A method for tracing groundwater pollution in a chemical industrial park based on geophysical and geochemical exploration cooperation

Abstract

The present application belongs to the field of sewage treatment, and provides a chemical industry park groundwater pollution tracing method based on geophysical exploration cooperation, which uses geophysical methods to detect target areas in the chemical industry park, and obtains underground electrical parameter data; based on the spatial form characteristics and physical parameter gradient change law of the resistivity anomaly area, a layout scheme of groundwater chemical sampling target points is determined; groundwater samples are collected according to the layout scheme, and chemical detection analysis is performed; the pollutant data obtained by the chemical detection analysis is subjected to spatial position corresponding and correlation analysis with the resistivity three-dimensional model; the pollutant concentration spatial distribution map, the park enterprise layout and the regional hydrogeological conditions are comprehensively considered to identify the pollutant migration path, lock the pollution source and output the pollution tracing result. The method improves the accuracy of pollution tracing.

Description

Technical Field

[0001] This invention belongs to the field of wastewater treatment, and specifically relates to a method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration synergy. Background Technology

[0002] Chemical industrial parks, as the main battleground for modern, high-quality development, drive regional economic growth but also pose a risk of groundwater pollution. Hidden underground or grounded storage tanks, pipelines, and pools within the parks, as well as damage to surface impermeable linings leading to pollutant leakage, can all cause groundwater pollution. Accurate source tracing of groundwater pollution is crucial for achieving scientific, precise, and law-based pollution control, and is of great significance for protecting public health and ecological environmental safety.

[0003] Currently, the main methods for tracing groundwater pollution sources in chemical industrial parks include geophysical and geochemical methods. Geophysical methods mainly include high-density electrical resistivity tomography (EDT), ground-penetrating radar (GPR), and stimulated polarization (SNP). These methods can rapidly, non-destructively, and continuously detect pollution within a certain range, providing a macroscopic understanding of the underground space conditions of the entire park. Among these, EDT is the mainstay of groundwater pollution tracing and is one of the most widely used and effective methods for groundwater pollution detection. Most soluble organic pollutants increase the ion concentration in groundwater, leading to a significant decrease in resistivity and forming a distinct "low-resistivity anomaly." By characterizing multiple profiles or conducting three-dimensional observations, the lateral and longitudinal spatial distribution range of the pollution plume, as well as its migration depth and morphology, can be accurately depicted. Furthermore, through repeated measurements at different times, dynamic monitoring of the diffusion and migration of groundwater pollution plumes can be achieved. However, this method is significantly affected by complex geological conditions and external electromagnetic interference, such as underground pipelines, cables, and concrete surfaces, resulting in interpretation ambiguity and making it difficult to accurately identify the specific location and type of pollution source. Furthermore, high-density electrical resistivity tomography (EDT) has limited resolution for detecting deep or small-scale pollution plumes, especially prone to missed or false detections in heterogeneous aquifers. Geochemical methods, primarily through borehole sampling, collect and analyze groundwater samples, accurately identifying pollutants and their concentrations, yielding reliable conclusions. However, their drawbacks include high cost, long processing times, and the significant impact of unrepresentative sampling locations or substandard monitoring well construction on source tracing results. Ground-penetrating radar (GPR), on the other hand, can clean and identify shallow underground buried pipes, tanks, and sludge pits. For insoluble light or heavy non-aqueous liquids, due to significant differences in dielectric constants, it can effectively characterize their distribution range, offering fast measurement speeds and on-site detection results. Its limitations lie in limited detection depth and stringent applicable conditions.

[0004] Therefore, relying solely on physical or chemical detection methods cannot guarantee the accuracy and scientific rigor of groundwater pollution source tracing, leading to high costs, low accuracy, and uncertain results. There is an urgent need for a method that can complement physical and chemical detection methods, enabling collaborative verification and achieving accurate and scientific source tracing. Summary of the Invention

[0005] To address the problems in existing technologies, this invention provides a method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration, comprising the following steps:

[0006] Geophysical methods are used to explore the target area of ​​the chemical industrial park, obtain underground electrical parameter data, and generate a three-dimensional resistivity model through data inversion to identify and delineate at least one resistivity anomaly area. The resistivity anomaly area is used to characterize the spatial range where underground pollution may exist.

[0007] Based on the spatial morphological characteristics and physical parameter gradient change law of the resistivity anomaly zone, a layout scheme for groundwater chemical sampling target points is determined. The layout scheme includes setting up points at the boundary of the resistivity anomaly zone with the largest resistivity gradient change, setting up points in the central area of ​​the resistivity anomaly, and setting up points upstream, inside and downstream of the resistivity anomaly zone according to the groundwater flow direction.

[0008] Groundwater samples were collected according to the deployment plan and chemically analyzed to obtain data on the types, concentrations, and spatial distribution of pollutants.

[0009] The pollutant data obtained from the chemical detection and analysis are spatially correlated and correlated with the resistivity three-dimensional model. Based on the spatial superposition relationship between the pollutant concentration distribution and the resistivity anomaly area, it is verified whether the cause of the resistivity anomaly is related to the pollutants, so as to eliminate the ambiguity of geophysical methods. Using the resistivity three-dimensional model as the spatial skeleton and the discrete pollutant concentration data as the constraint, a spatial distribution map of pollutant concentration is generated using spatial interpolation algorithms or machine learning algorithms.

[0010] Based on the spatial distribution map of pollutant concentration, the layout of enterprises in the park, and the regional hydrogeological conditions, the migration path of pollutants is identified, the source of pollution is located, and the pollution source tracing results are output.

[0011] Furthermore, the geophysical method employs a combination of high-density electrical resistivity tomography (EDT) and ground-penetrating radar (GPR).

[0012] Furthermore, the data inversion incorporates constraints on the depth of the formation interface, the thickness of the aquifer, and the distribution of underground pipelines.

[0013] Furthermore, the identification of the resistivity anomaly region is achieved by calculating the resistivity gradient of adjacent grid cells, and the region where the gradient magnitude exceeds a set threshold is taken as the anomaly boundary region.

[0014] Furthermore, the layout of the groundwater chemical sampling target points is determined in conjunction with the groundwater flow direction information, and sampling profiles are laid out along the main flow direction of the groundwater.

[0015] Furthermore, the spacing between the groundwater chemical sampling target points is determined based on the degree of resistivity gradient change. When the gradient change is drastic, the sampling density is increased, and when the resistivity anomaly zone has a regular shape, a regular grid layout is adopted.

[0016] Furthermore, spatial registration is performed during the spatial location correspondence and correlation analysis. A coordinate matching algorithm based on least squares optimization is used to ensure that the spatial deviation between the chemical sampling point and the resistivity model unit does not exceed a preset value.

[0017] Furthermore, after registration, a bivariate statistical analysis is performed on the pollutant concentration at each sampling point and the resistivity value of its corresponding resistivity unit to calculate the correlation coefficient and trend consistency index. If the correlation coefficient is negative and the difference between its absolute value and 1 is less than the first preset value, it indicates that the increase in pollutant concentration and the decrease in resistivity are significantly negatively correlated, that is, the resistivity anomaly can be attributed to the enhanced conductivity of the underground medium caused by pollutant leakage. If the correlation coefficient is less than the second preset value or is positive, it is necessary to further verify its local variation relationship through the spatial trend consistency index to determine whether the anomaly is caused by stratigraphic differences, water content changes or other non-pollution factors.

[0018] Furthermore, the spatial distribution map of pollutant concentration is visualized using three-dimensional isosurfaces, cross-sectional slices, or volume rendering.

[0019] Furthermore, the migration path is quantitatively identified using pollutant concentration gradient calculation and flow direction analysis algorithms. The concentration gradient field is calculated, and when the concentration gradient along a certain direction continues to decrease and is consistent with the groundwater flow direction, it is inferred that the pollution source is located upstream in that direction.

[0020] This invention, by constructing a collaborative system of geophysical exploration and chemical detection, achieves a closed-loop source tracing process from macroscopic anomaly identification to microscopic pollution confirmation, significantly improving the accuracy and efficiency of pollution identification and source locating. Compared with existing technologies, this invention combines the spatial continuity advantages of geophysical methods with the quantitative analysis capabilities of chemical detection, transforming the groundwater pollution source tracing process from a single linear inference to multi-source data fusion analysis. This collaborative mechanism effectively overcomes the problems of multiple interpretations of geophysical results and insufficient spatial representativeness of geochemical results in traditional methods, realizing an integrated process of pollutant identification, chemical verification, and spatial inversion, making the source tracing results more scientific, deterministic, and repeatable.

[0021] This invention further achieves targeted and intelligent pollution identification and sampling point deployment. By guiding sampling point deployment through resistivity anomalies, it not only improves sample representativeness but also significantly reduces the number of blind samplings, duplicate tests, and invalid drillings. This reduces sampling and testing costs and minimizes site disturbance while ensuring analytical accuracy. Spatial collaborative analysis of chemical detection data and resistivity models enables the identification of pollution migration paths and pollution sources to possess visual and quantifiable characteristics, allowing for dynamic tracking and trend prediction of pollutant migration direction, diffusion range, and concentration changes.

[0022] In summary, the technical solution of this invention constructs a complete technical chain of "reconnaissance-location-identification-confirmation" through a geophysical and geochemical exploration collaborative approach, realizing the systematic integration of pollution anomaly identification, chemical verification, spatial interpolation modeling, and pollution source confirmation. This method not only improves the timeliness and spatial resolution of groundwater pollution source tracing but also ensures the scientific rigor and reliability of the results, providing high-precision and low-cost technical support for groundwater pollution prevention and control, risk assessment, and remediation decisions in chemical industrial parks. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is the main flowchart of the present invention. Detailed Implementation

[0025] To make the technical solution of this invention clearer and more complete, the following detailed description of the groundwater pollution tracing method for chemical industrial parks based on geophysical and geochemical exploration is provided in conjunction with specific embodiments. It should be understood that the embodiments described are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0026] In this embodiment, the method is applicable to chemical industrial parks, concentrated chemical industrial zones, or industrial sites with potential pollution sources. By synergistically integrating geophysical exploration technology with chemical detection technology, it achieves accurate identification and scientific tracing of groundwater pollution sources. This method comprehensively utilizes the macroscopic and continuous detection advantages of geophysical methods with the qualitative and quantitative analysis capabilities of chemical detection. It forms a multi-dimensional, multi-modal data verification system within a closed-loop process of anomaly identification—targeted sampling—physicochemical synergy—pollution localization, thereby significantly improving the accuracy and efficiency of groundwater pollution source tracing.

[0027] like Figure 1As shown, the method described in this embodiment generally includes five main stages: regional scanning and anomaly identification, precise targeted sampling design, chemical analysis and data acquisition, physicochemical data collaborative analysis and source tracing diagnosis, and pollution source identification. These stages are sequentially linked, forming a comprehensive analytical chain from macroscopic to microscopic and from qualitative to quantitative analysis.

[0028] Specifically, the steps include the following:

[0029] Geophysical methods are used to explore the target area of ​​the chemical industrial park, obtain underground electrical parameter data, and generate a three-dimensional resistivity model through data inversion to identify and delineate at least one resistivity anomaly area. The resistivity anomaly area is used to characterize the spatial range where underground pollution may exist.

[0030] Based on the spatial morphological characteristics and physical parameter gradient change law of the resistivity anomaly zone, a layout scheme for groundwater chemical sampling target points is determined. The layout scheme includes setting up points at the boundary of the resistivity anomaly zone with the largest resistivity gradient change, setting up points in the central area of ​​the resistivity anomaly, and setting up points upstream, inside and downstream of the resistivity anomaly zone according to the groundwater flow direction.

[0031] Groundwater samples were collected according to the deployment plan and chemically analyzed to obtain data on the types, concentrations, and spatial distribution of pollutants.

[0032] The pollutant data obtained from the chemical detection and analysis are spatially correlated and correlated with the resistivity three-dimensional model. Based on the spatial superposition relationship between the pollutant concentration distribution and the resistivity anomaly area, it is verified whether the cause of the resistivity anomaly is related to the pollutants, so as to eliminate the ambiguity of geophysical methods. Using the resistivity three-dimensional model as the spatial skeleton and the discrete pollutant concentration data as the constraint, a spatial distribution map of pollutant concentration is generated using spatial interpolation algorithms or machine learning algorithms.

[0033] Based on the spatial distribution map of pollutant concentration, the layout of enterprises in the park, and the regional hydrogeological conditions, the migration path of pollutants is identified, the source of pollution is located, and the pollution source tracing results are output.

[0034] This embodiment constructs a multi-level source tracing system encompassing "physical detection—chemical verification—collaborative analysis—pollution identification," achieving a closed-loop technology for the entire process of groundwater pollution control, from anomaly identification to source confirmation. This method fully leverages the continuity advantages of geophysical exploration in macroscopic spatial identification and the precision of chemical detection in qualitative and quantitative pollutant analysis. Through data inversion, spatial interpolation, and multimodal fusion algorithms, it achieves bidirectional constraints and mutual verification of physicochemical information, effectively overcoming the problems of multiple solutions, insufficient representativeness, and low efficiency inherent in traditional single methods. This significantly improves the accuracy, scientific rigor, and repeatability of pollution source tracing, providing a systematic and intelligent technical approach for groundwater pollution prevention and control in chemical industrial parks.

[0035] To further clarify the technical content and implementation process of this invention, the following will provide a detailed description of each step in the above method in conjunction with embodiments. The steps are interconnected and executed sequentially, forming a complete pollution source tracing technical process. Each stage not only undertakes an independent technical task in terms of function, but also forms a sequential correlation and verification at the data and result level, realizing multi-scale fusion analysis from macroscopic detection to microscopic confirmation. Through the systematic decomposition and principle explanation of each step, the innovative mechanisms and technical advantages of this invention in data acquisition, anomaly identification, chemical verification, model collaboration, and pollution diagnosis can be more clearly understood.

[0036] In the process of tracing the source of groundwater pollution, the identification of pollution sources relies on the effective detection of the electrical characteristics of underground media. Different types of pollutants can alter the conductivity of underground media, typically manifesting as significant changes in groundwater resistivity. When soluble organic matter, heavy metal ions, or other highly conductive substances enter the aquifer, it leads to an increase in groundwater ion concentration, thereby reducing resistivity and forming a significant low-resistivity anomaly zone. Therefore, by detecting and modeling the electrical distribution characteristics of underground space, the potential spatial range of pollution can be revealed on a macroscopic scale, providing a targeted basis for subsequent chemical analysis and pollution source identification. To this end, geophysical methods were used to detect the target area of ​​the chemical industrial park, obtain underground electrical parameter data, and generate a three-dimensional resistivity model through data inversion to identify and delineate at least one resistivity anomaly zone, which is used to characterize the spatial range of potential underground pollution.

[0037] Geophysical methods refer to underground detection technologies based on electromagnetic field response characteristics. In this embodiment, a combination of high-density electrical resistivity tomography (EDS) and ground-penetrating radar (GPR) is preferred. High-density EDS involves deploying an electrode array on the surface, injecting current into the underground medium, measuring the corresponding potential difference, and calculating the apparent resistivity distribution data based on Ohm's law, thereby reconstructing the true resistivity distribution of the underground medium. GPR identifies the spatial location and shape of underground targets or structures by emitting high-frequency electromagnetic waves and receiving their reflected signals at different medium interfaces. Resistivity is a physical quantity reflecting the electrical conductivity of underground media, and its magnitude is affected by factors such as porosity, water content, dissolved ion concentration, and the nature of contaminants. Resistivity anomaly zones refer to spatial areas where resistivity values ​​significantly deviate from the regional background values; these zones typically correspond to pathways for contaminant accumulation, leakage, or migration.

[0038] In the specific implementation process, the layout of geophysical survey lines and the scope of survey areas are first determined based on the overall topographic features, geological structure, and potential pollution risk distribution areas of the chemical industrial park. The layout of these survey lines or areas should cover key areas, including production facilities, storage tank areas, wastewater treatment areas, and hazardous waste storage areas—areas with high pollution risk. Simultaneously, it should consider topographic relief, the distribution of surface obstacles, and the direction of underground pipelines to ensure the representativeness and continuity of the acquired data. The direction of the survey lines is preferably consistent with the main flow direction of groundwater to ensure a hydrogeological correspondence when subsequently determining pollution migration paths.

[0039] After the survey line is determined, a multi-channel high-density electrical resistivity electrode array is deployed along the survey line on the ground surface. The electrodes can be made of stainless steel or graphite. The electrode spacing is determined based on the expected detection depth and spatial resolution requirements, generally ranging from 1 to 5 meters. For deeper detection, the spacing can be appropriately increased to improve coverage, while for shallow, fine-grained detection, the spacing can be decreased to enhance resolution accuracy. After the electrodes are deployed, a stable current source is output from the control unit to inject current into the underground medium, and the potential difference between each electrode pair is measured simultaneously to obtain the apparent resistivity data at the corresponding location. Automatic multi-channel switching can be used during the measurement process to improve data acquisition efficiency and spatial coverage density. To eliminate the influence of surface conductors, metal structures, and environmental electromagnetic interference, a reference electrode can be set up during the data acquisition phase for real-time monitoring and signal correction.

[0040] After acquiring apparent resistivity data, the raw data undergoes preprocessing, including outlier removal, noise filtering, normalization, and background value correction. The preprocessed data is then input into the inversion algorithm module for spatial solution. The preferred algorithms are finite element inversion or least squares inversion. Based on the spatial distribution characteristics of the measured data and the conductivity model of the underground medium, the true underground resistivity distribution is obtained. The inversion process gradually reduces computational errors through iterative convergence until the model fit meets a preset accuracy threshold. To ensure the physical rationality of the inversion results, geological constraints or prior model information, such as stratigraphic interface depth, aquifer thickness, and underground pipeline distribution, can be introduced.

[0041] After completing the resistivity inversion calculation, the resulting 3D resistivity model is imported into a visualization platform to generate an electrical distribution map of the underground space. This model can be displayed using isosurface rendering, cross-sectional slicing, or volume rendering to visually demonstrate the resistivity variation characteristics at different depths and in different areas. For key resistivity anomaly areas, the resolution can be further increased and a map of the park's surface facilities can be overlaid, thereby enabling a corresponding analysis of above-ground and underground spaces.

[0042] In the data processing stage, to reduce the impact of signal noise and external interference on resistivity results, a bandpass filtering algorithm can be used to perform frequency domain filtering on the original signal to remove high-frequency interference components and low-frequency drift. Normalization processing is used to ensure that the data scales of different measurement lines are consistent. Background correction methods are used to compare the resistivity results of each measurement point with the regional background electrical parameters to extract relative anomaly features, thereby improving the significance and accuracy of anomaly identification.

[0043] Furthermore, to further enhance the model's resolution in shallow areas, ground-penetrating radar (GPR) technology can be used for supplementary detection in the same region. GPR, by emitting high-frequency electromagnetic pulses and receiving reflected signals from underground, can identify shallow subsurface structures, buried objects, and defects in impermeable layers. Comparing and analyzing GPR reflection profiles with high-density electrical resistivity profiles can effectively correct interpretation errors of shallow electrical anomalies, thereby improving the overall accuracy and reliability of spatial identification.

[0044] By implementing the above steps, the characteristics of underground electrical distribution can be quickly and intuitively grasped without large-scale drilling or excavation, and areas of resistivity anomalies that may exist in groundwater pollution can be identified. This process achieves efficient data acquisition and accurate spatial modeling, providing a scientific basis and spatial guidance for subsequent chemical detection site selection and pollution source tracing.

[0045] For example, when this procedure was applied to a tank area in a chemical industrial park, the coverage area of ​​the survey line was 80 meters × 120 meters, the electrode spacing was 2 meters, and the detection depth was approximately 25 meters. The resulting three-dimensional resistivity model showed that there was a continuously distributed low-resistivity anomaly zone in the northeast direction of the tank area, with a resistivity value approximately 40% lower than the surrounding background value. The morphology of this resistivity anomaly zone exhibited a strip structure extending along the groundwater flow direction.

[0046] After constructing the three-dimensional resistivity model and identifying resistivity anomalies, chemical sampling points need to be deployed in these areas to accurately verify groundwater pollution and identify pollution migration paths. Since groundwater pollutants diffuse and migrate along the aquifer under hydrodynamic forces, their distribution range is closely related to the spatial morphology of the resistivity anomaly area, and the resistivity gradient changes at different locations can reflect the spatial trend of pollutant concentration changes. If the sampling points are not distributed appropriately, it may lead to incomplete pollution identification or inaccurate concentration estimation, thus affecting the accuracy of pollution source location. Therefore, based on the spatial morphological characteristics and physical parameter gradient change patterns of the resistivity anomaly area, a deployment scheme for groundwater chemical sampling targets is determined. This scheme includes deploying points at the boundary of the resistivity anomaly area with the largest resistivity gradient change, at the center of the resistivity anomaly area, and upstream, inside, and downstream of the resistivity anomaly area according to the groundwater flow direction.

[0047] In this embodiment, resistivity gradient refers to the rate at which resistivity changes spatially with location, reflecting the spatial transition characteristics of electrical parameters. The boundary of the resistivity anomaly zone is the area where resistivity changes most significantly; this region often represents the boundary of pollutant concentration or the edge of a pollution plume, serving as the dividing line between pollutant diffusion and uncontaminated areas. The anomaly center region is the spatial location with the lowest or highest resistivity value, typically corresponding to the core area with the highest pollutant concentration or substance enrichment. Groundwater flow direction refers to the tendency of groundwater to move along the hydraulic gradient within a regional aquifer, determining the directionality and rate characteristics of pollutant migration paths. The sampling target deployment scheme is a set of chemical sampling points determined based on the above three spatial characteristics, aiming to achieve optimal spatial coverage of pollution distribution under limited sampling conditions.

[0048] In the specific implementation process, the resistivity 3D model obtained through geophysical inversion is first imported into a spatial analysis system. The spatial analysis system preferably employs a professional geological information processing platform with 3D modeling, attribute extraction, and data analysis capabilities to ensure the integrity of the model data and the accuracy of its spatial geometry. After importation, the resistivity 3D model is meshed, dividing it into multiple voxels, each corresponding to a spatial coordinate position and resistivity attribute value. By performing spatial traversal calculations on the entire model, the 3D spatial boundaries and resistivity gradient distribution information of resistivity anomaly areas are extracted, thus providing a quantitative basis for sampling deployment.

[0049] To accurately determine the boundary of the resistivity anomaly region, the resistivity difference Δρ between adjacent grid cells is calculated in the spatial analysis system, and expressed as the three-dimensional spatial gradient magnitude.

[0050] ,

[0051] As a criterion, ρ represents resistivity, and x, y, and z represent spatial coordinate directions. By screening regions with large gradient magnitudes, spatial locations exhibiting significant resistivity changes are identified and defined as candidate boundary zones. These candidate zones typically correspond to areas where pollutant concentrations change significantly, i.e., the boundary between the pollutant plume and clean water. Based on this, threshold segmentation algorithms or spatial clustering methods can be used to classify and filter candidate zone regions, ultimately determining the actual boundary distribution range of the resistivity anomaly zone.

[0052] After identifying the boundaries of the resistivity anomaly zone, spatial analysis is further performed within the model. Based on the resistivity distribution data, local minimum or maximum resistivity points are extracted as anomaly center regions. For groundwater pollution source tracing, if the pollutant is a highly conductive substance (such as dissolved organic matter, heavy metal ions, etc.), the anomaly center corresponds to the resistivity minimum point; if the pollutant is a low-conductivity substance (such as oil pollutants or non-aqueous liquids), the anomaly center corresponds to the resistivity maximum point. This method can determine the core area of ​​pollution enrichment and provide precise location data for deploying central sampling points.

[0053] Subsequently, the spatial information of the resistivity anomaly zone was overlaid with regional hydrogeological data for analysis. The hydrogeological data included parameters such as aquifer structure, permeability coefficient, groundwater depth, groundwater flow direction, and velocity, which could be derived from existing geological exploration data or long-term monitoring well observations. The main flow direction of groundwater was determined based on the hydraulic gradient of the aquifer, and sampling points were deployed upstream, inside, and downstream of the resistivity anomaly zone along this flow direction. Upstream sampling points were used to obtain uncontaminated control samples to determine the natural background value; internal sampling points were used to identify the chemical composition and concentration changes within the contamination plume; and downstream sampling points were used to observe the migration, diffusion, and attenuation patterns of pollutants along the water flow direction.

[0054] When setting up sampling points, the spatial morphology, size, and groundwater flow velocity of the resistivity anomaly zone should be comprehensively considered. For resistivity anomaly zones with complex shapes or strip-like distribution, sampling points should be laid out along the main axis to trace the pollution migration pathway. For blocky or sheet-like resistivity anomaly zones, a regular grid or ring-shaped sampling method can be used to achieve uniform spatial coverage. The spacing between sampling points is usually determined based on the length of the resistivity anomaly zone and the pollutant diffusion characteristics, generally ranging from 5 to 20 meters, and can be adjusted appropriately as needed in special cases. For areas with significant resistivity gradient changes or complex boundary changes, the sampling density can be appropriately increased to ensure detailed characterization of the pollution transition zone.

[0055] The sampling depth should be determined based on the burial depth and aquifer thickness of the resistivity anomaly zone, through comprehensive geological profile analysis and resistivity isosurface distribution. For shallow resistivity anomaly zones, manual drilling or shallow well sampling can be used; for deep contaminated bodies, mechanical drilling or existing monitoring wells can be used for stratified sampling. To ensure the reliability and comparability of sampling results, the geographic coordinates, elevation, sampling depth, stratigraphic lithology, and water level information of each sampling point should be recorded. During sample collection, strict adherence to cross-contamination prevention procedures is required, using pre-treated sampling bottles, disposable tubing, and standardized sampling procedures. Collected water samples should be immediately sealed, numbered, and sent to the laboratory for chemical analysis within the specified time.

[0056] After the sampling deployment is completed, a visualization system can be used to perform spatial checks and model verification on the distribution of sampling points. The sampling point locations are superimposed on the 3D resistivity model for visual comparison, ensuring that the sampling points cover areas with significant resistivity gradient changes and typical anomaly centers, avoiding concentrated or missed sampling points. For areas with uneven or overlapping sampling points, the sampling scheme can be re-optimized by adjusting the algorithm until the sampling distribution meets the requirements of spatial representativeness and data balance.

[0057] By implementing the above steps, a one-to-one correspondence was achieved between chemical sampling points and the spatial distribution of physical anomalies, enabling precise comparison and spatial verification of chemical detection results with resistivity models. This deployment method effectively overcomes the randomness and blindness of traditional sampling point placement, significantly improving the representativeness and interpretation accuracy of the data.

[0058] The sampling deployment scheme of this invention not only achieves a systematic transition from physical anomaly identification to chemical verification, but also establishes a unified data correlation between macroscopic structure and microscopic components, thereby significantly improving the scientific rigor, systematicity, and repeatability of the pollution source tracing process. This scheme, through targeted sampling, reduces the number of redundant samples, shortens the sampling and detection cycle, lowers overall costs, and ensures a complete spatial representation of pollutant concentration changes, providing high-precision data support for pollution diffusion pattern analysis and the formulation of subsequent remediation measures.

[0059] In the aforementioned example of a storage tank area in a chemical industrial park, a spatial analysis system was used to process the low-resistivity anomaly zone, extracting the area where the gradient modulus was greater than the threshold of 0.25 Ω·m / m as the boundary zone. The boundary zone was approximately 45 meters long and 8 meters wide. At the center of this area, a point of minimum resistivity (approximately 12 Ω·m) was identified, and a central sampling point was established based on this. Based on the characteristic of groundwater flow from northeast to southwest, sampling points were established upstream, in the middle, and downstream of the anomaly zone, with additional dense sampling points on both sides of the boundary zone. A total of 12 sampling points were ultimately established: 2 upstream, 4 central, 3 boundary, and 3 downstream.

[0060] The spatial distribution of groundwater pollutants depends not only on the emission characteristics of pollution sources but also on the combined influence of multiple factors, including groundwater flow, hydrogeological conditions, and stratigraphic pore structure. While resistivity anomalies can macroscopically reflect the spatial extent of potential pollution, they cannot directly reveal the chemical composition and concentration variations of pollutants. To accurately identify the causes of pollution, it is necessary to collect groundwater samples in the field and conduct chemical analysis to confirm and characterize pollutants from both qualitative and quantitative perspectives. Chemical analysis results can not only verify the authenticity of physical anomalies but also reveal the types, concentrations, and distribution characteristics of different pollutants in the underground space, thus providing data support for pollution migration path analysis and pollution source identification. Therefore, groundwater samples were collected according to the aforementioned deployment plan, and chemical analysis was performed to obtain data on pollutant types, concentrations, and spatial distribution.

[0061] In this embodiment, groundwater samples refer to representative water samples collected from underground aquifers or monitoring wells, used to detect the physicochemical indicators of dissolved or suspended pollutants in the water. Chemical detection and analysis refers to the qualitative identification and quantitative determination of pollutants in groundwater samples using laboratory instrument analysis methods, including the detection of inorganic ions, organic compounds, and heavy metal elements. Pollutant type refers to the actual type of pollutant present in the sample, concentration refers to the numerical content of pollutants in the sample, and spatial distribution data refers to the spatial variation of pollutant concentration at different sampling points and depths.

[0062] In the specific implementation process, groundwater samples are first collected at designated sampling points according to the established sampling layout plan. Before sampling, the sampling port should be cleaned and the equipment rinsed to avoid interference from historical residual water samples on the test results. For sampling using boreholes or monitoring wells, the stagnant water in the well should be pumped out first until the water quality parameters (such as temperature, conductivity, and pH value) stabilize before sampling. The sampling depth is determined based on the aforementioned burial depth of the resistivity anomaly zone, and stratified sampling can be performed at different depths to reveal the vertical distribution characteristics of pollutants.

[0063] During sample collection, appropriate containers and preservation methods should be selected according to the type of pollutant. For example, for volatile organic compound samples, sealed glass bottles should be used for preservation and transportation under low temperature conditions; for heavy metal samples, nitric acid stabilizers can be added to prevent precipitation or adsorption; for nutrient salts and anionic samples, polyethylene bottles can be used for preservation in the dark. All samples should be immediately numbered, and the sampling time, location, elevation, and water level information should be recorded, and a sampling record form should be filled out.

[0064] After sampling, the samples are sent to a qualified laboratory for chemical testing and analysis. The testing items can be determined based on the industrial park's process type and potential pollutant types, generally including heavy metals (such as Cr, Pb, Cd, Hg), organic matter (such as benzene compounds, chlorinated hydrocarbons, phenols), and routine physicochemical indicators (such as COD, NH3-N, TDS). Standard methods such as gas chromatography, ion chromatography, atomic absorption spectrometry, or inductively coupled plasma mass spectrometry can be used. The test data are entered into the database after quality control and repeatability verification. To ensure data accuracy, parallel samples, blank samples, and spiked recovery samples can be set up for quality comparison.

[0065] During the data processing phase, the detection results from all sampling points are subjected to spatial coordinate matching and normalization to form a spatial dataset of pollutant concentrations. By mapping the spatial locations of these datasets to resistivity anomaly areas, a preliminary distribution map of pollutant concentrations can be generated, providing input data for subsequent collaborative analysis of physicochemical data. If the pollutant concentrations at some sampling points are found to deviate significantly from those at neighboring points or background values, data verification or supplementary sampling should be performed.

[0066] By implementing the above steps, groundwater pollution can be accurately verified at the chemical level, achieving a seamless transition from electrical anomaly identification to chemical pollution confirmation. This process not only reveals the compositional characteristics and distribution patterns of pollutants but also provides quantitative evidence for subsequent pollution migration modeling and pollution source diagnosis.

[0067] This step significantly improves the scientific rigor and reliability of pollution source tracing results. The cross-verification of chemical analysis and geophysical exploration results helps confirm whether low resistivity anomalies are indeed caused by increased pollutant concentrations, avoiding misinterpretation of geological structural differences or aquifer changes as pollution anomalies. Obtaining high-precision pollutant concentration data provides direct support for analyzing pollutant migration patterns and conducting risk assessments, enabling a shift from qualitative speculation to quantitative analysis in groundwater pollution identification.

[0068] For example, in the aforementioned example of a storage tank area in a chemical industrial park, groundwater samples were collected from 12 sampling points. Laboratory testing and analysis revealed that the concentration of benzene compounds was the highest in the samples from the low-resistivity central area, reaching 1.8 mg / L, while the concentration in the downstream samples was 1.1 mg / L. No related pollutants were detected in the upstream samples.

[0069] In tracing the source of groundwater pollution, resistivity anomalies obtained solely through geophysical methods can only characterize the electrical differences in the underground medium. They cannot directly distinguish whether the anomaly is caused by pollutants or by variations in stratigraphic structure, water content, or mineral composition. To verify the true cause of resistivity anomalies, it is necessary to spatially correlate and jointly analyze pollutant concentration data obtained from chemical detection with a three-dimensional resistivity model. By overlaying and correlating these two types of data, the consistency between electrical anomalies and pollutant concentration changes can be identified. When areas of high pollutant concentration and areas of low resistivity anomalies show significant spatial overlap, it can be determined that the electrical anomaly originates from pollutant leakage. Conversely, if their spatial distributions are inconsistent, further investigation into the influence of geological structures or hydrological conditions on resistivity changes is required. To achieve this goal and realize a continuous and visual representation of the spatial distribution of pollutants, it is necessary to use a three-dimensional resistivity model as the spatial framework, with discrete pollutant concentration data as constraints, and generate a spatial distribution map of pollutant concentrations using spatial interpolation algorithms or machine learning algorithms. To this end, the pollutant data obtained from the chemical detection and analysis are spatially correlated and linked with the resistivity three-dimensional model. Based on the spatial superposition relationship between the pollutant concentration distribution and the resistivity anomaly area, it is verified whether the cause of the resistivity anomaly is related to the pollutants, so as to eliminate the ambiguity of geophysical methods. Using the resistivity three-dimensional model as the spatial skeleton and the discrete pollutant concentration data as the constraint, a spatial distribution map of pollutant concentration is generated using spatial interpolation algorithms or machine learning algorithms.

[0070] In this embodiment, spatial location correspondence refers to matching the geographic coordinates and sampling depth of the chemical detection sample points with the grid coordinates in the resistivity 3D model, so that the two types of data have a unified spatial reference system. Spatial overlay relationship refers to the projection distribution of pollutant concentration values ​​in the resistivity model space and its geometric relationship with low-resistivity anomalies. Spatial interpolation algorithm refers to a mathematical method for inferring continuous spatial distribution based on finite discrete sample data, which can employ inverse distance weighting, Kriging interpolation, or radial basis function methods. Machine learning algorithms may include support vector regression (SVR), random forest regression (RF), or neural network models, which achieve a nonlinear mapping between pollutant concentration and spatial electrical characteristics through sample learning.

[0071] In the specific implementation process, the pollutant concentration data obtained from chemical detection and analysis are first imported into a spatial analysis system. This system is used to perform unified spatial mapping, fusion, and calculation of data from different sources and at different precision levels. The system possesses geographic information modeling, data registration, attribute analysis, and 3D visualization functions, supporting comprehensive calculation and dynamic display of multi-source heterogeneous data within the same coordinate system. During the import process, the geographic coordinates (X, Y, elevation Z), detection index name, pollutant concentration value, and detection time of each sampling point are established as attribute fields, forming a structured database. To ensure the spatiotemporal consistency of the data, the data undergoes unit unification, time format standardization, and coordinate system unification during import. This database forms the basis for subsequent spatial registration, interpolation, and statistical analysis.

[0072] The aforementioned three-dimensional resistivity model is imported into the same spatial analysis system, and its coordinate system is converted to a spatial reference coordinate system consistent with the sampled data. The three-dimensional resistivity model is a voxelized data structure generated by high-density electrical resistivity inversion, where each voxel contains spatial coordinate attributes and a corresponding resistivity value. Through model resampling and spatial alignment, the resolution of the resistivity model is matched to the spatial accuracy of the sampling points, ensuring that the spatial projection error between different types of data does not exceed a set threshold (typically less than 0.5 meters). After model reconstruction, the sampling point positions are mapped onto the three-dimensional resistivity model, and the resistivity unit corresponding to each sampling point is marked by a spatial index, forming a data correspondence.

[0073] To ensure accurate spatial registration of the two types of data, a spatial registration algorithm is employed for coordinate alignment and geometric matching. This algorithm includes a geometric correction step based on nearest-neighbor interpolation and a residual correction step based on least-squares optimization. It automatically identifies the offset between the sampling point location and the model unit center point and makes minor adjustments to minimize spatial matching errors between different data sources. After registration, a bidirectional mapping table of chemical concentration and resistivity values ​​is established to provide a data foundation for subsequent correlation analysis.

[0074] After registration, a bivariate statistical analysis was performed on the pollutant concentration at each sampling point and the resistivity value of its corresponding resistivity unit to calculate the correlation coefficient R and the trend consistency index S. The correlation coefficient R was calculated using the Pearson correlation coefficient formula:

[0075] ,

[0076] Among them, C i Let ρ be the pollutant concentration at the i-th sampling point. i C represents the resistivity of the cell corresponding to the sampling point. - With ρ - These represent the average concentration and resistivity of all sample points, respectively. If the R value is negative and its absolute value is close to 1, it indicates a significant negative correlation between increased pollutant concentration and decreased resistivity, meaning the resistivity anomaly can be attributed to increased conductivity of the underground medium caused by pollutant leakage. If the R value is small or positive, further verification of its local variation relationship is needed using the spatial trend consistency index S to determine whether the anomaly is caused by stratigraphic differences, water content changes, or other non-polluting factors.

[0077] After completing the bivariate analysis, the system performs spatial interpolation or prediction calculations on the entire resistivity 3D model based on validated pollutant concentration data. Using the resistivity 3D model as a spatial skeleton, the pollutant concentration values ​​at each sampling point are taken as known node inputs. A spatial interpolation algorithm is used to estimate the concentration values ​​at unsampled locations, thus forming a continuous concentration distribution field. Inverse distance weighted (IDW) is a commonly used spatial interpolation algorithm, based on the assumption that "the closer the distance, the more similar the properties." Its calculation formula is as follows:

[0078] ,

[0079] Where C(x) is the concentration value at the predicted point, C i Let d be the concentration at the i-th sampling point. i Let p be the Euclidean distance between the predicted point and the i-th sampling point, and p be the distance weighting exponent, typically ranging from 1 to 3. A larger p value indicates a stronger dependence of the prediction result on nearby sampling points; a smaller p value results in a smoother prediction. To prevent excessive influence from distant points, a maximum effective radius r can be set. max Only consider distances less than r from the prediction point. max Interpolation calculations are performed on the sample points.

[0080] For complex situations involving a large number of samples and nonlinear changes in pollution distribution, a spatial prediction model based on machine learning can be employed. This machine learning model can establish a mapping relationship between pollution concentration and geophysical characteristics in a multidimensional input space. Input features may include spatial coordinates (X, Y, Z), resistivity value ρ, formation thickness h, hydrological permeability coefficient k, etc., with the output variable being the pollutant concentration C. Training is performed using algorithms such as Support Vector Regression (SVR) or Random Forest Regression (RF), utilizing known sampling point data to learn the variation patterns of pollutant concentration, thereby predicting the concentration in unknown areas. This method can significantly improve the nonlinear fitting ability of spatial prediction, and is particularly suitable for pollution plume edge transition zones and multi-source complex pollution scenarios.

[0081] After spatial interpolation and prediction calculations are completed, the system generates a spatial distribution map of pollutant concentrations. This map is visualized using 3D isosurfaces, cross-sectional slices, or volume rendering, providing a clear view of the pollutant distribution range, migration direction, and enrichment areas in underground space. To enhance the interpretability of the results, the concentration distribution map can be spatially overlaid with resistivity anomaly areas, using color, transparency, and hierarchical control to distinguish different attribute data. When high-concentration areas and low-resistivity areas spatially overlap, the system automatically labels them as "significantly coupled pollution areas"; if they do not match, they are labeled as "non-polluting resistivity anomaly areas," indicating the need for on-site verification or additional sampling.

[0082] In the analysis results, if the resistivity anomaly range is significantly larger than the concentration distribution area, it can be inferred that the pollution is still in the early stage of diffusion, and the pollutants have not yet fully migrated along the groundwater flow direction; if the concentration distribution range exceeds the resistivity anomaly range, it indicates that the pollution plume has expanded outward or that new leakage points exist. Through this comparison, the stage, direction, and potential diffusion risk of pollutants can be inferred.

[0083] This step enabled spatial coupling analysis and two-way verification of geophysical and chemical detection data, transforming pollution source tracing analysis from a singular interpretation of electrical anomalies to a physicochemical collaborative inference model. This process not only effectively eliminated the ambiguity of geophysical methods but also enhanced the certainty and scientific rigor of anomaly interpretation. The generated spatial distribution map of pollutant concentrations provides continuous spatial data support for pollution migration simulation, pollution source inversion, and risk assessment, achieving quantitative, visual, and intelligent diagnosis of groundwater pollution.

[0084] The spatial correlation and modeling method of this invention can complete the three-dimensional reconstruction of the concentration field without increasing the sampling density, significantly improving data utilization and source tracing efficiency. This method is repeatable, verifiable, and quantifiable, possessing good engineering feasibility and providing a scientific basis for pollution prevention and control decisions.

[0085] For example, in the aforementioned example of a storage tank area in a chemical industrial park, benzene series compound concentration data from 12 sampling points were imported into the system along with a three-dimensional resistivity model for spatial overlay analysis. After registration, the system calculated a correlation coefficient R = -0.87, indicating a significant negative correlation between increased benzene series compound concentration and decreased resistivity. Further analysis was performed using an inverse distance weighting method (p = 2, r...). max A pollution concentration distribution map was generated using a random forest model (30m). The results showed that pollutants were distributed in stripes along the groundwater flow direction, with high concentration areas highly consistent with areas of low resistivity anomalies. The three-dimensional concentration field obtained by predicting the same area using the random forest model differed from the interpolation results by less than 5%, verifying the stability and accuracy of the model.

[0086] Resistivity anomalies and pollutant concentration distribution can reveal the spatial characteristics of underground pollutants. However, to ultimately pinpoint the source of pollution, it is necessary to combine the layout of enterprises within the industrial park, the distribution of discharge points, and regional hydrogeological conditions to comprehensively analyze the migration paths and causal characteristics of pollutants. The migration of pollutants in underground media typically follows the direction of groundwater flow and is influenced by factors such as stratum permeability, aquifer dip angle, and recharge and discharge conditions. When pollutants form a high-concentration extension zone along the main groundwater flow direction during migration, its upstream starting point is highly likely to correspond to the location of the pollution source. Relying solely on the spatial distribution of pollutant concentration cannot distinguish between multiple sources or historical residues. Therefore, it is essential to introduce multi-dimensional constraints based on enterprise distribution and hydrological elements to achieve accurate source tracing. To this end, by combining the aforementioned spatial distribution map of pollutant concentration, the layout of enterprises within the industrial park, and regional hydrogeological conditions, pollutant migration paths are identified, pollution sources are located, and pollution source tracing results are output.

[0087] In this embodiment, the spatial distribution map of pollutant concentration refers to a three-dimensional visualization model of pollutant concentration generated based on a three-dimensional resistivity model and chemical detection results. This model represents the enrichment degree and migration direction of pollutants in underground space. The layout of enterprises within the chemical industrial park refers to the geographical location, main production processes, and emission characteristics of each enterprise, used to analyze the spatial distribution of potential pollution sources. Regional hydrogeological conditions, including groundwater flow direction, aquifer thickness, permeability coefficient, hydraulic gradient, and stratigraphic structure information, are important constraint parameters for pollution migration path analysis. The pollutant migration path refers to the dynamic channel formed by the diffusion and migration of pollutants from their source to the surrounding groundwater system.

[0088] In the specific implementation process, the spatial distribution map of pollutant concentration is first imported into the spatial analysis system, and then overlaid with a layer showing the distribution of enterprises in the industrial park and a regional hydrogeological layer. To achieve multi-source data fusion, spatial coordinate unification, attribute field matching, and hierarchical overlay techniques are adopted to display data from different sources in a unified three-dimensional coordinate system. The system initially determines the main migration axis of pollutants by identifying the extension direction of pollutant concentration contour lines, and verifies the consistency of the migration direction based on groundwater flow data. If the extension direction of the high-concentration pollutant area is consistent with the mainstream groundwater flow direction, then this direction can be identified as the main channel for pollution migration.

[0089] Subsequently, the migration path was quantitatively identified using pollutant concentration gradient calculation and flow direction analysis algorithms. The concentration gradient G is defined as...

[0090] G = ΔC / ΔL,

[0091] Where G is the concentration gradient, ΔC is the pollutant concentration difference between adjacent sampling points or model units, and ΔL is the spatial distance between the two points. By calculating the concentration gradient field, the changing trend of pollutants along the migration path can be identified. When the concentration gradient along a certain direction continuously decreases and is consistent with the groundwater flow direction, it can be inferred that the pollution source is located upstream in that direction. Furthermore, combining the formation permeability coefficient k and the hydraulic gradient i, based on the groundwater flow velocity formula...

[0092] v = k × i,

[0093] Where v is the groundwater flow velocity, it can be used to estimate the migration speed and time scale of pollutants, providing a basis for spatiotemporal inference of pollution processes.

[0094] After identifying the direction of pollution migration, the system filters potential pollution sources by combining information on the layout of enterprises in the industrial park. The system searches for enterprise nodes with discharge facilities, storage tank areas, or production units upstream of the high-concentration pollutant distribution zone, and performs matching analysis based on pollutant type and enterprise production process. For example, when benzene compounds, chlorinated hydrocarbons, or phenolic compounds are detected, the system prioritizes matching enterprises using organic solvents or chlorination processes. When the matching results show that an enterprise's production substances match the detected pollutant types, and its geographical location is upstream of the pollution plume, the enterprise can be preliminarily identified as a suspected pollution source.

[0095] To improve the accuracy of the results, the system can further overlay historical monitoring data, accident records, or wastewater discharge logs for cross-validation. If there are historical events such as tank leaks, pipeline ruptures, or excessive wastewater discharge in the area, the validation results will be given higher weight. Finally, the system comprehensively determines the location of the pollution source based on the spatial overlay results, concentration gradient direction, enterprise process type, and historical records, and marks the pollution source coordinates and migration path lines in the form of icons on the visualization interface. The system automatically generates a pollution source tracing report, including the pollution source location, pollutant type, concentration change trend, migration direction, and predicted diffusion range, providing management departments with intuitive decision-making basis.

[0096] This step enables the spatial reconstruction of pollutant migration processes, achieving a complete correlation from pollutant identification to pollution source locating. By integrating physical models, chemical data, and hydrogeological information, this method forms a multi-dimensional cross-validation mechanism, effectively eliminating biases caused by inferences from single data sources and enhancing the scientific rigor and credibility of pollution source tracing results.

[0097] The pollution migration path identification and source identification method of this invention can significantly improve the spatial resolution and quantitative accuracy of groundwater pollution tracing, achieving a closed-loop diagnosis from "pollution detection" to "source determination." This method can not only quickly identify the direction of pollutant diffusion, but also infer the pollutant migration process based on concentration change trends and hydrodynamic conditions, forming a traceable and verifiable chain of evidence, providing technical support for subsequent pollution control, risk assessment, and regulatory evidence collection.

[0098] For example, in the aforementioned example of a chemical industrial park's tank area, the system overlays and analyzes the benzene series compound concentration distribution map with data on the park's enterprise layout and groundwater flow direction. The results show that the high-concentration benzene series compound extension zone is aligned with the groundwater flow direction, extending approximately 60 meters from northeast to southwest. Its upstream area corresponds to a chemical enterprise's tank area using aromatics as feedstock. Concentration gradient calculations indicate that the concentration gradually decreases along the migration direction, with a gradient of approximately 0.012 mg / L·m³. -1 Based on the formation permeability coefficient of 2.3 × 10⁻⁶ -5 Based on the calculated groundwater velocity (m / s) and hydraulic gradient of 0.015, the groundwater flow velocity is approximately 3.45 × 10⁻⁶ m / s. -7 The flow rate is m / s, corresponding to a pollutant migration cycle of approximately six months. Combined with the company's historical wastewater discharge records, it was confirmed that an underground leakage event had occurred in the tank area, ultimately identifying the area beneath the company's tank foundations as the primary source of pollution. The results of this implementation validate the applicability and high-precision source tracing capabilities of this method in complex industrial scenarios.

[0099] The prior art mentioned in the foregoing background and specific embodiments sections can be considered as part of this invention and used to understand the meaning of some technical features or parameters.

Claims

1. A method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration, characterized in that, Includes the following steps: Geophysical methods are used to explore the target area of ​​the chemical industrial park, obtain underground electrical parameter data, and generate a three-dimensional resistivity model through data inversion to identify and delineate at least one resistivity anomaly area. The resistivity anomaly area is used to characterize the spatial range where underground pollution may exist. Based on the spatial morphological characteristics and physical parameter gradient change law of the resistivity anomaly zone, a layout scheme for groundwater chemical sampling target points is determined. The layout scheme includes setting up points at the boundary of the resistivity anomaly zone with the largest resistivity gradient change, setting up points in the central area of ​​the resistivity anomaly, and setting up points upstream, inside and downstream of the resistivity anomaly zone according to the groundwater flow direction. Groundwater samples were collected according to the deployment plan and chemically analyzed to obtain data on the types, concentrations, and spatial distribution of pollutants. The pollutant data obtained from the chemical detection and analysis are spatially correlated and correlated with the resistivity three-dimensional model. Based on the spatial superposition relationship between the pollutant concentration distribution and the resistivity anomaly area, it is verified whether the cause of the resistivity anomaly is related to the pollutants, so as to eliminate the ambiguity of geophysical methods. Using the resistivity three-dimensional model as the spatial skeleton and the discrete pollutant concentration data as the constraint, a spatial distribution map of pollutant concentration is generated using spatial interpolation algorithms or machine learning algorithms. A bivariate statistical analysis was performed on the pollutant concentration at each sampling point and the resistivity value of its corresponding resistivity unit to calculate the correlation coefficient R and the trend consistency index S. If the R value is negative and its absolute value is close to 1, it indicates a significant negative correlation between the increase in pollutant concentration and the decrease in resistivity, meaning that the resistivity anomaly can be attributed to the enhanced conductivity of the underground medium caused by pollutant leakage. If the R value is small or positive, it is necessary to further verify its local variation relationship through the spatial trend consistency index S to determine whether the anomaly is caused by stratigraphic differences, water content changes, or other non-polluting factors. Based on the verified pollutant concentration data, spatial interpolation or prediction calculations were performed on the entire three-dimensional resistivity model. Using the three-dimensional resistivity model as a spatial skeleton, the pollutant concentration values ​​at each sampling point were used as known node inputs, and the concentration values ​​at unsampled locations were estimated through a spatial interpolation algorithm, thereby forming a continuous concentration distribution field. Based on the spatial distribution map of pollutant concentration, the layout of enterprises in the park, and the regional hydrogeological conditions, the migration path of pollutants is identified, the source of pollution is located, and the pollution source tracing results are output.

2. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The geophysical method described uses a combination of high-density electrical resistivity tomography (EDT) and ground-penetrating radar (GPR).

3. The method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The data inversion incorporates constraints on the depth of the stratigraphic interface, the thickness of the aquifer, and the distribution of underground pipelines.

4. The method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The identification of the resistivity anomaly region is achieved by calculating the resistivity gradient of adjacent grid cells, and the region where the gradient magnitude exceeds a set threshold is taken as the anomaly boundary region.

5. The method for tracing the source of groundwater pollution in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The layout of groundwater chemical sampling targets is determined in conjunction with groundwater flow direction information, and sampling profiles are laid out along the main flow direction of groundwater.

6. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 5, characterized in that, The spacing between groundwater chemical sampling target points is determined based on the degree of resistivity gradient change. When the gradient change is drastic, the sampling density is increased, and when the resistivity anomaly zone has a regular shape, a regular grid layout is used.

7. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, Spatial registration is performed during the spatial location correspondence and correlation analysis. A coordinate matching algorithm based on least squares optimization is used to ensure that the spatial deviation between the chemical sampling point and the resistivity model unit does not exceed a preset value.

8. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 7, characterized in that, After registration, a bivariate statistical analysis was performed on the pollutant concentration at each sampling point and the resistivity value of its corresponding resistivity unit to calculate the correlation coefficient and trend consistency index. If the correlation coefficient is negative and the difference between its absolute value and 1 is less than the first preset value, it indicates that the increase in pollutant concentration and the decrease in resistivity are significantly negatively correlated, that is, the resistivity anomaly can be attributed to the enhanced conductivity of the underground medium caused by pollutant leakage. If the correlation coefficient is less than the second preset value or is positive, it is necessary to further verify its local variation relationship through the spatial trend consistency index to determine whether the anomaly is caused by stratigraphic differences, water content changes or other non-pollution factors.

9. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The spatial distribution map of pollutant concentration is visualized using three-dimensional isosurfaces, cross-sectional slices, or volume rendering.

10. The method for tracing groundwater pollution sources in chemical industrial parks based on geophysical and geochemical exploration synergy as described in claim 1, characterized in that, The migration path is quantitatively identified using pollutant concentration gradient calculation and flow direction analysis algorithms. The concentration gradient field is calculated, and when the concentration gradient along a certain direction continues to decrease and is consistent with the groundwater flow direction, it is inferred that the pollution source is located upstream in that direction.

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