sequence according to the borehole core data, and at the same time use the resistivity imaging data to correct the deep soil layer, and vertically layer the soil in the target mining area; Horizontally expand the borehole core data to the target mining area through Kriging interpolation to generate a continuous three-dimensional soil layer type distribution; Fuse the vertical distribution and the three-dimensional soil layer type distribution to obtain a three-dimensional geological model.

5. A geological and mineral analysis method according to claim 3, characterized in that, The heterogeneous geological marks include preferential flow channel marks and high pollution risk area marks. The specific process is as follows: Locate the deep high-conductivity area in each soil layer according to the resistivity imaging data, and locate the shallow fracture position in each soil layer according to the geological radar data; fuse the deep high-conductivity area and the shallow fracture position, and mark this migration path as a preferential flow channel; Query the physical and chemical properties of each soil layer. When the physical and chemical properties exceed the set threshold, mark this soil layer as a high pollution risk area.

6. The geological and mineral analysis method according to claim 3, characterized in that, The process of obtaining the permeability coefficient matrix is as follows: Use a double-ring infiltrometer to measure the vertical permeability coefficient of each soil layer, combine the pumping test to obtain the horizontal permeability coefficient, form an anisotropic permeability tensor according to the vertical permeability coefficient and the horizontal permeability coefficient, and construct the permeability coefficient matrix of each soil layer; If the current soil layer is a fracture area, amplify and correct the permeability coefficient of the soil layer where the fracture area is located, and mark the corresponding permeability coefficient matrix of this soil layer as a preferential flow channel.

7. A geological and mineral analysis method according to claim 3, characterized in that, The adsorption model parameters of each soil layer are determined by batch adsorption experiments, and a regression model between the adsorption model parameters and soil parameters is established through multiple regression analysis to calibrate the adsorption capacity of each soil layer for heavy metals, and the heavy metal adsorption characteristic parameters of each soil layer are obtained.

8. The geological and mineral analysis method according to claim 3, characterized in that The method for constructing the pollution migration model is as follows: According to Darcy's law, a convection model is constructed to describe the migration of pollutants with the overall flow of the fluid. According to Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion process of pollutants from high-concentration areas to low-concentration areas. According to the Freundlich model, an adsorption model is constructed to describe the process of pollutants being adsorbed and fixed by the soil. The convection model, diffusion model and adsorption model are coupled to obtain the pollution migration model.

9. A geological and mineral analysis system, characterized in that, Including: One or more processors; A storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a geological and mineral analysis method as described in any one of claims 1-8.

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