Soil contaminant detection and assessment method and system based on high-density resistivity

Abstract

The application relates to the technical field of soil pollution monitoring, in particular to a soil pollutant detection and evaluation method and system based on high-density resistivity. The method comprises the following steps: acquiring soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting an electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data according to the soil electrode arrangement mode data to obtain soil resistivity data; performing resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; and performing resistivity anomaly identification on the soil resistivity distribution map to generate abnormal resistivity identification data. The application optimizes soil uniformity analysis, electrode arrangement, resistivity inversion and pollutant diffusion analysis, and improves the accuracy in the soil pollutant detection and evaluation process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil pollution monitoring, in particular to a soil pollutant detection and evaluation method and system based on high-density resistivity. BACKGROUND

[0002] High-density resistivity method was first used for mineral exploration and water resource detection, and then gradually applied to environmental pollution monitoring. The initial resistivity measurement method is relatively simple, mainly relying on single and double electrode devices, with limited depth and resolution. With the progress of technology, high-density resistivity method emerged as the times require, through increasing the density of measurement points and improving the data collection method, realizing the identification of deeper and smaller scale pollutants below the ground surface. With the development of computer technology, high-density resistivity method has been widely used, and the accuracy and speed of data processing have been significantly improved. After that, the emergence of three-dimensional inversion algorithm makes the resistivity data more accurately describe the spatial distribution of underground pollutants, especially in detecting organic matter, heavy metals and other pollutants in soil. In recent years, the high-density resistivity data analysis method combined with artificial intelligence and machine learning has further improved the detection accuracy. By integrating different electrode arrangements and multi-frequency signals, high-density resistivity method has gradually become a core tool for environmental geophysical detection, providing scientific basis for soil pollution control and environmental remediation. However, the current traditional high-density resistivity method is easily affected by electrode arrangement and data collection when applied in areas with poor soil uniformity, resulting in inaccurate results. At the same time, the distribution and diffusion of pollutants are evaluated as an independent link, ignoring the long-term impact of pollution diffusion on soil quality and groundwater, which leads to low accuracy in soil pollutant detection and evaluation process. SUMMARY

[0003] Therefore, it is necessary to provide a soil pollutant detection and evaluation method and system based on high-density resistivity to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a soil pollutant detection and evaluation method based on high-density resistivity, the method comprising the following steps:

[0005] Step S1: obtaining soil information data; performing soil uniformity analysis on the soil information data to generate soil uniformity data; selecting electrode arrangement mode based on the soil uniformity data to generate soil electrode arrangement mode data; collecting soil resistivity data according to the soil electrode arrangement mode data to obtain soil resistivity data;

[0006] Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; identify resistivity anomalies on the soil resistivity distribution map to generate anomaly resistivity identification data; segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions.

[0007] Step S3: Conduct pollutant diffusion analysis on highly conductive polluted areas to generate pollutant diffusion data for highly conductive polluted areas; conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas to generate hazard assessment data for highly conductive polluted areas; conduct soil quality degradation assessment on low conductive polluted areas to generate hazard assessment data for low conductive polluted areas.

[0008] Step S4: Perform regional risk labeling on the hazard assessment data of highly conductive polluted areas and low conductive polluted areas to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

[0009] This invention, through the collection and homogeneity analysis of soil information data, provides a comprehensive understanding of the physical and chemical properties of soil, identifies the level of soil homogeneity, and lays the foundation for subsequent electrode arrangement selection. The results of soil homogeneity analysis help determine suitable electrode arrangements, improving the accuracy and effectiveness of resistivity measurements. By inverting and identifying anomalies in soil resistivity data, a precise spatial distribution map of soil resistivity can be drawn, identifying and classifying areas with abnormal resistivity. The soil resistivity distribution map obtained through resistivity inversion provides data support for the precise location of polluted areas, clearly distinguishing between highly conductive and low-conductivity polluted areas, guiding subsequent pollution assessment and remediation. Analysis of pollutant diffusion in highly conductive polluted areas allows for tracing pollutant diffusion paths and conducting groundwater pollution risk assessments. This process helps predict pollution diffusion trends and provides a scientific basis for environmental protection measures. Assessment of soil quality degradation in low-conductivity polluted areas quantifies the degree and impact of pollution, providing clear guidance for key areas of pollution remediation. By integrating hazard assessment data from highly conductive and low-conductivity contaminated areas and generating a soil regional pollutant risk mapping map, the pollution risk levels of different areas can be clearly displayed. Through pollution decision-making, specific remediation strategies can be formulated to achieve efficient remediation of soil pollutants. The visualization of the map helps decision-makers quickly understand the spatial distribution and severity of pollution, providing scientific guidance for actual soil pollution remediation operations and ensuring the accuracy and effectiveness of remediation actions. The integrated application of these four steps, through scientific data analysis and model selection, achieves accurate detection, risk assessment, and remediation decision-making for soil pollution. Each step provides data support for pollution identification, diffusion prediction, risk assessment, and remediation plan formulation, not only improving the efficiency of pollution remediation but also reducing resource waste during the remediation process, optimizing environmental remediation strategies, and thus effectively improving soil environmental quality. Therefore, this invention improves the accuracy of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion, and pollutant diffusion analysis.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain soil information data;

[0012] Step S12: Perform soil uniformity analysis on soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern.

[0013] Step S13: Based on the shallow and deep detection arrangement patterns, the target area boundary of the soil is confirmed to obtain soil detection target area boundary data; the soil information data is divided into measurement grids using the soil detection target area boundary data to generate soil measurement grid data;

[0014] Step S14: Collect soil resistivity data from the soil measurement grid data to obtain soil resistivity data.

[0015] This invention establishes accurate and comprehensive soil information data as the foundation for the entire soil resistivity measurement process, ensuring the reliability and representativeness of data used in subsequent steps and providing data support for the entire detection process. By analyzing soil homogeneity, heterogeneous regions within the soil can be identified, providing a basis for selecting electrode arrangement patterns. This optimizes electrode placement, improves measurement accuracy and efficiency, and avoids detection errors caused by soil inhomogeneity. Selecting appropriate electrode arrangement patterns (such as shallow and deep detection patterns) based on soil homogeneity data effectively optimizes electrode configuration for different soil characteristics, thereby ensuring accuracy and sensitivity at different detection depths. By clearly defining the boundaries of the target soil detection area, the accuracy and specificity of the measurement can be improved, ensuring the reasonable division of the measurement grid and avoiding the impact of unnecessary regional interference on the overall data quality. Scientific grid division refines soil information data, improving the accuracy of data acquisition. This process ensures uniform coverage of measurement points, making the data more representative and accurate. Resistivity data is the core of soil exploration. By collecting resistivity data point by point on the measurement grid, detailed soil resistivity distribution can be obtained, providing accurate basic data for subsequent data analysis and processing, and ultimately achieving a precise assessment of soil properties and structure.

[0016] Preferably, step S12 includes the following steps:

[0017] Step S121: Extract soil particle morphology features from soil information data to obtain soil particle morphology feature data; divide soil information data into soil layers based on soil particle morphology feature data to generate soil layer division data.

[0018] Step S122: Calculate the soil stratification height from the soil stratification data to generate soil stratification height data; perform stratification particle distribution analysis on the soil stratification data using the soil stratification height data to generate a soil stratification particle distribution map; calculate the distribution height difference from the soil stratification particle distribution map to obtain distribution height difference data.

[0019] Step S123: Calculate the uniformity of soil particle distribution using the height difference data to obtain soil uniformity data; compare the soil uniformity data with a preset soil uniformity threshold. When the soil uniformity data is greater than or equal to the preset soil uniformity threshold, uniform soil data is generated; when the soil uniformity data is less than the preset soil uniformity threshold, non-uniform soil data is generated.

[0020] Step S124: Based on the soil stratification data, perform multimodal electrode arrangement selection on homogeneous soil data and heterogeneous soil data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern.

[0021] This invention helps understand the physical properties of soil by extracting soil particle morphology characteristics, such as particle size and shape, providing crucial data for subsequent soil stratification. Particle morphology data allows for the identification of differences between soil types and layers, providing an accurate basis for electrode arrangement. Soil stratification clarifies the characteristics of different soil layers, providing more refined guidance for the design of measurement grids and the selection of electrode arrangement patterns. Stratification allows for the targeted selection of different detection depths, improving detection accuracy and flexibility. Calculating the height of soil layers clarifies the distribution range of different layers, providing physical boundaries for subsequent measurements and detection. This facilitates the rational allocation of electrode arrays, ensuring detection accuracy at different depths. Generating stratified particle distribution maps provides a more intuitive view of particle distribution across soil layers, aiding in the identification of soil structural characteristics. This is crucial for analyzing soil resistivity distribution, permeability, and other properties, helping to determine the most suitable electrode arrangement. Calculating the difference in distribution height quantifies the non-uniformity of soil layer particle distribution, providing a foundation for subsequent soil uniformity analysis. This reveals soil heterogeneity, helping to determine resistivity differences between different soil layers, thus influencing the selection of detection modes. Calculating soil particle distribution uniformity helps determine soil homogeneity. High soil homogeneity simplifies the selection of electrode arrangement and detection depth, improving detection efficiency and accuracy. If the soil is heterogeneous, the electrode arrangement will be adjusted according to the needs of different areas to ensure optimal detection results. By comparing with a preset soil homogeneity threshold, it is possible to quickly determine whether the soil meets the predetermined homogeneity standard. This helps in selecting whether to use a more refined measurement method or adjust the electrode arrangement based on the actual soil homogeneity, ensuring the reliability of the measurement results. Based on different soil layer data and soil homogeneity information, an appropriate electrode arrangement mode (shallow or deep detection mode) can be flexibly selected. This multimodal electrode arrangement selection not only improves detection accuracy but also optimizes electrode configuration according to actual soil conditions, avoiding unnecessary energy waste and inaccurate measurements.

[0022] Preferably, step S124 includes:

[0023] The soil stratification data is subjected to stratification number extraction to obtain soil stratification number data; based on the soil stratification number data, soil stratification pattern analysis is performed on the soil stratification data to generate shallow soil pattern data and deep soil pattern data.

[0024] Based on shallow soil model data, Wenner electrode arrangement is performed on homogeneous soil data to generate the first shallow detection mode; Pole-Dipole electrode arrangement is performed on heterogeneous soil data using shallow soil model data to generate the second shallow detection mode.

[0025] Based on deep soil model data, Schlumberger electrode arrangement is performed on homogeneous soil data to generate the first deep detection mode; Dipole-Dipole electrode arrangement is performed on heterogeneous soil data using deep soil model data to generate the second deep detection mode.

[0026] The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

[0027] This invention, by extracting the number of soil layers, can accurately identify the soil's hierarchical structure, thus providing fundamental information for electrode configuration. This helps to better understand the layered characteristics of the soil and determine the detection depth and arrangement pattern for each layer. Analysis of soil layer patterns can generate different electrode arrangement patterns for different soil layer characteristics. This analysis helps determine the measurement methods for shallow and deep soils, making the electrode arrangement more consistent with the actual soil structure and improving detection depth and accuracy. The Wenner electrode arrangement is well-suited for homogeneous soils, providing relatively accurate resistivity data in shallow detection. This arrangement effectively captures the resistivity characteristics of shallow soils and improves data reliability. The Pole-Dipole electrode arrangement is suitable for heterogeneous soils, better detecting resistivity differences in heterogeneous soil layers. For heterogeneous soils, using this pattern can increase detection depth and resolution, obtaining higher precision measurement results. The Schlumberger electrode arrangement is suitable for deep soil detection, providing high-precision resistivity measurements in homogeneous soils. Deep detection can better adapt to the resistivity characteristics of soils, reducing errors and improving measurement accuracy. Dipole-Dipole electrode arrangement is particularly effective for deep probing of heterogeneous soils, further revealing resistivity differences between different soil layers and optimizing deep probing results. It can penetrate deep into heterogeneous soils, providing more accurate resistivity measurements. By integrating shallow probing modes, the characteristics of both homogeneous and heterogeneous soils can be considered within a complete probing framework, improving the comprehensiveness and accuracy of shallow measurements. The integrated mode can adapt to various soil conditions, enhancing overall probing performance. Integrating deep probing modes allows for simultaneous optimization of probing strategies for both homogeneous and heterogeneous soils, ensuring high-quality data in deep soil measurements. Through integration, the efficiency of deep probing can be improved, ensuring the comprehensiveness and depth of the data.

[0028] Preferably, step S2 includes the following steps:

[0029] Step S21: Perform data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein data preprocessing includes data cleaning, data missing value filling and data standardization;

[0030] Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; calculate the variation range of the soil resistivity distribution map to obtain resistivity variation range data; identify resistivity anomalies in the soil resistivity distribution map based on the resistivity variation range data to generate abnormal resistivity identification data.

[0031] Step S23: Identify suspected pollution areas in the soil resistivity distribution map using resistivity anomaly identification data to obtain suspected pollution area data; perform resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data.

[0032] Step S24: Classify the suspected contamination area data into abnormal areas based on resistivity change trend data, and generate high conductivity contamination areas and low conductivity contamination areas.

[0033] This invention ensures the quality of input data through data preprocessing, resolving issues such as missing data and noise interference, and improving the accuracy of subsequent analysis. Through data cleaning, missing value imputation, and standardization, standardized data provides a reliable foundation for resistivity inversion and anomaly detection, thereby enhancing the robustness of the entire analysis process. Resistivity inversion transforms measured resistivity data into a spatial distribution map of the soil, visualizing changes in soil resistivity. The soil resistivity distribution map provides an intuitive basis for subsequent pollution area detection and classification, helping researchers identify potential pollution hotspots. By calculating the magnitude of resistivity changes, spatial fluctuations in soil resistivity can be revealed, further identifying areas of significant resistivity variation in the soil. The magnitude of these changes provides a quantitative basis for identifying soil resistivity anomalies. Resistivity anomaly identification can accurately capture areas in the soil that differ from normal levels, especially those affected by pollution or other abnormal factors. The generated anomaly resistivity identification data can help locate problematic soil areas, providing a basis for pollution source tracing and soil remediation. By combining resistivity anomaly identification with soil resistivity distribution maps, contaminated areas can be identified more precisely. Identifying suspected contaminated areas provides a clear target area for subsequent in-depth analysis and practical pollution remediation measures. Resistivity trend analysis helps understand the evolution of contaminated areas and whether pollution is expanding or slowing down. Trend analysis provides crucial temporal information for tracing the source of soil pollution, the rate of pollution spread, and the extent of impact. In-depth analysis of resistivity trends allows contaminated areas to be categorized into highly conductive and low-conductivity areas, facilitating more precise identification of pollution types. High-conductivity areas are typically associated with soils with high pollutant concentrations, while low-conductivity areas are those with low pollutant concentrations. This classification enables more targeted solutions for remediation strategies of different pollution types.

[0034] Preferably, resistivity inversion of standard soil resistivity includes:

[0035] The standard soil resistivity is spatially calibrated using soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is then divided into soil spatial region grid cells to generate soil spatial grid cells, where each soil spatial grid cell contains a local soil resistivity value.

[0036] The theoretical apparent resistivity of soil spatial grid cells is simulated using a forward modeling algorithm to generate forward modeling resistivity values; the error between the forward modeling resistivity values ​​and local soil resistivity values ​​is calculated to generate resistivity error values; and the local soil resistivity values ​​are adjusted using the resistivity error values ​​to generate local adjusted resistivity values.

[0037] The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolated data; the resistivity distribution interpolated data is visualized to generate a soil resistivity distribution map.

[0038] This invention helps eliminate spatial errors in soil resistivity data through spatial calibration, making the data more accurately reflect actual soil characteristics. By considering the soil electrode arrangement pattern, it ensures good matching of calibrated resistivity data in different regions, providing high-quality input data for subsequent inversion. Dividing the soil region into grid cells transforms the soil resistivity distribution into processable discrete data, making further analysis and calculation more detailed and accurate. The local resistivity value of each grid cell provides an important foundation for subsequent forward simulation and error calculation. The forward modeling algorithm can simulate theoretical resistivity values ​​based on the spatial distribution of soil resistivity. This process helps to infer the soil resistivity distribution under given conditions, providing theoretical reference values ​​for comparison with actual data. Error calculation is a key step in ensuring data quality. By calculating the error between the forward-simulated resistivity value and the actual measured value, the differences in the data can be quantified, helping to identify potential problems or anomalies and providing a basis for further optimization of soil resistivity values. Error adjustment can correct deviations in the actual measured data, ensuring more accurate local soil resistivity values. The adjusted local resistivity provides a more accurate input for generating global resistivity, helping to improve the accuracy of the final resistivity distribution. Iterative inversion is an optimization process that gradually approximates the true global resistivity of the soil by continuously adjusting local resistivity. This process effectively reduces calculation errors, making the final resistivity more consistent with actual soil conditions and improving the accuracy of the inversion. Data interpolation fills in the gaps between grid cells, forming continuous resistivity distribution data. Interpolated data makes the soil resistivity distribution smoother, facilitating subsequent visualization and analysis, while avoiding errors caused by data sparsity. Through data visualization, resistivity distribution maps can intuitively show the spatial variation of soil resistivity, providing researchers with effective decision-making support. Soil resistivity distribution maps help to better understand soil properties, pollution levels, and treatment options, and provide scientific support for subsequent soil remediation.

[0039] Preferably, step S3 includes the following steps:

[0040] Step S31: Perform resistivity time series monitoring on the highly conductive polluted area to generate a resistivity time series image of the highly conductive polluted area; perform pollutant diffusion analysis on the resistivity time series image of the highly conductive polluted area to generate pollutant diffusion data of the highly conductive polluted area.

[0041] Step S32: Screen the lowest resistance extreme value area from the soil resistivity distribution map to obtain the resistance extreme value area; locate the pollutant accumulation area in the resistance extreme value area using pollutant diffusion data from highly conductive polluted areas to generate pollutant diffusion and accumulation center point data; conduct groundwater pollution risk assessment in highly conductive polluted areas using pollutant diffusion and accumulation center point data to generate hazard assessment data for highly conductive polluted areas.

[0042] Step S33: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas; performing pollution level migration analysis on soil profiles of low-conductivity contaminated areas to generate pollution level migration data; performing vertical migration restriction analysis on soil profiles of conductive contaminated areas to generate vertical migration data.

[0043] Step S34: Based on pollution level migration data and pollution vertical migration data, conduct soil quality degradation assessment of low conductivity pollution areas and generate hazard assessment data for low conductivity pollution areas.

[0044] This invention, through resistivity time-series monitoring, can dynamically capture resistivity changes within highly conductive contaminated areas, providing real-time data support for pollutant diffusion. Pollutant diffusion analysis helps reveal the expansion trend and scope of pollution sources, providing a scientific basis for implementing pollution prevention and control measures. Screening for extreme resistivity areas can identify regions with the lowest soil resistivity, which are often hotspots for pollutant accumulation. By locating these accumulation zones, the centers of pollutant accumulation can be further analyzed, providing accurate data for groundwater pollution risk assessment and helping to formulate countermeasures to reduce the harm of groundwater pollution. Soil profile sampling and analysis can provide a more precise understanding of soil properties and pollutant distribution in low-conductivity contaminated areas. Horizontal migration analysis reveals the horizontal migration trend of pollutants, while vertical migration limitation analysis helps assess whether pollutants will extend to important resource layers such as groundwater, providing strong data support for soil remediation. By combining horizontal and vertical migration data, the soil quality degradation in low-conductivity contaminated areas can be comprehensively assessed. This helps identify the severity of soil pollution, providing a quantitative basis for the design of soil remediation and pollution control strategies, and effectively predicting the potential harm of contaminated areas to the ecological environment.

[0045] Preferably, step S33 includes the following steps:

[0046] Step S331: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas;

[0047] Step S332: Detect pollutant concentration on the soil cross-section of the low-conductivity contaminated area to generate pollutant concentration data for the contaminated area; calculate the horizontal diffusion rate of pollutants on the soil cross-section of the low-conductivity contaminated area using the pollutant concentration data to obtain pollutant horizontal diffusion data; calculate the horizontal migration trend of pollutants based on the pollutant horizontal diffusion data and the pollutant concentration data for the contaminated area to generate pollution level migration data.

[0048] Step S333: Analyze the vertical concentration distribution of pollutants on the soil cross-section of the conductive contaminated area to generate pollutant vertical concentration distribution data; identify the vertical migration barrier layer on the soil cross-section of the conductive contaminated area based on the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data.

[0049] Step S334: Calculate the vertical migration depth of pollution using pollutant vertical migration barrier layer data and pollutant vertical concentration distribution data to generate pollution vertical migration data.

[0050] This invention, through soil profile sampling, can obtain detailed soil hierarchical structure in low-conductivity contaminated areas, providing necessary sample data for subsequent pollutant detection and diffusion analysis. This helps to accurately understand the depth and distribution range of the contaminated layer. By detecting pollutant concentrations and calculating horizontal diffusion rates, it is possible to gain a deeper understanding of the distribution of pollutants in the soil and their horizontal diffusion trends, thereby predicting the diffusion range of pollutants. Calculating the horizontal migration trend of pollutants helps to assess the degree of diffusion reached by pollutants in the future, thus providing a basis for the timely formulation of pollution prevention and control measures. By analyzing the vertical concentration distribution of pollutants, it is possible to assess the distribution characteristics of pollutants at different soil depths, helping to identify the migration direction of pollutants and obstruction layers. Identifying vertical migration barrier layers helps to determine whether pollutants will further infiltrate groundwater layers or deep soil layers, providing a scientific basis for pollution source location and groundwater protection. The calculation of the vertical migration depth trend of pollutants combines the vertical concentration distribution of pollutants and migration barrier layer data, helping to gain a deeper understanding of the potential vertical migration paths and depths of pollutants. This can predict whether pollutants will affect groundwater layers and other deep soil resources, and assess the depth of pollution expansion, providing key support for the formulation of soil remediation and groundwater protection plans.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Integrate the hazard assessment data of highly conductive polluted areas and the hazard assessment data of low conductive polluted areas to generate soil pollutant detection and assessment data; perform soil area risk labeling on the soil pollutant detection and assessment data to generate soil area pollutant risk labeling data.

[0053] Step S42: Visualize the soil regional pollutant risk labeling data to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

[0054] This invention integrates hazard assessment data from highly conductive and low-conductivity contaminated areas to comprehensively evaluate the pollution level of the entire soil region, providing a unified data basis for subsequent risk assessment and remediation efforts. By risk-labeling these assessment data, potentially hazardous areas of pollutants in the soil can be identified, helping to pinpoint areas requiring priority treatment and improving the accuracy and efficiency of pollution remediation. Data visualization transforms complex pollution risk information into intuitive and easy-to-understand maps, facilitating a rapid understanding of the soil region's pollution status, particularly the distribution and hazard level of pollutants. This visualization provides clearer and more effective support for pollution decision-making, enabling decision-makers to develop remediation plans based on the labeled layers on the map, accurately locating pollution sources and target remediation areas, thereby achieving efficient soil pollution remediation operations. The labeling and visualization of soil pollutant risks enhance the operability of pollution assessment and remediation. The risk-labeled map not only helps to intuitively understand the spatial distribution of soil pollution but also provides a scientific basis for soil pollution remediation work, ensuring the accuracy and targeting of remediation operations, thereby effectively reducing the potential hazards of soil pollution and promoting the smooth progress of environmental remediation.

[0055] This specification provides a soil pollutant detection and assessment system based on high-density resistivity for performing the aforementioned soil pollutant detection and assessment method based on high-density resistivity. The high-density resistivity-based soil pollutant detection and assessment system includes:

[0056] The electrode arrangement module is used to acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select the electrode arrangement pattern based on the soil uniformity data to generate soil electrode arrangement pattern data; and collect soil resistivity data based on the soil electrode arrangement pattern data to obtain soil resistivity data.

[0057] The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; to identify resistivity anomalies in the soil resistivity distribution map to generate abnormal resistivity identification data; and to segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions.

[0058] The hazard assessment module is used to analyze pollutant diffusion in highly conductive polluted areas and generate pollutant diffusion data for these areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas and generate hazard assessment data for these areas; and to assess soil quality degradation in low-conductivity polluted areas and generate hazard assessment data for these areas.

[0059] The risk decision-making module is used to label regional risks based on hazard assessment data of highly conductive polluted areas and low conductive polluted areas, generating a soil regional pollutant risk labeling map; and to construct pollution decisions based on the soil regional pollutant risk labeling map in order to carry out soil pollutant remediation operations.

[0060] The beneficial effects of this invention lie in ensuring that the electrode arrangement matches the soil characteristics through soil homogeneity analysis, thus optimizing the accuracy of data acquisition. This improves the quality of resistivity data, reduces errors caused by differences in soil homogeneity, and provides reliable basic data for subsequent steps. The soil resistivity distribution map generated through resistivity inversion provides spatial distribution information for contaminated areas, and resistivity anomaly identification further distinguishes between highly conductive and low-conductivity contaminated areas. This allows for accurate location of contaminated areas, aiding in the identification of pollution sources and levels, laying the foundation for subsequent analysis. Pollutant diffusion analysis of highly conductive contaminated areas reveals the spread trends of pollutants, thereby predicting potential threats to groundwater. Soil quality degradation assessment of low-conductivity contaminated areas helps evaluate soil health and its future recovery potential, enhancing the scientific rigor and relevance of environmental governance decisions. The generated soil regional pollutant risk mapping clearly identifies the risk level of contaminated areas, providing clear priorities and strategies for pollution remediation. This step helps develop efficient soil pollution remediation plans, ensures optimal resource allocation, and helps reduce remediation costs and improve the effectiveness of pollution remediation. Therefore, this invention improves the accuracy of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion, and pollutant diffusion analysis. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the steps involved in a soil pollutant detection and assessment method based on high-density resistivity.

[0062] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0063] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0064] Figure 4 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4.

[0065] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0066] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0067] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0068] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0069] To achieve the above objectives, please refer to Figures 1 to 4 A method for detecting and assessing soil pollutants based on high-density resistivity, the method comprising the following steps:

[0070] Step S1: Acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data; collect soil resistivity data based on soil electrode arrangement pattern data to obtain soil resistivity data.

[0071] Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; identify resistivity anomalies on the soil resistivity distribution map to generate anomaly resistivity identification data; segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions.

[0072] Step S3: Conduct pollutant diffusion analysis on highly conductive polluted areas to generate pollutant diffusion data for highly conductive polluted areas; conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas to generate hazard assessment data for highly conductive polluted areas; conduct soil quality degradation assessment on low conductive polluted areas to generate hazard assessment data for low conductive polluted areas.

[0073] Step S4: Perform regional risk labeling on the hazard assessment data of highly conductive polluted areas and low conductive polluted areas to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

[0074] This invention, through the collection and homogeneity analysis of soil information data, provides a comprehensive understanding of the physical and chemical properties of soil, identifies the level of soil homogeneity, and lays the foundation for subsequent electrode arrangement selection. The results of soil homogeneity analysis help determine suitable electrode arrangements, improving the accuracy and effectiveness of resistivity measurements. By inverting and identifying anomalies in soil resistivity data, a precise spatial distribution map of soil resistivity can be drawn, identifying and classifying areas with abnormal resistivity. The soil resistivity distribution map obtained through resistivity inversion provides data support for the precise location of polluted areas, clearly distinguishing between highly conductive and low-conductivity polluted areas, guiding subsequent pollution assessment and remediation. Analysis of pollutant diffusion in highly conductive polluted areas allows for tracing pollutant diffusion paths and conducting groundwater pollution risk assessments. This process helps predict pollution diffusion trends and provides a scientific basis for environmental protection measures. Assessment of soil quality degradation in low-conductivity polluted areas quantifies the degree and impact of pollution, providing clear guidance for key areas of pollution remediation. By integrating hazard assessment data from highly conductive and low-conductivity contaminated areas and generating a soil regional pollutant risk mapping map, the pollution risk levels of different areas can be clearly displayed. Through pollution decision-making, specific remediation strategies can be formulated to achieve efficient remediation of soil pollutants. The visualization of the map helps decision-makers quickly understand the spatial distribution and severity of pollution, providing scientific guidance for actual soil pollution remediation operations and ensuring the accuracy and effectiveness of remediation actions. The integrated application of these four steps, through scientific data analysis and model selection, achieves accurate detection, risk assessment, and remediation decision-making for soil pollution. Each step provides data support for pollution identification, diffusion prediction, risk assessment, and remediation plan formulation, not only improving the efficiency of pollution remediation but also reducing resource waste during the remediation process, optimizing environmental remediation strategies, and thus effectively improving soil environmental quality. Therefore, this invention improves the accuracy of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion, and pollutant diffusion analysis.

[0075] In this embodiment of the invention, reference is made to Figure 1 The above is a schematic flowchart of a soil pollutant detection and assessment method based on high-density resistivity according to the present invention. In this example, the soil pollutant detection and assessment method based on high-density resistivity includes the following steps:

[0076] Step S1: Acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data; collect soil resistivity data based on soil electrode arrangement pattern data to obtain soil resistivity data.

[0077] In this embodiment of the invention, soil samples are collected at multiple representative points within a target area using soil sampling equipment (such as soil borehole machines, soil samplers, etc.). Sampling points must be scientifically selected based on factors such as regional geological characteristics, soil type, and pollution level to ensure the representativeness of the sampled data. The collected soil samples are comprehensively analyzed using laboratory or field analysis techniques (such as laser-induced breakdown spectroscopy, X-ray fluorescence analysis, etc.) to obtain basic soil information, including soil type, particle size distribution, moisture content, mineral composition, chemical properties, and pH. Based on the acquired soil information data, statistical analysis methods are used to analyze the spatial distribution and physical and chemical properties of the soil for homogeneity. Commonly used methods include analysis of variance, standard deviation calculation, and Kriging interpolation. Soil homogeneity is quantitatively evaluated, generating soil homogeneity data that reflects the degree of uniformity of the soil within the target area. Homogeneity data is typically presented in the form of charts, spatial distribution maps, or parameter values. Based on the soil homogeneity data, and considering the requirements for resistivity measurement (such as measurement depth, resolution, and detection accuracy), a suitable electrode arrangement is selected. Common electrode arrangements include the Wenner four-electrode method, the Schlumberger four-electrode method, and the Dipole-Dipole method. The applicability of different electrode arrangements under specific soil conditions is evaluated through simulation or actual testing. The arrangement that best meets the requirements for soil resistivity collection is selected, generating soil electrode arrangement data that describes parameters such as electrode installation method, spacing, and depth. Based on the selected electrode arrangement, electrodes are installed at soil sampling points to collect soil resistivity data. Data acquisition is performed using a georesistivity meter (such as AGI or other brands of resistivity measurement equipment), ensuring stable electrode installation and good contact. Appropriate measurement parameters (such as current intensity, electrode spacing, and scanning depth) are set according to different measurement targets and electrode arrangements, and multiple data acquisitions are conducted to ensure data accuracy and reliability. Soil resistivity data at different depths and spatial locations are obtained through inverse calculation or interpolation. This data can help assess the soil's conductivity characteristics and further support related analyses such as soil pollution, remediation, and permeability.

[0078] Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; identify resistivity anomalies on the soil resistivity distribution map to generate anomaly resistivity identification data; segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions.

[0079] In this embodiment of the invention, the soil resistivity data (including information such as electrode spacing, depth, and measurement location) collected in step S1 is organized into input data for inversion analysis. A suitable resistivity inversion algorithm is selected, such as a least squares-based inversion algorithm, simulated annealing algorithm, or genetic algorithm. Commonly used resistivity inversion methods include gradient-based inversion and nonlinear inversion. The collected soil resistivity data is processed using the resistivity inversion algorithm to calculate and generate a two-dimensional or three-dimensional distribution map of soil resistivity. During the inversion process, soil physical properties (such as soil moisture and particle size) are used for auxiliary correction to improve the accuracy of the inversion results. Through resistivity inversion, a spatial distribution map of soil resistivity is obtained, showing the soil resistivity at different depths and in different regions. This map can be displayed using a chromatogram or contour map to reflect changes in soil electrical conductivity. Based on the soil resistivity distribution map, statistical analysis or machine learning methods (such as K-means clustering and support vector machines) are used to identify resistivity outliers. First, the mean and standard deviation of the resistivity data are calculated, and data with large deviations from the mean are marked as outliers. Appropriate resistivity thresholds or standard deviation multiples (e.g., ±2σ, ±3σ) are set as the criteria for identifying abnormal resistivity. Based on the normal resistivity range of the soil, areas with high or low resistivity are identified. Through calculation, areas where resistivity significantly deviates from the normal range are identified, and resistivity anomaly identification data is generated. This data typically includes information such as the location of the anomaly area, the magnitude of the anomaly, and its type. Based on the resistivity anomaly identification data, the resistivity distribution map is segmented. Using segmentation algorithms (such as image segmentation, region growing algorithms, etc.), the soil resistivity distribution map is divided into multiple regions, with a focus on identifying highly conductive and low-conductivity contamination areas. Areas with low resistivity (i.e., areas with high conductivity) are identified; these areas typically represent areas with high concentrations of pollutants in the soil, such as heavy metals and oil contamination. By analyzing the resistivity values ​​of these areas, the distribution range and concentration of pollutants are assessed. Areas with high resistivity (i.e., areas with low conductivity) are identified; these areas are associated with areas far from pollution sources or with milder pollution levels. Based on the segmentation results of resistivity variation regions, spatial distribution data of highly conductive and low-conductivity contamination areas are generated. These contamination areas are then visually displayed using different color markings or region division diagrams.

[0080] Step S3: Conduct pollutant diffusion analysis on highly conductive polluted areas to generate pollutant diffusion data for highly conductive polluted areas; conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas to generate hazard assessment data for highly conductive polluted areas; conduct soil quality degradation assessment on low conductive polluted areas to generate hazard assessment data for low conductive polluted areas.

[0081] In this embodiment of the invention, a pollutant diffusion model is selected for a specific highly conductive polluted area. Common pollutant diffusion models include conventional diffusion equation models, numerical simulation models (such as the finite element method and finite difference method), and pollutant transport models based on physicochemical principles. Based on existing soil resistivity distribution maps and resistivity anomaly areas, the diffusion process of pollutants is analyzed using information such as soil moisture, soil texture, pollutant type, and concentration. The horizontal diffusion process of pollutants in the soil is simulated, and its diffusion rate and path are calculated. Commonly used methods include the Laplace equation, Fick's law of diffusion, and pollutant migration simulation. Based on the simulation results, pollutant diffusion data is generated, including information such as the diffusion range, speed, and temporal changes of pollutants in highly conductive polluted areas. This data is usually presented in the form of diffusion maps, concentration distribution maps, etc., showing the diffusion process of pollutants in different times and spaces. Based on the pollutant diffusion data of highly conductive polluted areas, the risk of pollutants seeping into groundwater is analyzed. The relationship between soil conductivity and groundwater flow is assessed to determine whether and how pollutants seep into the groundwater layer. Groundwater flow models (such as the Darcian flow model and gradient flow model) are used to simulate the pollutant seepage path. Based on the diffusion and infiltration of pollutants, combined with factors such as groundwater flow velocity, soil permeability, and pollutant properties, the risk of groundwater contamination is assessed. Quantitative risk assessment methods (such as probabilistic risk assessment and exposure-response assessment) can be used to quantitatively analyze the likelihood of groundwater contamination. By analyzing pollutant diffusion and groundwater contamination risks, hazard assessment data for highly conductive contaminated areas is generated. This data includes information such as the risk of pollutant diffusion, the likelihood of groundwater contamination, and the scope of impact, used to assess the pollution risk level and remediation priority of the area, and to select specific soil quality degradation assessment models for low-conductivity contaminated areas. These models include factors such as changes in soil organic matter, pH changes, and pollutant accumulation. Commonly used soil degradation assessment models include humus kinetic models and soil pH response models. Based on the physical and chemical properties of the soil, the soil quality degradation in low-conductivity contaminated areas is analyzed, including changes in soil compaction, mineral changes, and pollutant concentration accumulation. For low-conductivity contaminated areas, the focus is on changes in soil pH, nutrient loss, and the impact of pollutants on soil microorganisms. Based on the soil quality degradation analysis results, hazard assessment data for low-conductivity contaminated areas is generated. Assessing the degree of soil quality degradation and its potential impact on soil functions (such as plant growth and groundwater filtration) helps identify high-risk areas and develop appropriate remediation measures.

[0082] Step S4: Perform regional risk labeling on the hazard assessment data of highly conductive polluted areas and low conductive polluted areas to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

[0083] In this embodiment of the invention, hazard assessment data from highly conductive and low-conductivity contaminated areas are integrated. This integration process requires generating comprehensive regional risk data based on assessment data regarding pollutant diffusion, groundwater pollution risk, and soil quality degradation for each area. This data includes information such as pollutant concentration, diffusion rate, risk level, and impact on groundwater. A specific risk labeling model (e.g., based on weighted scoring, fuzzy logic, or Geographic Information System (GIS) analysis) is selected to regionalize and label the integrated hazard assessment data. Through this model, the system can classify areas according to their pollution risk level (e.g., high, medium, low). Based on the hazard assessment data, GIS technology is used to spatially label contaminated areas according to their risk levels. In the soil regional risk labeling map, pollutant risk can be represented by color, symbols, or density layers. For example, high-risk areas are labeled in red, and low-risk areas are labeled in green, generating a soil regional pollutant risk labeling map that includes soil pollutant risk levels. This map provides intuitive geographic information for pollution control, displaying pollutant diffusion areas and their potential hazards, facilitating regional division, resource allocation, and decision-making. Based on soil regional pollutant risk mapping, a suitable pollution remediation decision support model is selected. Common decision models include the Analytic Hierarchy Process (AHP), fuzzy comprehensive evaluation, and artificial intelligence optimization algorithms (such as genetic algorithms and simulated annealing). These models can comprehensively consider factors such as the severity of pollutants, remediation costs, and technical feasibility to help formulate remediation plans. The pollution decision-making process needs to consider factors such as reducing pollutant concentrations, restoring soil quality, and protecting groundwater resources. Based on the risk mapping, high-risk areas should be prioritized for remediation, especially those with rapid pollution diffusion and high potential for groundwater pollution. Appropriate soil pollution remediation technologies should be selected based on regional characteristics (such as pollutant types, concentrations, and soil types). For example, bioremediation, chemical remediation, or physical remediation methods can be used. The economics of remediation should be considered during the decision-making process, optimizing resource allocation and developing phased and regional remediation plans. Using the selected decision support model, combined with information such as the pollution level, risk grade, and remediation costs of each region, specific pollution remediation strategies are generated. This includes specific remediation areas, implementation timelines, budgets, and technical requirements. Based on the results of pollution decision-making, specific soil pollutant remediation operation plans are formulated and remediation work is carried out, including various remediation methods such as pollution source isolation, pollutant removal, and soil improvement.

[0084] Preferably, step S1 includes the following steps:

[0085] Step S11: Obtain soil information data;

[0086] Step S12: Perform soil uniformity analysis on soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern.

[0087] Step S13: Based on the shallow and deep detection arrangement patterns, the target area boundary of the soil is confirmed to obtain soil detection target area boundary data; the soil information data is divided into measurement grids using the soil detection target area boundary data to generate soil measurement grid data;

[0088] Step S14: Collect soil resistivity data from the soil measurement grid data to obtain soil resistivity data.

[0089] In this embodiment of the invention, soil information data is collected from different soil depths and locations using soil detection equipment (such as soil sensors or remote sensing technology). The data should include physical properties of the soil (such as moisture, temperature, density, texture, etc.) and chemical properties (such as pH, salinity, etc.). The collected soil information data is formatted into structured data suitable for analysis, such as tables or three-dimensional datasets, and the homogeneity of the soil samples is evaluated using statistical analysis methods or spatial analysis techniques (such as Geographic Information System, GIS). Based on the analysis results, the variation patterns of the soil in different regions are determined. The homogeneity of the soil samples is quantified using methods such as standard deviation and coefficient of variation; the obtained soil homogeneity data may include soil homogeneity scores for different regions. Based on the soil homogeneity data, a suitable electrode arrangement pattern is selected. If the soil homogeneity is good, a simpler shallow detection arrangement pattern is selected; if the soil layers are distinct or heterogeneous, a deep detection arrangement pattern is selected. The shallow detection arrangement pattern is suitable for situations where the soil is homogeneous and the detection depth is shallow. The deep detection arrangement pattern is suitable for situations where the soil is heterogeneous and requires deeper detection. By combining shallow and deep probe arrangement patterns and utilizing soil homogeneity data and electrode arrangement patterns, the boundary of the target area is clearly defined. Target area boundary data can be generated through preliminary soil resistivity measurements or delineation using geographic information data. Spatial delineation of the boundary is performed based on the probe pattern and soil characteristics, combined with topography or soil type. Based on the target area boundary data, the soil area is divided into standardized measurement grids to facilitate subsequent resistivity measurements. The size and shape of the measurement grids are set according to the required probe accuracy. Regular rectangular or circular grids are typically used to improve data consistency and operability. The generated soil measurement grid data should include grid location coordinates, grid size, and numbering. Resistivity data is collected from the delineated measurement grids using a resistivity instrument. Resistivity tests are performed at each grid point, and the data is recorded. Resistivity measurement methods such as the four-electrode method and the two-electrode method are employed, and the instrument's operating mode is adjusted according to the probe pattern (shallow or deep). Preliminary processing of the collected resistivity data is performed, such as noise reduction and data interpolation, to ensure data accuracy and continuity. The final soil resistivity data should include the resistivity value of each measurement point, measurement depth, corresponding grid number, and other information for subsequent analysis and modeling.

[0090] Preferably, step S12 includes the following steps:

[0091] Step S121: Extract soil particle morphology features from soil information data to obtain soil particle morphology feature data; divide soil information data into soil layers based on soil particle morphology feature data to generate soil layer division data.

[0092] Step S122: Calculate the soil stratification height from the soil stratification data to generate soil stratification height data; perform stratification particle distribution analysis on the soil stratification data using the soil stratification height data to generate a soil stratification particle distribution map; calculate the distribution height difference from the soil stratification particle distribution map to obtain distribution height difference data.

[0093] Step S123: Calculate the uniformity of soil particle distribution using the height difference data to obtain soil uniformity data; compare the soil uniformity data with a preset soil uniformity threshold. When the soil uniformity data is greater than or equal to the preset soil uniformity threshold, uniform soil data is generated; when the soil uniformity data is less than the preset soil uniformity threshold, non-uniform soil data is generated.

[0094] Step S124: Based on the soil stratification data, perform multimodal electrode arrangement selection on homogeneous soil data and heterogeneous soil data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern.

[0095] In this embodiment of the invention, soil particle morphological characteristics, such as particle size, shape, and surface roughness, are obtained using soil particle analysis instruments (e.g., laser particle size analyzer, scanning electron microscope, etc.). Specific particle morphology data are extracted through image analysis or physical testing. Image processing-based analysis methods are used to extract parameters such as particle length, width, and thickness, and to calculate particle morphology indices (e.g., aspect ratio, roundness, etc.). Based on these morphological characteristics, soil particles can be clustered to identify different particle types. The obtained soil particle morphological characteristic data includes the average diameter, shape factor, and surface texture characteristics of the particles. Based on the morphological characteristics of the soil particles, combined with other physicochemical properties of the soil (e.g., particle density, soil texture, etc.), the soil is classified into layers. Soil layering can be determined based on particle size, settling properties, or uniformity of particle distribution. Cluster analysis or stratification algorithms (e.g., K-means clustering, hierarchical clustering, etc.) are used to divide the soil into several layers based on the size, shape, and other characteristics of the soil particles. The obtained soil layering data includes information such as soil type, thickness, and particle characteristics for each layer. Based on soil stratification data, the soil height of each stratum is calculated. This can be determined using indicators such as soil thickness and deposition depth. Geographic Information System (GIS) or surveying instruments (such as depth sensors) are used to acquire height information for each stratum. The resulting soil stratification height data will include soil height information for each stratum, such as the vertical distance from the surface to the deepest layer. Using the soil stratification height data, the distribution of particles within each soil stratum is analyzed. This analysis can use statistical methods or image analysis techniques to create a longitudinal distribution map of particles. Based on the particle distribution, aggregation analysis and distribution pattern recognition are performed to generate a particle distribution map. The resulting soil stratification particle distribution map can include information such as the number, distribution pattern, and density of particles in each stratum. By analyzing the stratification particle distribution map, the height difference of particle distribution is calculated. The height difference reflects the degree of variation of soil particles between strata. By calculating the standard deviation or coefficient of variation of particle distribution in each stratum, the distribution height difference data is obtained, which contains quantitative information on the differences in particle distribution between strata. Based on the distribution height difference data, the uniformity of soil particle distribution is calculated. Evenness can be quantified using statistical indicators such as variability and coefficient of variation, reflecting the uniformity of soil particles within a target area. Evenness is calculated using methods such as analysis of variance, standard deviation, or distribution index based on the distribution height difference data. The resulting soil evenness data is a digital indicator representing the degree of uniformity in soil particle distribution. The calculated soil evenness data is compared with a preset soil evenness threshold. The threshold can be set based on experience or soil characteristics. If the soil evenness data is greater than or equal to the preset threshold, it is considered uniform soil; otherwise, it is considered heterogeneous soil. Based on the comparison results, uniform or heterogeneous soil data is generated.Based on soil stratification data and data on homogeneous or heterogeneous soils, an appropriate electrode arrangement pattern is selected. Shallow detection arrangements are suitable for soils with good homogeneity, while deep detection arrangements are suitable for heterogeneous soils or soils requiring deeper detection. Shallow detection arrangements are suitable for soils with good homogeneity and shallow detection ranges, typically using smaller electrode arrays. Deep detection arrangements are suitable for soils with poor homogeneity or requiring deeper detection, typically using larger or specially arranged electrode arrays. The final generated soil electrode arrangement pattern data will include specific electrode layout information for both shallow and deep detection modes.

[0096] Preferably, step S124 includes:

[0097] The soil stratification data is subjected to stratification number extraction to obtain soil stratification number data; based on the soil stratification number data, soil stratification pattern analysis is performed on the soil stratification data to generate shallow soil pattern data and deep soil pattern data.

[0098] Based on shallow soil model data, Wenner electrode arrangement is performed on homogeneous soil data to generate the first shallow detection mode; Pole-Dipole electrode arrangement is performed on heterogeneous soil data using shallow soil model data to generate the second shallow detection mode.

[0099] Based on deep soil model data, Schlumberger electrode arrangement is performed on homogeneous soil data to generate the first deep detection mode; Dipole-Dipole electrode arrangement is performed on heterogeneous soil data using deep soil model data to generate the second deep detection mode.

[0100] The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

[0101] In this embodiment of the invention, soil stratification data is statistically analyzed to obtain data such as the quantity and thickness of each stratum. The obtained soil stratification data contains quantity information for each stratum, which is helpful for subsequent pattern analysis. Based on the number of strata and the soil hierarchical structure, the distribution patterns of shallow and deep soils are identified. Cluster analysis or regularized pattern recognition techniques can be used to generate shallow and deep soil pattern data for subsequent detection pattern selection. For homogeneous soil data in shallow soils, the Wenner electrode arrangement pattern is used. In this pattern, the electrodes are arranged at equal intervals, suitable for shallow soils with good homogeneity, and helpful for uniform detection of shallow resistivity. For heterogeneous soil data in shallow soils, the Pole-Dipole electrode arrangement pattern is used. The electrodes are arranged in a monopole-dipole structure, suitable for detecting changes in shallow heterogeneous soils. For homogeneous soil data in deep soils, the Schlumberger electrode arrangement pattern is used. The arrangement with a larger distance between the central and outer electrodes is suitable for detecting deep homogeneous soils and helps to increase the detection range. For heterogeneous soil data in deep soil layers, a Dipole-Dipole electrode arrangement is employed. This dipole electrode arrangement is suitable for deep detection in heterogeneous soils and provides higher resolution. The first and second shallow detection modes are integrated to generate a unified shallow detection arrangement. The Wenner and Pole-Dipole electrode arrangements are combined to form a comprehensive detection mode suitable for shallow soil layers. Similarly, the first and second deep detection modes are integrated to generate a unified deep detection arrangement. Finally, the Schlumberger and Dipole-Dipole electrode arrangements are combined to form a comprehensive detection mode suitable for deep soil layers.

[0102] As an example of the present invention, reference is made to Figure 2 As shown, in this example, step S2 includes:

[0103] Step S21: Perform data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein data preprocessing includes data cleaning, data missing value filling and data standardization;

[0104] Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; calculate the variation range of the soil resistivity distribution map to obtain resistivity variation range data; identify resistivity anomalies in the soil resistivity distribution map based on the resistivity variation range data to generate abnormal resistivity identification data.

[0105] Step S23: Identify suspected pollution areas in the soil resistivity distribution map using resistivity anomaly identification data to obtain suspected pollution area data; perform resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data.

[0106] Step S24: Classify the suspected contamination area data into abnormal areas based on resistivity change trend data, and generate high conductivity contamination areas and low conductivity contamination areas.

[0107] In this embodiment of the invention, soil resistivity data is initially cleaned to remove outliers and noise, ensuring data quality. Statistical methods (such as extreme value removal) or machine learning algorithms are used for anomaly detection and cleaning. For the cleaned data, missing values ​​are filled to complete the dataset. Interpolation, mean imputation, or machine learning methods (such as KNN imputation) can be used. The imputed soil resistivity data is then standardized to eliminate dimensional differences and ensure the data conforms to a uniform scale. Z-score or Min-Max standardization is used to adjust the data to a uniform range. Resistivity inversion is performed on the standard soil resistivity data to generate a soil resistivity distribution map. Geophysical inversion algorithms (such as gradient descent or finite element method) are used to convert surface resistivity measurements into a spatial distribution map, generating a soil resistivity distribution map reflecting the resistivity distribution in different regions of the soil. The variation amplitude of the soil resistivity distribution map is calculated to identify resistivity variations in different regions. Differential calculation or percentage variation methods are used to generate resistivity variation amplitude data for each region, which is used to identify abnormal areas. Anomaly identification is performed on soil resistivity distribution maps based on resistivity variation amplitude data. A threshold for resistivity variation amplitude is set, and areas exceeding the threshold are marked as anomalous areas, generating anomalous resistivity identification data for identifying and locating abnormal soil regions. The anomalous resistivity identification data is used to analyze the soil resistivity distribution map to confirm contaminated areas. By comparing the anomalous resistivity distribution with known pollutant characteristics, suspected contaminated areas are screened, generating suspected contaminated area data for further analysis. Resistivity variation trend analysis is performed on the suspected contaminated area data to identify resistivity variation patterns over time or space. Time series analysis or spatial trend analysis is used to assess the resistivity variation trend in suspected contaminated areas, obtaining resistivity variation trend data that reflects the dynamic characteristics of the contaminated area's resistivity. Based on the resistivity variation trend data, suspected contaminated areas are classified, distinguishing between highly conductive and low-conductivity contaminated areas. Based on conductivity differences, a threshold classification method is used to classify areas into highly conductive (areas with higher pollutant concentrations) and low-conductivity areas, generating highly conductive and low-conductivity contaminated area data to provide a basis for subsequent treatment and remediation plans.

[0108] Preferably, resistivity inversion of standard soil resistivity includes:

[0109] The standard soil resistivity is spatially calibrated using soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is then divided into soil spatial region grid cells to generate soil spatial grid cells, where each soil spatial grid cell contains a local soil resistivity value.

[0110] The theoretical apparent resistivity of soil spatial grid cells is simulated using a forward modeling algorithm to generate forward modeling resistivity values; the error between the forward modeling resistivity values ​​and local soil resistivity values ​​is calculated to generate resistivity error values; and the local soil resistivity values ​​are adjusted using the resistivity error values ​​to generate local adjusted resistivity values.

[0111] The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolated data; the resistivity distribution interpolated data is visualized to generate a soil resistivity distribution map.

[0112] In this embodiment of the invention, soil electrode arrangement pattern data is collected. This refers to placing electrodes in the soil of the measurement area according to a specific arrangement to record resistivity data at different locations. Different electrode arrangements (such as vertical, horizontal, or trapezoidal arrangements) will affect the accuracy and range of the measurement data. Standard soil resistivity data is combined with electrode arrangement data for spatial calibration to generate "soil resistivity spatial calibration data." This step uses electrode arrangement data to adjust and correct the original soil resistivity values ​​to ensure that the data reflects the spatial characteristics and geological structure of the soil. Based on the calibrated soil resistivity data, the soil area is divided into multiple small units (grid units). These grid units can be divided into different sizes according to actual needs to accommodate more refined or larger-scale measurements. A representative soil resistivity value is calculated in each grid unit to obtain a "local soil resistivity value," which is used for subsequent simulation and adjustment. The resistivity value of each grid unit is usually represented by the center point value or the average value to simplify subsequent model processing. Forward modeling algorithms (such as finite difference or finite element methods) are used to theoretically calculate the resistivity values ​​of soil spatial grid cells, simulating the apparent resistivity values ​​of the region, known as "forward modeled resistivity values." The forward modeling algorithm uses existing soil resistivity models to calculate the theoretical apparent resistivity values ​​under different electrode arrangements, allowing for comparison with actual measured resistivity. Different forward modeled resistivity values ​​are calculated for different electrode arrangements and depths to ensure the comparability of the generated simulated data with actual measured data. The forward modeled resistivity values ​​are compared one by one with local soil resistivity values ​​to calculate the error value, known as the "resistivity error value." Error calculation often uses absolute error or relative error to quantify the deviation between the two. The resistivity error value is used to adjust the local soil resistivity, generating "locally adjusted resistivity." Adjustment methods can employ iterative optimization techniques such as least squares or gradient descent to minimize resistivity error, thereby improving model accuracy. The adjusted resistivity values ​​are fed back into the soil spatial grid cells, preparing for the next iterative inversion step. The locally adjusted resistivity values ​​are used as input for multiple iterative inversions. Each iteration utilizes error information to optimize resistivity values, gradually reducing resistivity error until a preset convergence condition is met. After each iteration, a "global adjusted resistivity of the soil" is generated, representing the comprehensive resistivity distribution of the entire measurement area. The inversion process typically sets a certain number of iterations or an error threshold to ensure the reliability and stability of the results. The global adjusted resistivity data is then interpolated to generate "resistivity distribution interpolated data." Interpolation methods can include inverse distance weighted interpolation (IDW), kriging interpolation, or other specific geological data interpolation algorithms to smooth the resistivity distribution. The interpolated resistivity data is then visualized to generate a "soil resistivity distribution map." Visualization typically produces a color distribution map, showing the spatial distribution of resistivity, facilitating subsequent analysis and geological research.

[0113] As an example of the present invention, reference is made to Figure 3 As shown, step S3 in this example includes:

[0114] Step S31: Perform resistivity time series monitoring on the highly conductive polluted area to generate a resistivity time series image of the highly conductive polluted area; perform pollutant diffusion analysis on the resistivity time series image of the highly conductive polluted area to generate pollutant diffusion data of the highly conductive polluted area.

[0115] Step S32: Screen the lowest resistance extreme value area from the soil resistivity distribution map to obtain the resistance extreme value area; locate the pollutant accumulation area in the resistance extreme value area using pollutant diffusion data from highly conductive polluted areas to generate pollutant diffusion and accumulation center point data; conduct groundwater pollution risk assessment in highly conductive polluted areas using pollutant diffusion and accumulation center point data to generate hazard assessment data for highly conductive polluted areas.

[0116] Step S33: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas; performing pollution level migration analysis on soil profiles of low-conductivity contaminated areas to generate pollution level migration data; performing vertical migration restriction analysis on soil profiles of conductive contaminated areas to generate vertical migration data.

[0117] Step S34: Based on pollution level migration data and pollution vertical migration data, conduct soil quality degradation assessment of low conductivity pollution areas and generate hazard assessment data for low conductivity pollution areas.

[0118] In this embodiment of the invention, electrode sensors are uniformly arranged within a highly conductive contaminated area, and the monitoring depth and frequency are selected to adapt to the pollutant diffusion characteristics within the area. Resistivity data is periodically collected using the electrode sensors, recorded, and compiled into a time series to ensure the integrity and continuity of the monitoring data. The collected data is processed into a resistivity time series image using a resistivity imaging system for intuitive analysis of resistivity changes over time. A diffusion model (such as a Gaussian model) is used to analyze the resistivity change trend in the time series image, extracting the pollutant diffusion trajectory and generating pollutant diffusion data for the highly conductive contaminated area. The area with the lowest resistivity is selected from the soil resistivity distribution map, pinpointing the most severely polluted locations. By analyzing the pollutant diffusion data in the resistivity extreme areas, a concentration gradient calculation method is used to determine the pollutant diffusion center and generate aggregation center point data. Based on the pollutant diffusion center point and soil layer structure, a groundwater pollution diffusion model is established. Pollution risk is assessed by simulating groundwater pollution paths, ultimately generating hazard assessment data for the highly conductive contaminated area. Representative sampling points are selected in the low-conductivity contaminated area for stratified sampling, collecting soil samples at different depths. The concentration of pollutants in soil samples is measured, and the horizontal migration rate of pollutants is calculated using a diffusion model to generate horizontal migration data. Using equipment such as a permeability meter, the permeability of the soil layer and the vertical migration capacity of pollutants are analyzed to generate vertical migration data. The horizontal and vertical migration data are integrated, and a soil quality degradation assessment model is used to analyze the degree of soil degradation in low-conductivity polluted areas. Based on the soil degradation analysis results, a hazard assessment report is generated to support pollution control decisions and to produce hazard assessment data for low-conductivity polluted areas.

[0119] Preferably, step S33 includes the following steps:

[0120] Step S331: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas;

[0121] Step S332: Detect pollutant concentration on the soil cross-section of the low-conductivity contaminated area to generate pollutant concentration data for the contaminated area; calculate the horizontal diffusion rate of pollutants on the soil cross-section of the low-conductivity contaminated area using the pollutant concentration data to obtain pollutant horizontal diffusion data; calculate the horizontal migration trend of pollutants based on the pollutant horizontal diffusion data and the pollutant concentration data for the contaminated area to generate pollution level migration data.

[0122] Step S333: Analyze the vertical concentration distribution of pollutants on the soil cross-section of the conductive contaminated area to generate pollutant vertical concentration distribution data; identify the vertical migration barrier layer on the soil cross-section of the conductive contaminated area based on the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data.

[0123] Step S334: Calculate the vertical migration depth of pollution using pollutant vertical migration barrier layer data and pollutant vertical concentration distribution data to generate pollution vertical migration data.

[0124] In this embodiment of the invention, sampling points are selected in low-conductivity contaminated areas based on soil type and contamination diffusion to ensure representative sampling distribution. Specialized sampling equipment, such as geological drilling rigs, is used to perform stratified sampling at each point, recording information such as depth, soil structure, and moisture content of each layer. The sampling results are used to generate soil profile data to characterize the vertical soil distribution characteristics of the low-conductivity contaminated area. The collected soil samples are then tested for contaminant concentrations, including heavy metals and volatile organic compounds, generating contaminant concentration data for the contaminated area. Combining the contaminant concentration data and the stratification structure of the soil profile, the horizontal diffusion rate of contaminants in the soil is calculated using diffusion equations, yielding horizontal contaminant diffusion data. Based on the horizontal diffusion and concentration data, numerical simulation methods are used to analyze the horizontal migration trend of contaminants within the region, generating pollution horizontal migration data that reflects the diffusion trend and potential contamination range of contaminants. Vertical concentration testing is performed on soil samples at different depths within the soil profile to generate vertical contaminant concentration distribution data, helping to identify the vertical aggregation characteristics of contaminants. Based on vertical concentration distribution data, combined with soil stratification, permeability, and chemical reaction characteristics, barrier layers (such as clay or hard layers) that hinder the vertical migration of pollutants are identified, generating data on vertical migration barrier layers. Based on the pollutant vertical concentration distribution data and the vertical migration barrier layer data, geological models are used to simulate and assess the vertical migration depth of pollutants, calculating the depth and trend of downward migration. Through vertical migration depth analysis, pollution vertical migration data is generated to provide an assessment of the diffusion risk of pollutants in the vertical distribution of soil, supporting groundwater pollution prevention and control.

[0125] As an example of the present invention, reference is made to Figure 4 As shown, step S4 in this example includes:

[0126] Step S41: Integrate the hazard assessment data of highly conductive polluted areas and the hazard assessment data of low conductive polluted areas to generate soil pollutant detection and assessment data; perform soil area risk labeling on the soil pollutant detection and assessment data to generate soil area pollutant risk labeling data.

[0127] Step S42: Visualize the soil regional pollutant risk labeling data to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

[0128] In this embodiment of the invention, hazard assessment data from highly conductive polluted areas and low-conductivity polluted areas are collected. The two types of data undergo unified format conversion and numerical normalization to ensure data compatibility and consistency. Weighted averaging or hierarchical clustering algorithms are used to integrate and analyze the assessment data, generating comprehensive soil regional pollutant detection and assessment data based on pollutant type, concentration, and migration characteristics. Based on the soil regional pollutant detection and assessment data, a risk grading model (such as matrix risk assessment) is used to classify the polluted areas into risk levels. Key factors considered include pollutant concentration, migration rate, soil properties, and the potential environmental hazards of the pollutants. Different risk levels (e.g., low, medium, high risk) are color-coded and categorized to generate soil regional pollutant risk labeling data, which visually displays the spatial distribution characteristics of pollution risks. A Geographic Information System (GIS) is used to visualize the soil regional pollutant risk labeling data, generating a soil regional pollutant risk labeling map. Different colors, symbols, or legends are used on the map to distinguish various pollution risk areas (e.g., green for low risk, yellow for medium risk, and red for high risk), ensuring the map is clear, intuitive, and easy to interpret and use for subsequent decision-making. Based on soil regional pollutant risk mapping, pollution remediation decisions are made. Following the principle of prioritizing high-risk areas, specific remediation operation plans are developed, including pollutant removal, soil remediation, and post-remediation monitoring. Resource allocation, remediation methods (such as physical remediation, chemical treatment, or bioremediation), personnel arrangements, and timelines are incorporated into the remediation decision-making process to form a comprehensive soil pollutant remediation operation plan. Finally, soil pollution remediation operations are implemented, and progress and effects are recorded for subsequent tracking and evaluation.

[0129] This specification provides a soil pollutant detection and assessment system based on high-density resistivity for performing the aforementioned soil pollutant detection and assessment method based on high-density resistivity. The high-density resistivity-based soil pollutant detection and assessment system includes:

[0130] The electrode arrangement module is used to acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select the electrode arrangement pattern based on the soil uniformity data to generate soil electrode arrangement pattern data; and collect soil resistivity data based on the soil electrode arrangement pattern data to obtain soil resistivity data.

[0131] The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; to identify resistivity anomalies in the soil resistivity distribution map to generate abnormal resistivity identification data; and to segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions.

[0132] The hazard assessment module is used to analyze pollutant diffusion in highly conductive polluted areas and generate pollutant diffusion data for these areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas and generate hazard assessment data for these areas; and to assess soil quality degradation in low-conductivity polluted areas and generate hazard assessment data for these areas.

[0133] The risk decision-making module is used to label regional risks based on hazard assessment data of highly conductive polluted areas and low conductive polluted areas, generating a soil regional pollutant risk labeling map; and to construct pollution decisions based on the soil regional pollutant risk labeling map in order to carry out soil pollutant remediation operations.

[0134] The beneficial effects of this invention lie in ensuring that the electrode arrangement matches the soil characteristics through soil homogeneity analysis, thus optimizing the accuracy of data acquisition. This improves the quality of resistivity data, reduces errors caused by differences in soil homogeneity, and provides reliable basic data for subsequent steps. The soil resistivity distribution map generated through resistivity inversion provides spatial distribution information for contaminated areas, and resistivity anomaly identification further distinguishes between highly conductive and low-conductivity contaminated areas. This allows for accurate location of contaminated areas, aiding in the identification of pollution sources and levels, laying the foundation for subsequent analysis. Pollutant diffusion analysis of highly conductive contaminated areas reveals the spread trends of pollutants, thereby predicting potential threats to groundwater. Soil quality degradation assessment of low-conductivity contaminated areas helps evaluate soil health and its future recovery potential, enhancing the scientific rigor and relevance of environmental governance decisions. The generated soil regional pollutant risk mapping clearly identifies the risk level of contaminated areas, providing clear priorities and strategies for pollution remediation. This step helps develop efficient soil pollution remediation plans, ensures optimal resource allocation, and helps reduce remediation costs and improve the effectiveness of pollution remediation. Therefore, this invention improves the accuracy of soil pollutant detection and assessment by optimizing soil uniformity analysis, electrode arrangement, resistivity inversion, and pollutant diffusion analysis.

[0135] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0136] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for detecting and assessing soil pollutants based on high-density resistivity, characterized in that, Includes the following steps: Step S1: Acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data; collect soil resistivity data based on soil electrode arrangement pattern data to obtain soil resistivity data. Step S2: Perform resistivity inversion on the soil resistivity data to generate a soil resistivity distribution map; identify resistivity anomalies on the soil resistivity distribution map to generate anomaly resistivity identification data; segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions. Step S3: Perform pollutant diffusion analysis on highly conductive polluted areas to generate pollutant diffusion data for highly conductive polluted areas; Groundwater pollution risk assessment is conducted using pollutant diffusion data from highly conductive polluted areas to analyze soil resistivity distribution maps, generating hazard assessment data for highly conductive polluted areas; soil quality degradation assessment is conducted using low-conductivity polluted areas, generating hazard assessment data for low-conductivity polluted areas. Step S4: Perform regional risk labeling on the hazard assessment data of highly conductive polluted areas and low conductive polluted areas to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

2. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain soil information data; Step S12: Perform soil uniformity analysis on soil information data to generate soil uniformity data; select electrode arrangement pattern based on soil uniformity data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern. Step S13: Based on the shallow and deep detection arrangement patterns, the target area boundary of the soil is confirmed to obtain soil detection target area boundary data; the soil information data is divided into measurement grids using the soil detection target area boundary data to generate soil measurement grid data; Step S14: Collect soil resistivity data from the soil measurement grid data to obtain soil resistivity data.

3. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 2, characterized in that, Step S12 includes the following steps: Step S121: Extract soil particle morphology features from soil information data to obtain soil particle morphology feature data; divide soil information data into soil layers based on soil particle morphology feature data to generate soil layer division data. Step S122: Calculate the soil stratification height from the soil stratification data to generate soil stratification height data; perform stratification particle distribution analysis on the soil stratification data using the soil stratification height data to generate a soil stratification particle distribution map; calculate the distribution height difference from the soil stratification particle distribution map to obtain distribution height difference data. Step S123: Calculate the uniformity of soil particle distribution using the height difference data to obtain soil uniformity data; compare the soil uniformity data with a preset soil uniformity threshold. When the soil uniformity data is greater than or equal to the preset soil uniformity threshold, uniform soil data is generated; when the soil uniformity data is less than the preset soil uniformity threshold, non-uniform soil data is generated. Step S124: Based on the soil stratification data, perform multimodal electrode arrangement selection on homogeneous soil data and heterogeneous soil data to generate soil electrode arrangement pattern data, which includes shallow detection arrangement pattern and deep detection arrangement pattern.

4. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 3, characterized in that, Step S124 includes: The soil stratification data is subjected to stratification number extraction to obtain soil stratification number data; based on the soil stratification number data, soil stratification pattern analysis is performed on the soil stratification data to generate shallow soil pattern data and deep soil pattern data. Based on shallow soil model data, Wenner electrode arrangement is performed on homogeneous soil data to generate the first shallow detection mode; Pole-Dipole electrode arrangement is performed on heterogeneous soil data using shallow soil model data to generate the second shallow detection mode. Based on deep soil model data, Schlumberger electrode arrangement is performed on homogeneous soil data to generate the first deep detection mode; Dipole-Dipole electrode arrangement is performed on heterogeneous soil data using deep soil model data to generate the second deep detection mode. The shallow first detection mode and the shallow second detection mode are integrated to generate a shallow detection arrangement mode; the deep first detection mode and the deep second detection mode are integrated to generate a deep detection arrangement mode.

5. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform data preprocessing on the soil resistivity data to generate standard soil resistivity data, wherein data preprocessing includes data cleaning, data missing value filling and data standardization; Step S22: Perform resistivity inversion on the standard soil resistivity to generate a soil resistivity distribution map; calculate the variation range of the soil resistivity distribution map to obtain resistivity variation range data; identify resistivity anomalies in the soil resistivity distribution map based on the resistivity variation range data to generate abnormal resistivity identification data. Step S23: Identify suspected pollution areas in the soil resistivity distribution map using resistivity anomaly identification data to obtain suspected pollution area data; perform resistivity change trend analysis on the suspected pollution area data to generate resistivity change trend data. Step S24: Classify the suspected contamination area data into abnormal areas based on resistivity change trend data, and generate high conductivity contamination areas and low conductivity contamination areas.

6. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 5, characterized in that, Resistivity inversion of standard soil resistivity includes: The standard soil resistivity is spatially calibrated using soil electrode arrangement pattern data to generate soil resistivity spatial calibration data; the soil resistivity spatial calibration data is then divided into soil spatial region grid cells to generate soil spatial grid cells, where each soil spatial grid cell contains a local soil resistivity value. The theoretical apparent resistivity of soil spatial grid cells is simulated using a forward modeling algorithm to generate forward modeling resistivity values; the error between the forward modeling resistivity values ​​and local soil resistivity values ​​is calculated to generate resistivity error values; and the local soil resistivity values ​​are adjusted using the resistivity error values ​​to generate local adjusted resistivity values. The local adjusted resistivity is iteratively inverted to generate the global adjusted resistivity of the soil; the global adjusted resistivity of the soil is interpolated to generate resistivity distribution interpolated data; the resistivity distribution interpolated data is visualized to generate a soil resistivity distribution map.

7. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform resistivity time series monitoring on the highly conductive polluted area to generate a resistivity time series image of the highly conductive polluted area; perform pollutant diffusion analysis on the resistivity time series image of the highly conductive polluted area to generate pollutant diffusion data of the highly conductive polluted area. Step S32: Screen the lowest resistance extreme value area from the soil resistivity distribution map to obtain the resistance extreme value area; locate the pollutant accumulation area in the resistance extreme value area using pollutant diffusion data from highly conductive polluted areas to generate pollutant diffusion and accumulation center point data; conduct groundwater pollution risk assessment in highly conductive polluted areas using pollutant diffusion and accumulation center point data to generate hazard assessment data for highly conductive polluted areas. Step S33: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas; performing pollution level migration analysis on soil profiles of low-conductivity contaminated areas to generate pollution level migration data; performing vertical migration restriction analysis on soil profiles of conductive contaminated areas to generate vertical migration data. Step S34: Based on pollution level migration data and pollution vertical migration data, conduct soil quality degradation assessment of low conductivity pollution areas and generate hazard assessment data for low conductivity pollution areas.

8. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 7, characterized in that, Step S33 includes the following steps: Step S331: Sampling soil profiles in low-conductivity contaminated areas to generate soil profiles of low-conductivity contaminated areas; Step S332: Detect pollutant concentration on the soil cross-section of the low-conductivity contaminated area to generate pollutant concentration data for the contaminated area; calculate the horizontal diffusion rate of pollutants on the soil cross-section of the low-conductivity contaminated area using the pollutant concentration data to obtain pollutant horizontal diffusion data; calculate the horizontal migration trend of pollutants based on the pollutant horizontal diffusion data and the pollutant concentration data for the contaminated area to generate pollution level migration data. Step S333: Analyze the vertical concentration distribution of pollutants on the soil cross-section of the conductive contaminated area to generate pollutant vertical concentration distribution data; identify the vertical migration barrier layer on the soil cross-section of the conductive contaminated area based on the pollutant vertical concentration distribution data to generate pollutant vertical migration barrier layer data. Step S334: Calculate the vertical migration depth of pollution using pollutant vertical migration barrier layer data and pollutant vertical concentration distribution data to generate pollution vertical migration data.

9. The method for detecting and assessing soil pollutants based on high-density resistivity according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Integrate the hazard assessment data of highly conductive polluted areas and the hazard assessment data of low conductive polluted areas to generate soil pollutant detection and assessment data; perform soil area risk labeling on the soil pollutant detection and assessment data to generate soil area pollutant risk labeling data. Step S42: Visualize the soil regional pollutant risk labeling data to generate a soil regional pollutant risk labeling map; construct pollution decision-making based on the soil regional pollutant risk labeling map to carry out soil pollutant remediation operations.

10. A soil pollutant detection and assessment system based on high-density resistivity, characterized in that, For performing the soil pollutant detection and assessment method based on high-density resistivity as described in claim 1, the soil pollutant detection and assessment system based on high-density resistivity comprises: The electrode arrangement module is used to acquire soil information data; perform soil uniformity analysis on the soil information data to generate soil uniformity data; select the electrode arrangement pattern based on the soil uniformity data to generate soil electrode arrangement pattern data; and collect soil resistivity data based on the soil electrode arrangement pattern data to obtain soil resistivity data. The abnormal soil identification module is used to perform resistivity inversion on soil resistivity data to generate a soil resistivity distribution map; to identify resistivity anomalies in the soil resistivity distribution map to generate abnormal resistivity identification data; and to segment the soil resistivity distribution map into resistivity variation regions using the resistivity anomaly identification data to generate high conductivity pollution regions and low conductivity pollution regions. The hazard assessment module is used to analyze pollutant diffusion in highly conductive polluted areas and generate pollutant diffusion data for these areas; to conduct groundwater pollution risk assessment on soil resistivity distribution maps using pollutant diffusion data from highly conductive polluted areas and generate hazard assessment data for these areas; and to assess soil quality degradation in low-conductivity polluted areas and generate hazard assessment data for these areas. The risk decision-making module is used to label regional risks based on hazard assessment data of highly conductive polluted areas and low conductive polluted areas, generating a soil regional pollutant risk labeling map; and to construct pollution decisions based on the soil regional pollutant risk labeling map in order to carry out soil pollutant remediation operations.

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