A geological mineral analysis system and method

Abstract

The present invention discloses a geological and mineral analysis system and method. The system obtains geological profile data of a target mining area and constructs a three-dimensional geological model based on the data. The system then detects the permeability matrix and heavy metal adsorption characteristic parameters of each soil layer in the three-dimensional geological model, dividing the target mining area soil into multiple layers of heterogeneous soil structures. A pollution migration model corresponding to each heterogeneous soil layer is established. The pollution migration model corresponding to each heterogeneous soil layer is solved using the finite difference method, and heavy metal flux is iteratively calculated layer by layer. Based on the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, the spatial distribution of heavy metal concentrations in groundwater at the target depth is predicted. By dividing the soil in the mining area into multiple layers of heterogeneous soil structures, the prediction of heavy metal pollution migration is improved.

Description

Technical Field

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

[0002] Heavy metal pollution in the soil of industrial and mining areas is a major environmental problem caused by activities such as mineral resource development, smelting, and waste dumping. Heavy metals enter the soil through dry and wet deposition, surface runoff, sewage irrigation, etc., and are enriched in the soil. They eventually threaten groundwater safety through infiltration and migration, and even endanger human health through the food chain. At present, the assessment and prediction technology for heavy metal pollution risks in industrial and mining areas mainly relies on large-scale field surveys, fixed-point observations, or regular sampling of monitoring wells. However, the input pathways of heavy metals in the soil are complex, and the heterogeneity of the physical and chemical properties of the soil layer is significant. Existing prediction models mostly assume that the soil is a homogeneous medium, ignore the impact of heterogeneous structures such as cracks and preferential flow channels on the migration process, and underestimate the risk of rapid infiltration of pollutants. Summary of the Invention

[0003] The purpose of the present invention is to provide a geological 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 multiple layers of heterogeneous structure soil.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In one aspect, the present application provides a method for geological mineral analysis, comprising the following steps:

[0006] S1. Obtain geological profile data of the target mining area and construct a three-dimensional geological model based on the geological profile data;

[0007] S2. Detect the permeability matrix and heavy metal adsorption characteristic parameters of each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multiple layers of heterogeneous soil structures;

[0008] S3. Establish a pollution migration model corresponding to each heterogeneous soil layer;

[0009] 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. Based on the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of heavy metal concentrations in groundwater at the target depth.

[0010] In some specific embodiments, the geological profile data includes stratigraphic structure information in the vertical direction of the target mining area, including drill core data, geological radar scanning data, resistivity imaging data and soil parameters. The drill core data includes vertically distributed stratigraphic type, thickness, and porosity; the geological radar scanning data includes shallow geological structure data; the resistivity imaging data includes deep geological structure data; and the soil parameters include soil pH value, organic matter content, and initial heavy metal concentration.

[0011] In some specific implementations, the specific process of step S2 is:

[0012] S21. Clean and fuse geological profile data, construct a three-dimensional geological model, and calibrate the physical and chemical properties of different strata types in the three-dimensional geological model and perform heterogeneous geological labeling. The physical and chemical properties include permeability matrix and heavy metal adsorption characteristic parameters;

[0013] S22. Preliminary stratification of the target mining area soil according to the stratum type in the three-dimensional geological model to divide it into multiple main layers;

[0014] S23. Based on the physicochemical characteristics and heterogeneous geological markers of each main layer, the main layer is further subdivided to obtain multiple layers of heterogeneous structural soil layers.

[0015] In some specific implementations, the process of constructing a three-dimensional geological model is as follows:

[0016] Determine the vertical soil layer sequence based on the drill core data, and use the resistivity imaging data to correct the deep soil layer, and vertically stratify the soil in the target mining area;

[0017] The drill core data is laterally extended to the target mining area through Kriging interpolation to generate a continuous three-dimensional distribution of soil layer types;

[0018] The vertical distribution and the three-dimensional soil layer type distribution are integrated to obtain a three-dimensional geological model.

[0019] In some specific embodiments, the heterogeneous geological markers include preferential flow channel markers and high-risk pollution area markers, and the specific process is as follows:

[0020] Deep high-conductivity areas are located in each soil layer based on resistivity imaging data, and shallow fractures are located in each soil layer based on geological radar data. The deep high-conductivity areas and shallow fractures are merged to mark the migration path as a preferential flow channel.

[0021] The physical and chemical properties of each soil layer are queried. When the physical and chemical properties exceed the set threshold, the soil layer is marked as a high-risk pollution area.

[0022] In some specific embodiments, the process of obtaining the permeability coefficient matrix is:

[0023] The vertical permeability coefficient of each soil layer was measured using a double-ring permeameter, and the horizontal permeability coefficient was obtained by combining it with a pumping test. The anisotropic permeability tensor was formed based on the vertical and horizontal permeability coefficients, and the permeability coefficient matrix of each soil layer was constructed.

[0024] If the current soil layer is a fractured area, the permeability coefficient of the soil layer where the fractured area is located is amplified and corrected, and the permeability coefficient matrix corresponding to the soil layer is marked as the preferential flow channel.

[0025] In some specific embodiments, batch adsorption experiments are used to determine the adsorption model parameters of each soil layer, and a regression model of the adsorption model parameters and soil parameters is established through multivariate regression analysis to calibrate the adsorption capacity of each soil layer for heavy metals and obtain the heavy metal adsorption characteristic parameters of each soil layer.

[0026] In some specific embodiments, the pollution migration model is constructed by:

[0027] Based on Darcy's law, a convection model is constructed to describe the migration of pollutants along the overall flow of the fluid;

[0028] Based on Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion of pollutants from high-concentration areas to low-concentration areas.

[0029] Based on the Freundlich model, an adsorption model is constructed to describe the process of pollutants being adsorbed and fixed by soil;

[0030] The convection model, diffusion model and adsorption model are coupled to obtain the pollution migration model.

[0031] In a second aspect, the present application provides a geological and mineral analysis system, comprising:

[0032] one or more processors;

[0033] A storage unit is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors can implement the geological mineral analysis method described in the first aspect.

[0034] The present invention has the beneficial effects:

[0035] By combining geological radar and resistivity imaging data to construct a three-dimensional geological model, the soil layer boundaries were dynamically divided and the permeability coefficient matrix was modified to accurately characterize preferential migration pathways such as fracture zones. The migration equation was then discretized in time and space using the finite difference method, and the heavy metal flux was iteratively calculated layer by layer, improving the accuracy and practicality of heavy metal infiltration risk prediction in industrial and mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1A schematic diagram of a geological mineral analysis method provided by an embodiment of the present invention;

[0037] Figure 2 A flowchart for dividing multi-layer heterogeneous soil layers provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Unless otherwise specifically stated, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0040] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0041] Additionally, descriptions of well-known structures, functions, and configurations may be omitted for clarity and conciseness. Those skilled 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.

[0042] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0043] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0044] Before introducing the specific implementation methods of this application, the following concepts are explained:

[0045] Example 1

[0046] like Figure 1 As shown, this embodiment provides a geological mineral analysis method, which specifically includes the following steps:

[0047] S1. Obtain geological profile data of the target mining area and construct a three-dimensional geological model based on the geological profile data;

[0048] The geological profile data includes the vertical stratigraphic structure information of the target mining area, which includes:

[0049] Drill core data, providing vertical distribution of formation type (e.g., fill, clay, bedrock), thickness, porosity, and permeability;

[0050] Geological radar scanning data to identify shallow (0-30m) geological structure data (such as fractures, caves and other heterogeneous structures);

[0051] Resistivity imaging data: reveals deep (>30m) geological structure data (such as aquifer boundaries and pollution plume diffusion paths);

[0052] Soil parameters included soil pH, organic matter content (SOM) and initial concentrations of heavy metals.

[0053] In order to facilitate the establishment of subsequent models, the geological profile data is cleaned and normalized, where data cleaning includes:

[0054] Outlier removal: For example, abnormally high values ​​of permeability in a borehole due to drilling disturbance.

[0055] Missing value filling: For unsampled areas, Kriging interpolation is used to estimate parameters.

[0056] Spatial interpolation:

[0057] 3D Kriging Interpolation: Converts discrete borehole data (such as clay layer thickness) into a continuous 3D grid model.

[0058] Trend surface analysis: Modify the spatial distribution trend of soil layer thickness based on terrain elevation data.

[0059] Data normalization:

[0060] Unit unification: for example, the permeability coefficient is unified into m / s and the heavy metal concentration is unified into mg / kg.

[0061] Coordinate system alignment: convert geological radar scan data, resistivity imaging data and drillhole data into the same geographic coordinate system (such as WGS84)

[0062] S2. Detect the permeability matrix and heavy metal adsorption characteristic parameters of each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multiple layers of heterogeneous soil structures;

[0063] like Figure 2 As shown, the specific process of step S2 is:

[0064] S21. Clean and fuse geological profile data, construct a three-dimensional geological model, and calibrate the physical and chemical properties of different strata types in the three-dimensional geological model and perform heterogeneous geological labeling. The physical and chemical properties include permeability matrix and heavy metal adsorption characteristic parameters;

[0065] 1. The process of constructing a 3D geological model is as follows:

[0066] Step 1: Determine the vertical soil layer sequence based on the drill core data, and use the resistivity imaging data to correct the deep soil layer, and vertically stratify the soil in the target mining area;

[0067] Step 2: Kriging interpolation is used to laterally expand the target mining area based on the drill core data to generate a continuous three-dimensional distribution of soil layer types. Combined with the fracture locations scanned by geological radar scanning data, high-permeability preferential flow channels are marked in the model.

[0068] Step 3: Integrate the vertical distribution and the 3D soil layer type distribution to obtain a 3D geological model.

[0069] 2. Heterogeneous geological marking includes preferential flow channel marking and high-risk pollution area marking. The specific process is as follows:

[0070] Deep high-conductivity areas are located in each soil layer based on resistivity imaging data, and shallow fractures are located in each soil layer based on geological radar data. The deep high-conductivity areas and shallow fractures are merged to mark the migration path as a preferential flow channel.

[0071] The physical and chemical properties of each soil layer are queried. When the physical and chemical properties exceed the set threshold, the soil layer is marked as a high-risk pollution area.

[0072] Among them, the permeability coefficient matrix and heavy metal adsorption characteristic parameters provide a quantitative basis for heterogeneous labeling. Based on the parameter values ​​(such as permeability coefficient, adsorption capacity), the labeling rules (such as vertical permeability coefficient Kv>1×10 −4 m / s for high permeability areas). Parameter outlier marking: Identify abnormal areas in parameter distribution through statistical analysis (such as Z-score):

[0073] High permeability area: The permeability coefficient is significantly higher than that of the surrounding units;

[0074] Low adsorption zone: The adsorption characteristic parameters are lower than the threshold (such as adsorption parameter Kf < 1.0 L / kg).

[0075] Multi-parameter coupling: combining permeability and adsorption capacity (e.g. high permeability + low adsorption) to mark high-risk pollution areas.

[0076] 3. The process of obtaining the permeability coefficient matrix is:

[0077] The vertical permeability coefficient of each soil layer was measured using a double-ring permeameter. Kv ), combined with the pumping test to obtain the horizontal permeability coefficient ( Kh ), anisotropic permeability tensor is formed according to vertical permeability coefficient and horizontal permeability coefficient, and permeability coefficient matrix of each soil layer is constructed;

[0078] If the current soil layer is a fractured area, the permeability coefficient of the soil layer where the fractured area is located is amplified and corrected, and the permeability coefficient matrix corresponding to the soil layer is marked as the preferential flow channel.

[0079] 4. Use batch adsorption experiments to determine the adsorption model (e.g., Freundlich model) parameters of each soil layer. Use multiple regression analysis to establish a regression model of adsorption model parameters and soil parameters, calibrate the adsorption capacity of each soil layer for heavy metals, and obtain the heavy metal adsorption characteristic parameters of each soil layer.

[0080] S22. Preliminary stratification of the target mining area soil according to the stratum type in the three-dimensional geological model to divide it into multiple main layers;

[0081] S23. Based on the physicochemical characteristics and heterogeneous geological markers of each main layer, the main layer is further subdivided to obtain multiple layers of heterogeneous structural soil layers.

[0082] Specifically, when dividing:

[0083] Parameter mutation criterion: when the difference in permeability coefficients between adjacent main layers exceeds a threshold, a new soil layer is divided;

[0084] Cluster analysis: The K-means algorithm is used to group the main layers with similar soil parameters (such as clay content, pH, etc.) into the same layer;

[0085] Trend function fitting: describe the spatial variation of soil thickness through polynomial or exponential functions.

[0086] Treatment of abnormal geological structures: Fracture zones or fault areas are layered separately and assigned high permeability coefficients to characterize preferential flow.

[0087] S3. Establish a pollution migration model corresponding to each heterogeneous soil layer;

[0088] The pollution migration model is constructed as follows:

[0089] Based on Darcy's law, a convection model is constructed to describe the migration of pollutants along the overall flow of the fluid;

[0090] Based on Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion of pollutants from high-concentration areas to low-concentration areas.

[0091] Based on the Freundlich model, an adsorption model was constructed to describe the process of pollutant adsorption and fixation by soil. The adsorption model adopted the improved Freundlich model, and the adsorption capacity was calibrated by the soil organic matter content and pH value. The expression of the improved Freundlich model is:

[0092]

[0093] Where 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 exponent, α is the organic matter correction factor, and SOM is the soil organic matter content (%). Kf and α are determined by fitting batch adsorption experiments.

[0094] The convection model, diffusion model and adsorption model are coupled to obtain the pollution migration model.

[0095] 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. Based on the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of heavy metal concentrations in groundwater at the target depth.

[0096] When making predictions, 1. First, divide the target mining area into time and space grids:

[0097] The vertical depth of each soil layer is divided into several layers (e.g., 0.5 meters per layer) to form a spatial grid.

[0098] Divide the forecast period (e.g., 10 years) into time steps (e.g., one step per month) to form a time grid. Assign independent parameters to each heterogeneous soil layer: permeability, porosity, adsorption parameters, etc.

[0099] 2. Discretize the transport equation

[0100] The output of the convection model is used as the convection term of the migration model. The convection term is processed by using the "upwind method" to discretize the influence of the water flow direction to avoid numerical oscillation. For example, when the water flows downward, the concentration of the current layer is affected by the previous layer.

[0101] Diffusion term processing: The central difference method is used to approximate the concentration gradient and calculate the diffusion flux between adjacent spatial grids.

[0102] Adsorption term processing: According to the adsorption model (such as the Freundlich equation), the solid phase adsorption amount is dynamically related to the liquid phase concentration, and the adsorption amount is updated at each time step.

[0103] 3. Layer-by-layer iterative calculation process

[0104] Step 1: Set the initial conditions: the liquid phase concentration (such as the initial concentration of surface pollution sources) and the solid phase adsorption amount of each layer are zero.

[0105] Step 2, time step loop: calculate layer by layer for each time step (such as month 1, month 2...):

[0106] 2.1 Top layer calculation: Based on rainfall or surface input, the heavy metal flux at the top layer boundary is updated. The convection-diffusion-adsorption process in the top layer is calculated to obtain the concentration change of this layer.

[0107] 2.2 Layer-by-layer transfer: The output flux of the upper layer (e.g., the lower boundary flux of the fill layer) is used as the input flux of the lower layer (e.g., the upper boundary flux of the clay layer). The concentration distribution of each layer is calculated in turn, taking into account the permeability coefficient and adsorption capacity of the layer.

[0108] 2.3 Adsorption amount update: According to the liquid phase concentration at the current time step, the solid phase adsorption amount is updated through the adsorption model.

[0109] Step 3, flux output: record the heavy metal flux at the lower boundary of each layer as the input basis for groundwater pollution.

[0110] 4. Predict the spatial distribution of heavy metal concentrations in groundwater at the target depth

[0111] 4.1. Determine the input of pollution sources: Obtain the heavy metal output flux of the bottom soil layer, that is, the mass of heavy metals entering the groundwater layer from the soil per unit time and per unit area. This flux reflects the intensity of the pollution source released over time.

[0112] 4.2. Construction of groundwater flow field model

[0113] Based on geological survey data (such as permeability, aquifer thickness, and hydraulic gradient), a groundwater flow model is developed to determine the direction and velocity of water flow at the target depth. For example, a highly permeable sand layer may form a fast-flowing channel, while a clay layer may restrict water flow.

[0114] 4.3 Simulating pollutant migration paths

[0115] Taking heavy metal flux as the initial pollution source and combining it with groundwater flow velocity and direction, the migration process of pollutants in groundwater is simulated:

[0116] Convection-dominated migration: pollutants spread in the direction of water flow, forming a pollution plume along the water flow path.

[0117] Dispersion and diffusion effect: Pollutants diffuse to the surrounding low-concentration areas due to concentration gradient, forming a concentration gradient distribution.

[0118] Adsorption correction: If the aquifer medium (e.g., clay or organic matter) has an adsorption effect on heavy metals, the model needs to be adjusted to reflect the concentration decay due to solid-liquid partitioning.

[0119] 4.4 Quantifying the dilution effect

[0120] Based on the groundwater flow rate (flow velocity × cross-sectional area) and the pollutant input rate, the concentration after dilution is calculated as:

[0121] High flow areas: Large amounts of clean water are mixed, significantly reducing pollutant concentrations.

[0122] Low flow areas: limited dilution capacity, which may lead to local concentration accumulation.

[0123] For example, if the groundwater flow 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 based on actual adsorption and diffusion).

[0124] 4.5 Dynamic Time Stepping and Spatial Interpolation

[0125] Time iteration: gradually update the pollutant migration status by hours, days or months to simulate long-term cumulative effects.

[0126] Spatial interpolation: Discrete model output (such as monitoring point data) is interpolated through inverse distance weighted (IDW) or kriging to generate a continuous spatial concentration distribution map.

[0127] S5. Quantify parameter uncertainties based on Monte Carlo simulation, generate pollution risk probability cloud maps, and visualize high-risk migration paths through a three-dimensional geographic information system.

[0128] Based on the S4 migration model, the spatial distribution of heavy metal concentrations in groundwater at the target depth (deterministic prediction) was calculated. These data (such as concentration gradients and diffusion ranges) were directly used as the baseline scenario for the S5 Monte Carlo simulation. Key parameters in the migration model (such as permeability coefficients, adsorption intensity coefficients, and hydrological dynamic parameters) were assigned probabilistic distributions (such as normal distribution and triangular distribution). The Monte Carlo simulation randomly sampled these parameters to generate multiple sets of possible concentration distribution results. The combination of Monte Carlo simulation and three-dimensional geographic information enabled the visualization of risk probability cloud maps and migration paths for the first time, providing an intuitive basis for prevention and control decisions.

[0129] Example 2

[0130] This embodiment provides a geological and mineral analysis system, including:

[0131] one or more processors;

[0132] A storage unit is used to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a geological mineral analysis method in Example 1.

[0133] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Based on the technical essence of the present invention and within the spirit and principles of the present invention, any simple modification, equivalent replacement and improvement of the above embodiment shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A geological mineral analysis method, characterized in that: The following steps are involved: S1. Obtain 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 matrix and heavy metal adsorption characteristic parameters of each soil layer in the three-dimensional geological model, and divide the soil in the target mining area into multiple layers of heterogeneous soil structures; The specific process of step S2 is: S21. Clean and fuse geological profile data, construct a three-dimensional geological model, and calibrate the physical and chemical properties of different strata types in the three-dimensional geological model and perform heterogeneous geological labeling. The physical and chemical properties include permeability matrix and heavy metal adsorption characteristic parameters; Heterogeneous geological marking includes preferential flow channel marking and pollution high risk area marking. The specific process is as follows: Locate deep high-conductivity areas in each soil layer based on resistivity imaging data, and locate shallow fractures in each soil layer based on geological radar data; merge the deep high-conductivity areas and shallow fracture locations, and mark the soil layer 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, the soil layer is marked as a high-risk pollution area; S22. Preliminary stratification of the target mining area soil according to the stratum type in the three-dimensional geological model to divide it into multiple main layers; S23, further subdividing the main layer according to the physical and chemical characteristics and heterogeneous geological markers of each main layer to obtain multiple layers of heterogeneous structural soil layers; S3. Establish a pollution migration model corresponding to each heterogeneous soil layer; 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. Based on the output flux of the bottom soil layer, combined with the groundwater flow direction and dilution effect, predict the spatial distribution of heavy metal concentrations in groundwater at the target depth.

2. A geological 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 drill core data, geological radar scanning data, resistivity imaging data and soil parameters. The drill core data includes the vertically distributed stratigraphic type, thickness and porosity; the geological radar scanning data includes shallow geological structure data; the resistivity imaging data includes deep geological structure data; and the soil parameters include soil pH value, organic matter content and initial heavy metal concentration.

3. A geological mineral analysis method according to claim 1, characterized in that: The process of constructing a 3D geological model is as follows: Determine the vertical soil layer sequence based on the drill core data, and use the resistivity imaging data to correct the deep soil layer, and vertically stratify the soil in the target mining area; The drill core data is laterally extended to the target mining area through Kriging interpolation to generate a continuous three-dimensional distribution of soil layer types; The vertical distribution and the three-dimensional soil layer type distribution are integrated to obtain a three-dimensional geological model.

4. A geological mineral analysis method according to claim 1, characterized in that: The process of obtaining the permeability matrix is: The vertical permeability coefficient of each soil layer was measured using a double-ring permeameter, and the horizontal permeability coefficient was obtained by combining it with a pumping test. The anisotropic permeability tensor was formed based on the vertical and horizontal permeability coefficients, and the permeability coefficient matrix of each soil layer was constructed. If the current soil layer is a fractured area, the permeability coefficient of the soil layer where the fractured area is located is amplified and corrected, and the permeability coefficient matrix corresponding to the soil layer is marked as the preferential flow channel.

5. A geological mineral analysis method according to claim 1, characterized in that: Batch adsorption experiments were used to determine the adsorption model parameters of each soil layer. A regression model of the adsorption model parameters and soil parameters was established through multiple regression analysis. The adsorption capacity of each soil layer for heavy metals was calibrated, and the heavy metal adsorption characteristic parameters of each soil layer were obtained.

6. A geological mineral analysis method according to claim 1, characterized in that: The pollution migration model is constructed as follows: Based on Darcy's law, a convection model is constructed to describe the migration of pollutants along the overall flow of the fluid; Based on Fick's second law, a diffusion model is constructed to describe the spontaneous dispersion of pollutants from high-concentration areas to low-concentration areas. Based on the Freundlich model, an adsorption model is constructed to describe the process of pollutants being adsorbed and fixed by soil; The convection model, diffusion model and adsorption model are coupled to obtain the pollution migration model.

7. A geological mineral analysis system, characterized in that: include: one or more processors; A storage unit for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement a geological and mineral analysis method as described in any one of claims 1-6.

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