Groundwater pollution plume source identification method and system based on three-dimensional dynamic monitoring

Abstract

The application discloses a groundwater pollution plume source tracing identification method and system based on three-dimensional dynamic monitoring, relates to the technical field of data processing, and comprises the following steps: constructing a three-dimensional dynamic model of a groundwater pollution plume by acquiring groundwater monitoring data and stratum monitoring data, and predicting the migration of the pollution plume to obtain a predicted pollution plume state and an initial pollution source inversion position; identifying an underground influence type by performing difference analysis on a real-time pollution plume state and the predicted pollution plume state; dynamically adjusting underground monitoring parameters based on the underground influence type, re-updating the three-dimensional dynamic model of the groundwater pollution plume, and realizing dynamic correction of the migration process of the groundwater pollution; and obtaining a new pollution source area by combining historical pollution and new pollution analysis and reverse tracking of the pollution source. The application can reduce the influence of changes in a groundwater flow field and changes in a groundwater medium structure on the results of pollution source tracing, and improve the accuracy of pollution source tracing in a complex underground environment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for tracing and identifying groundwater pollution plumes based on three-dimensional dynamic monitoring. Background Technology

[0002] Groundwater pollution is characterized by its high degree of concealment, wide spread, and complex migration process. When industrial wastewater, landfill leachate, or hazardous waste leaks, the pollutants will continue to spread in the underground space with the flow of groundwater, and will form a complex pollution distribution area due to the influence of the underground medium structure and the flow state of groundwater.

[0003] Traditional monitoring methods often employ a planar sampling approach with single-well, single-pump sampling, lacking the ability to perceive the vertical layers of aquifers. This makes it difficult to capture the penetration and diffusion behavior of pollution plumes between multiple aquifers, leading to "spatial misalignment" in source tracing analysis. Furthermore, existing source identification methods are often based on the assumption of isotropic ideal media, which is insufficient for simulating complex geological backgrounds with strong heterogeneity (such as fracture development and lithological abrupt changes), making it difficult to handle multi-source contribution identification and dynamic location of sudden pollution events. Therefore, existing technologies suffer from low accuracy in pollution source tracing results due to the influence of changes in the underground flow field and underground media structure on the migration process of underground pollution plumes. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for tracing and identifying groundwater pollution plumes based on three-dimensional dynamic monitoring.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] In a first aspect, this invention discloses a method for tracing and identifying groundwater pollution plumes based on three-dimensional dynamic monitoring, comprising the following steps: acquiring groundwater monitoring data and stratigraphic monitoring data at different spatial locations in a target area, and analyzing these data to construct a three-dimensional dynamic model of the groundwater pollution plume; predicting the migration of the groundwater pollution plume based on the three-dimensional dynamic model to obtain the predicted plume state and the initial pollution source inversion location; continuously acquiring real-time groundwater monitoring data and analyzing it to obtain the real-time plume state; performing a difference analysis between the real-time plume state and the predicted plume state within a first preset time period to obtain the pollution migration anomaly analysis result; when the pollution migration anomaly analysis result indicates that there is no pollution migration anomaly, the initial pollution source inversion location is taken as a suspected pollution source. If the underground impact parameter set is not obtained, a coupling correlation analysis of the underground impact parameter set is performed to identify the corresponding underground impact type. If the underground impact type is underground state stable, an early warning is issued and real-time underground monitoring is maintained. Otherwise, the underground monitoring parameters are dynamically adjusted based on the underground impact type, and the groundwater monitoring data is re-acquired after adjustment to update the three-dimensional dynamic model of the underground pollution plume, thus obtaining each suspected pollution source area. Historical pollution and new pollution analysis are performed on the corresponding areas of each suspected pollution source area in the second preset time period to obtain historical residual pollution areas and new pollution source areas. Based on the underground pollution plume dynamic migration path corresponding to the new pollution source area, the pollution source is traced backward to obtain the new pollution source area.

[0007] Secondly, this invention discloses a groundwater pollution plume source tracing and identification system based on three-dimensional dynamic monitoring, comprising the following modules: a data processing module, used to acquire groundwater monitoring data and stratum monitoring data at different spatial locations in the target area, and analyze them to construct a three-dimensional dynamic model of the groundwater pollution plume; a prediction and inversion module, used to acquire the current underground flow field state and combine it with the three-dimensional dynamic model of the groundwater pollution plume to predict the migration of the groundwater pollution plume, obtaining the predicted pollution plume state and the inversion location of the initial pollution source; a pollution migration anomaly analysis module, used to continuously acquire real-time groundwater monitoring data and analyze it to obtain the real-time pollution plume state; perform difference analysis between the real-time pollution plume state and the predicted pollution plume state within a first preset time period to obtain the pollution migration anomaly analysis result; and an impact type analysis module, used to, when the pollution migration anomaly analysis result indicates that there is no pollution migration anomaly, invert the initial pollution source location. The system identifies suspected pollution source areas. Otherwise, it acquires a set of underground fluctuation impact parameters and performs coupling correlation analysis on these parameters to identify the corresponding underground impact type. The suspected pollution source area analysis module issues an early warning and maintains real-time underground monitoring when the underground impact type indicates a stable underground state; otherwise, it dynamically adjusts underground monitoring parameters based on the underground impact type and re-acquires groundwater monitoring data to update the three-dimensional dynamic model of the underground pollution plume, thus obtaining each suspected pollution source area. The pollution source location confirmation module performs historical and new pollution analysis on the corresponding areas of each suspected pollution source area within a second preset time period, obtaining historical residual pollution areas and new pollution source areas. The new pollution source area confirmation module performs reverse tracking of pollution sources based on the dynamic migration path of the underground pollution plume corresponding to the new pollution source area, thus obtaining the new pollution source area.

[0008] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0009] 1. This invention constructs a three-dimensional dynamic model of underground pollution plumes by acquiring groundwater monitoring data and stratum monitoring data at different spatial locations in the target area, and performs underground pollution plume migration prediction. Then, it combines real-time groundwater monitoring data with the predicted pollution plume status for difference analysis, thereby realizing real-time correction and anomaly identification of the dynamic migration process of underground pollution plumes. This enables dynamic inversion and accurate source tracing of underground pollution sources, effectively solving the problem of low accuracy of pollution source tracing results caused by changes in underground flow field and underground medium structure in the underground pollution plume migration process in existing technologies.

[0010] 2. By identifying different types of underground influences, this invention enables the classification, identification, and dynamic analysis of the causes of abnormal underground pollution plume migration, thereby improving the ability to identify abnormal migration states of underground pollution plumes and enhancing the stability of underground pollution migration process analysis.

[0011] 3. This invention analyzes the historical and new pollution in each suspected pollution source area and performs reverse tracking of pollution sources based on the dynamic migration path of underground pollution plumes corresponding to the new pollution source area. This effectively distinguishes between historical residual pollution areas and new pollution source areas, thereby improving the accuracy of new pollution source identification results and effectively solving the problem of historical residual pollution interfering with the judgment of new pollution sources in the prior art. Attached Figure Description

[0012] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0013] Figure 1 This is the overall flowchart of the groundwater pollution plume dynamic monitoring and source tracing of the present invention;

[0014] Figure 2 This is a flowchart of the pollution migration anomaly analysis of the present invention;

[0015] Figure 3 This is a flowchart of the underground impact type identification and dynamic monitoring adjustment process of the present invention;

[0016] Figure 4 This is a system architecture diagram of the present invention. Detailed Implementation

[0017] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0018] Most existing groundwater pollution source tracing technologies are based on static monitoring data analysis, which makes it difficult to reflect the dynamic migration process of underground pollution plumes under the conditions of changes in underground flow field and underground media structure. This can easily lead to increased prediction errors in pollution plume migration and insufficient accuracy in locating pollution sources.

[0019] This invention constructs a three-dimensional dynamic model of underground pollution plumes by acquiring groundwater monitoring data and stratigraphic monitoring data at different spatial locations in the target area, and predicts underground pollution plume migration based on the three-dimensional dynamic model. By continuously acquiring real-time groundwater monitoring data and performing difference analysis between the real-time pollution plume state and the predicted pollution plume state, abnormal pollution migration situations can be identified. When abnormal pollution migration exists, the type of underground impact is identified based on the underground fluctuation impact parameter set, and the underground monitoring parameters are dynamically adjusted and the three-dimensional dynamic model of underground pollution plumes is updated. By combining historical pollution and new pollution analysis and reverse inference of pollution propagation paths, the identification of new pollution source areas can be achieved.

[0020] like Figure 1 The diagram shows the overall flowchart of the groundwater pollution plume dynamic monitoring and source tracing method of the present invention. The method includes the following steps: acquiring groundwater monitoring data and stratum monitoring data at different spatial locations in the target area, and analyzing them to construct a three-dimensional dynamic model of the groundwater pollution plume; predicting the migration of the groundwater pollution plume based on the three-dimensional dynamic model to obtain the predicted pollution plume state and the initial pollution source inversion location; continuously acquiring real-time groundwater monitoring data and analyzing it to obtain the real-time pollution plume state; performing a difference analysis between the real-time pollution plume state and the predicted pollution plume state within a first preset time period to obtain the pollution migration anomaly analysis result; when the pollution migration anomaly analysis result indicates that there is no pollution migration anomaly, the initial pollution source inversion location is taken as the suspected pollution source area. If the underground wave impact parameter set is obtained, the coupling correlation change analysis of the underground wave impact parameter set is performed to identify the corresponding underground impact type. If the underground impact type is underground state stable, an early warning is issued and real-time underground monitoring is maintained. Otherwise, the underground monitoring parameters are dynamically adjusted based on the underground impact type, and the groundwater monitoring data is re-acquired after adjustment to update the three-dimensional dynamic model of the underground pollution plume and obtain each suspected pollution source area. Historical pollution and new pollution analysis are performed on the corresponding areas of each suspected pollution source area in the second preset time period to obtain historical residual pollution areas and new pollution source areas. Based on the underground pollution plume dynamic migration path corresponding to the new pollution source area, the pollution source is traced backward to obtain the new pollution source area.

[0021] In this embodiment, it should be noted that an underground pollution plume refers to a contaminated area formed by pollutants migrating and spreading with the water flow after entering the groundwater body.

[0022] The initial pollution source inversion location refers to the coordinates of the suspected source calculated and simulated using groundwater monitoring data and stratum monitoring data from the initially established three-dimensional dynamic model of the underground pollution plume.

[0023] The three-dimensional dynamic model of the underground pollution plume is updated by rearranging the underground monitoring points based on the adjusted sampling interval distance to obtain updated spatial sampling data.

[0024] Dynamic sampling was performed based on the adjusted groundwater flow velocity sampling frequency and deep contamination concentration detection frequency to obtain updated time-series monitoring data; underground structures were re-scanned based on the adjusted resistivity tomography resolution and vertical scanning depth to obtain updated underground structure data; and a three-dimensional dynamic model of the underground contamination plume was reconstructed based on the updated spatial sampling data, updated time-series monitoring data, and updated underground structure data.

[0025] Furthermore, a three-dimensional dynamic model of the underground pollution plume is constructed. The specific method is as follows: groundwater monitoring data at different spatial locations in the target area are acquired, including pollutant concentration values, groundwater flow velocity values, and groundwater flow direction angles; stratigraphic monitoring data at different spatial locations in the target area are acquired, including stratigraphic resistivity values, seismic wave propagation velocity values, and stratigraphic pore pressure values; the groundwater monitoring data and stratigraphic monitoring data are then fused in three dimensions to obtain the three-dimensional dynamic model of the underground pollution plume.

[0026] In this embodiment, pollutant concentration values ​​can be obtained in real time using an online water quality analyzer by deploying groundwater sampling probes at different depths using a layered monitoring well structure. Groundwater flow velocity values ​​are obtained using a thermal pulse groundwater velocity meter. Groundwater flow direction angle values ​​are obtained using a groundwater flow direction meter combined with a three-dimensional spatial coordinate system. Formation resistivity values ​​are obtained using resistivity tomography, specifically by injecting low-frequency current into the underground medium and collecting the potential difference between different electrodes to calculate the resistivity of the underground medium. Seismic wave propagation velocity values ​​are obtained using the shallow seismic reflection method, specifically by using a seismic wave excitation device to emit P-waves underground and obtaining the seismic wave propagation time using a seismic detector, thereby calculating the seismic wave propagation velocity value based on the propagation distance. Formation pore pressure values ​​can be measured in real time using a pore pressure sensor.

[0027] A three-dimensional dynamic model of the underground pollution plume is obtained by fusing groundwater monitoring data and formation monitoring data in three dimensions. The specific method involves establishing a three-dimensional spatial coordinate system based on pollutant concentration values ​​at different depths underground, mapping these concentration values ​​to their corresponding spatial coordinates, and creating the three-dimensional dynamic model of the underground pollution plume. All pollutant concentration values ​​at these different depths are actual measured data. In other words, the three-dimensional dynamic model of the underground pollution plume is established based on data obtained from actual monitoring. Specifically, the method for establishing the three-dimensional spatial coordinate system based on pollutant concentration values ​​at different depths underground involves mapping formation monitoring data and groundwater monitoring data from different spatial locations in the target area to the underground three-dimensional spatial coordinate system. This system uses east-west, north-south, and depth coordinates. Then, a three-dimensional fusion process is performed, dividing the underground space into multiple voxel units. Each voxel unit corresponds to a pollutant concentration value, groundwater flow velocity value, groundwater flow direction angle, formation resistivity value, seismic wave propagation velocity value, and formation pore pressure value, thus obtaining the three-dimensional dynamic model of the underground pollution plume.

[0028] Furthermore, underground pollution plume migration prediction is performed to obtain the predicted plume state and the initial pollution source inversion location. The specific method is as follows: extracting the plume diffusion boundary data and pollution concentration diffusion trend data from the three-dimensional dynamic model of the underground pollution plume; extracting groundwater dynamic migration direction data based on the current underground flow field state data; predicting the underground pollution plume migration path based on the plume diffusion boundary data, pollution concentration diffusion trend data, and groundwater monitoring data to obtain the initial pollution diffusion direction and initial pollution migration velocity; performing pollution source reverse path analysis based on the initial pollution diffusion direction and initial pollution migration velocity to obtain the predicted plume state and the initial pollution source inversion location; the predicted plume state includes the initial plume boundary data and the initial pollution diffusion depth.

[0029] In this embodiment, the pollution plume diffusion boundary data refers to the spatial boundary area corresponding to the pollutant concentration value reaching the preset pollution concentration threshold. The specific acquisition method is as follows: spatial interpolation analysis is performed based on the pollutant concentration values ​​at different underground sampling points. When the pollution concentration value is greater than the pollution concentration threshold, the corresponding area is marked as a polluted area. The pollution plume boundary is obtained by using a three-dimensional isosurface extraction method, wherein the spatial interpolation analysis adopts the inverse distance weighted interpolation method.

[0030] Pollution concentration diffusion trend data refers to the trend of pollutant concentration values ​​over time. Pollution concentration diffusion trend data is obtained through statistical analysis of pollutant concentration changes over continuous time periods.

[0031] Based on the current underground flow field data, groundwater dynamic migration direction data can be extracted. This can be achieved using a vector field construction method based on Darcy's Law. The physical vector direction of groundwater-driven pollutant movement is determined by monitoring the water level gradient. Specifically, the water level (obtainable via a water level gauge) is acquired at each monitoring point within the target area using three monitoring wells arranged in a triangular pattern. The hydraulic gradient vector is then calculated using a plane analytical method.

[0032] ;

[0033] In the formula, Represents the hydraulic gradient vector. This indicates the groundwater level height obtained from the monitoring well. This indicates the spatial coordinates of the monitoring point in the geographic coordinate system. Represents the unit vectors in the horizontal and vertical directions.

[0034] The direction of the hydraulic gradient vector is defined as the direction of groundwater dynamic migration data:

[0035] ;

[0036] In the formula, This indicates data on the direction of groundwater dynamic migration.

[0037] Based on pollution plume diffusion boundary data, pollution concentration diffusion trend data, and groundwater monitoring data, the migration path of the groundwater pollution plume is predicted to obtain the initial pollution diffusion direction and initial pollution migration velocity. The specific method is as follows:

[0038] ;

[0039] In the formula, This indicates the groundwater flow velocity value. Indicates the initial migration rate of contaminants. The retardation factor represents a known constant determined by formation pore pressure and medium parameters, reflecting the influence of formation adsorption on pollutants.

[0040] Concentration gradient vector constructed using pollution concentration diffusion trend data The initial pollution diffusion direction is obtained by weighted fusion with the hydrodynamic direction:

[0041] ;

[0042] ;

[0043] In the formula, Represents the concentration gradient vector. Indicates the initial direction of pollution diffusion. Indicates the pollutant concentration value. This indicates that the concentration gradient points in the direction of the fastest decrease in concentration. Indicates data on the direction of groundwater dynamic migration. Indicates the weight of the direction of groundwater dynamic migration. This indicates the weight of the concentration diffusion direction.

[0044] The weights for groundwater dynamic migration direction and concentration diffusion direction represent the proportions of contribution from "flow-driven" and "self-diffusion" to pollution migration. These can be obtained by fitting historical monitoring data using the Pearson correlation coefficient method. Specifically, historical monitoring data within the first preset time period is retrieved to obtain the actual displacement vector, and then the correlation coefficient between the actual displacement direction and the groundwater dynamic migration direction is calculated. And calculate the correlation coefficient between the actual displacement direction and the concentration gradient direction. Specifically:

[0045] ;

[0046] ;

[0047] in, The larger the value, the more consistent the movement trajectory of the pollution plume is with the direction of groundwater flow. The larger the value, the more the model leans towards "convective migration". The larger the value, the more likely the pollutant diffuses primarily through concentration gradient in still water or low-flow-rate environments. As the size increases, the model becomes more biased towards "diffuse diffusion".

[0048] Based on the initial pollution diffusion direction and initial pollution migration velocity, reverse path analysis of the pollution source is performed to obtain the predicted pollution plume state and the inverted location of the initial pollution source. The inverse evolution analysis using the random walk method can be employed. By combining the initial pollution migration velocity and initial pollution diffusion direction with the pollution plume diffusion boundary data, forward integration is performed on the time axis to calculate the positions of each characteristic point of the pollution plume within the first preset time period.

[0049] ;

[0050] In the formula, Indicates the characteristic points of the pollution plume at The coordinates in three-dimensional space at a given time. Indicates the initial coordinates of the feature points of the pollution plume. Indicates the initial migration rate of contaminants. Indicates the initial direction of pollution diffusion. The unit direction vector of the initial pollution diffusion direction. Since the groundwater flow velocity and migration velocity are dynamic and change dynamically at different times, the integration process needs to iterate from 0 to... Every tiny time segment between The displacements within these segments are accumulated.

[0051] The location of the initial pollution source can be determined by following the initial pollution diffusion direction. The opposite direction (i.e.) ), based on the initial pollution migration rate The backtracking is performed using a step size, and the backtracking terminates when the pollution concentration diffusion trend data at the backtracking point shows that the concentration has reached a local maximum, and the underground media structure data (such as the formation permeability coefficient) at that location allows for the contaminant to be present. In this case, the coordinate is marked as the initial pollution source inversion location. The specific formula is as follows:

[0052] ;

[0053] In the formula, Indicates the initial pollution source inversion location, This indicates the location of the centroid of the pollution plume as currently monitored in real time. Indicates the initial migration rate of contaminants. This indicates the time elapsed from the estimated start time of emissions to the current monitoring time. Indicates the initial direction of pollution diffusion. The unit direction vector of the initial pollution diffusion direction.

[0054] The method for obtaining the predicted initial pollution plume boundary data is as follows: assuming the currently monitored set of boundary points is... The predicted boundary is the envelope of all boundary points after displacement:

[0055] ;

[0056] In the formula, The union of all predicted point locations forms a closed prediction boundary curve.

[0057] The predicted initial pollution diffusion depth is determined by taking the component of the predicted location vector on the vertical coordinate axis (Z-axis).

[0058] ;

[0059] In the formula, Indicates the initial depth of pollution diffusion. This indicates the vertical coordinates of the deepest point of the current plume. This represents the vertical migration velocity component.

[0060] By extracting pollution plume diffusion boundary data and pollution concentration diffusion trend data from the three-dimensional dynamic model of the underground pollution plume, and combining it with the current underground flow field state data to extract groundwater dynamic migration direction data, the underground pollution plume migration path can be predicted based on the pollution plume diffusion boundary data, pollution concentration diffusion trend data, and groundwater monitoring data. This enables dynamic prediction and analysis of pollutant migration direction, migration speed, and diffusion depth. Furthermore, based on the initial pollution diffusion direction and initial pollution migration speed, reverse path analysis of the pollution source can be performed, enabling early location of the initial pollution source inversion location. This effectively solves the problems of existing technologies that rely solely on static monitoring data for pollution source identification, cannot combine underground flow field changes to predict the dynamic migration process of the pollution plume, and result in delayed pollution source location and low source tracing accuracy.

[0061] By jointly analyzing pollution plume diffusion boundary data and pollution concentration diffusion trend data, the spatial diffusion range of the pollution plume and the evolution trend of pollution concentration can be reflected simultaneously. This allows pollution migration path prediction to not only describe the current location of pollutants but also reflect changes in the future diffusion direction of the pollution plume. By introducing groundwater dynamic migration direction data, the influence of changes in the underground flow field direction on the pollution migration path can be coupled into the prediction process, thereby avoiding path bias problems that occur when tracing sources solely based on pollution concentration distribution. By performing reverse path analysis of pollution sources based on the initial pollution diffusion direction and initial pollution migration velocity, the time inversion relationship of the pollution plume migration process can be used to perform inverse convergence analysis of the pollution source formation area, thereby improving the accuracy of identifying the initial location of the pollution source.

[0062] Furthermore, the results of pollution migration anomaly analysis are obtained. Specifically, the following methods are used: updating and acquiring the pollution plume diffusion boundary data and pollution concentration diffusion trend data at the actual preset location, and comparing them with the predicted values ​​at the corresponding preset location using the underground pollution plume 3D dynamic model, to obtain the pollution plume boundary offset distance; based on the underground pollution plume 3D dynamic model analysis, obtaining the predicted pollution migration velocity at the preset location; performing depth offset analysis based on real-time pollution diffusion depth data and predicted pollution diffusion depth data to obtain the pollution plume depth offset value; acquiring the real-time pollution plume boundary position at each time point within the first preset time period, and performing real-time migration path change analysis to obtain the real-time pollution concentration migration velocity; performing migration deviation analysis based on the real-time pollution concentration migration velocity and predicted pollution migration velocity to obtain the pollution migration velocity change rate; and based on the pollution plume... Anomaly correlation analysis was performed on boundary offset distance, plume depth offset value, and the rate of change of plume migration velocity to obtain the results of the plume migration anomaly analysis. When the plume boundary offset distance is greater than a preset threshold and the plume depth offset value is greater than a preset threshold, the result of the plume migration anomaly analysis is that there is an influence of underground structural anomalies. When the rate of change of plume migration velocity is greater than a preset threshold and the plume migration direction deflection angle is greater than a preset threshold, the result of the plume migration anomaly analysis is that there is an influence of underground flow field anomalies. Otherwise, the result of the plume migration anomaly analysis is that there are no plume migration anomalies. The results of the plume migration anomaly analysis showing the presence of underground structural anomalies and the presence of underground flow field anomalies are jointly marked as the presence of plume migration anomalies.

[0063] In this embodiment, as Figure 2 As shown, Figure 2 The flowchart of the pollution migration anomaly analysis of the present invention includes acquiring actual pollution plume diffusion boundary data, predicting pollution plume diffusion boundary data, pollution migration velocity and pollution plume depth offset value, and identifying the influence of underground structural anomalies or underground flow field anomalies through anomaly correlation analysis, so as to improve the ability to identify anomalies in the dynamic migration of underground pollution plumes.

[0064] The preset location refers to the key monitoring well point selected in the 3D model, which serves as the benchmark anchor point for comparative analysis. The plume boundary offset distance is used to measure the degree to which the plume deviates from the expected trajectory in the horizontal direction, and the plume depth offset value is used to measure the degree to which the plume deviates from the expected depth in the vertical direction (vertical migration).

[0065] The specific steps to obtain the plume boundary offset distance are as follows: Obtain the plume boundary at its actual location through real-time monitoring, and extract the predicted boundary with the same time step from the 3D dynamic model, using the average Euclidean distance of the point-to-point set.

[0066] ;

[0067] In the formula, Indicates the offset distance of the contamination plume boundary. This indicates the coordinates of the boundary points monitored in real time. This represents the coordinates of the corresponding boundary points predicted by the 3D dynamic model. The number representing the coordinates of the boundary point. , This represents the total number of boundary points.

[0068] Based on the analysis of a three-dimensional dynamic model of underground pollution plumes, the predicted pollution migration velocity at preset locations is obtained. The specific method is as follows:

[0069] ;

[0070] In the formula, This indicates the predicted rate of pollution migration. This indicates the groundwater flow velocity value. Indicates the formation pore pressure value. Indicates the formation compressibility coefficient. This represents the total formation pressure load.

[0071] The formation compressibility coefficient is obtained by matching the seismic wave propagation velocity value in the formation monitoring data with the known formation type manual. The total formation pressure load can be obtained by using the theoretical reference value of the total pressure of the overlying strata and fluid at a preset location, and is a static constant calculated from the monitoring well depth and the average formation density.

[0072] The pollution plume depth offset value is obtained by subtracting the predicted depth value output by the three-dimensional dynamic model of the underground pollution plume from the measured value of the pollution diffusion depth obtained by the real-time monitoring probe, and taking the absolute value.

[0073] To obtain the real-time pollution concentration migration rate, the specific method is as follows: within the first preset time period, extract... and The positional change of the centroid (concentration-weighted center) of the polluted plume at constant time:

[0074] ;

[0075] In the formula, Indicates the real-time migration rate of pollutant concentration. Indicates in Constantly polluting the feather's center of mass. Indicates in It constantly pollutes the center of the feather.

[0076] The method for obtaining the rate of change in pollution migration velocity is as follows:

[0077] ;

[0078] In the formula, Indicates the rate of change in the speed of pollution migration. This indicates the predicted rate of pollution migration. This indicates the real-time migration rate of pollution concentration.

[0079] Analyzing the boundary offset distance of a pollution plume can reflect the degree of diffusion offset of the plume in planar space. When the boundary offset distance increases, it indicates that the underground medium structure may have changed, leading to abnormal expansion or contraction of the pollution plume diffusion boundary. Analyzing the depth offset value of a pollution plume can reflect the abnormal migration of pollutants during vertical infiltration. When the depth offset value increases, it indicates that the interlayer permeability of the underground medium has changed or that there are cracks in the underground structure. Analyzing the rate of change of pollution migration velocity can reflect the influence of changes in underground flow field dynamics on pollution migration velocity. When the rate of change of pollution migration velocity increases, it indicates that there are abnormal changes in the driving force of the underground flow field, which causes the pollution migration path to deviate from the original predicted path. This approach does not analyze each parameter in isolation, but rather performs a correlation analysis of the plume boundary offset distance, plume depth offset, and the rate of change of plume migration velocity. This is because anomalies in underground structure and anomalies in underground flow field exhibit different characteristics during plume migration: when the underground structure changes, the plume boundary offset distance and plume depth offset usually increase simultaneously, while the rate of change of plume migration velocity does not change significantly; when the underground flow field undergoes anomaly changes, the rate of change of plume migration velocity and the deflection angle of plume migration direction increase significantly, but the plume boundary offset distance and plume depth offset may not change synchronously. Therefore, by jointly analyzing the interrelationships between multiple parameters, it is possible to distinguish whether plume migration anomalies originate from changes in underground media structure or changes in underground flow field, avoiding the problem of misjudging anomaly types caused by traditional single-parameter analysis.

[0080] Furthermore, the corresponding subsurface impact types are identified. Specifically, the following method is used: First, a set of subsurface wave impact parameters for the current time point is obtained. This set includes seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area. Second, the subsurface wave impact parameter set is analyzed against corresponding historical benchmark monitoring data to obtain the rates of change for seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area. Third, a formation compression correlation analysis is performed based on the rates of change for seismic wave propagation velocity and pollution diffusion velocity. If the rate of change for seismic wave propagation velocity is greater than a threshold value, and the rate of change for pollution diffusion velocity is less than the pollution diffusion velocity threshold value, then the subsurface wave propagation velocity is considered a threshold value. When the rate of change of resistivity is less than the threshold, the subsurface impact type is subsurface medium compression enhancement; based on the formation resistivity change rate and the pollution plume diffusion area change rate, a formation loosening correlation analysis is performed. When the formation resistivity change rate is less than the formation resistivity change rate threshold and the pollution plume diffusion area change rate is greater than the pollution plume diffusion area change rate threshold, the subsurface impact type is subsurface medium loosening enhancement; based on the pollution diffusion velocity change rate and the pollution plume diffusion area change rate, a pollution migration diffusion correlation analysis is performed. When the pollution diffusion velocity change rate is greater than the pollution diffusion velocity change rate threshold and the pollution plume diffusion area change rate is greater than the pollution plume diffusion area change rate threshold, the subsurface impact type is rapid pollution plume migration; otherwise, the subsurface impact type is subsurface state stability.

[0081] In this embodiment, the seismic wave propagation velocity can be measured using an acoustic transducer, and the contamination diffusion velocity can be calculated using the real-time displacement increment based on the centroid of contaminant concentration. The formation resistivity can be measured using a pore water resistivity sensor. The contamination plume diffusion area can be calculated by performing a closed integral on the contamination plume boundary of the three-dimensional model projected onto a horizontal plane.

[0082] The rates of change of seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area were obtained by performing absolute difference processing on the set of underground wave influence parameters and historical benchmark monitoring data (which includes historical benchmark seismic wave propagation velocity, historical benchmark pollution diffusion velocity, historical benchmark formation resistivity, and historical benchmark pollution plume diffusion area). The absolute difference was then divided by the corresponding historical benchmark monitoring data. For example, the specific calculation method for the rate of change of seismic wave propagation velocity is as follows:

[0083] ;

[0084] In the formula, This represents the rate of change of the propagation velocity of seismic waves. This represents the seismic wave propagation velocity value at the current time point. This represents the historical baseline seismic wave propagation velocity value.

[0085] By analyzing the relationship between the rate of change of seismic wave propagation velocity and the rate of change of pollution diffusion velocity, we can identify the state of enhanced compression of the subsurface medium. This is because when the stratum is compressed and enhanced, the density of the subsurface medium increases, the seismic wave propagation velocity increases, and the diffusion capacity of pollutants in the subsurface pores decreases. Therefore, this manifests as an increase in the rate of change of seismic wave propagation velocity and a decrease in the rate of change of pollution diffusion velocity. By analyzing the relationship between the rate of change of formation resistivity and the rate of change of pollution plume diffusion area, we can identify the state of enhanced loosening of the subsurface medium. This is because when the stratum structure becomes loose, the number of underground water-bearing channels increases, the formation resistivity decreases, and the pollution plume diffusion area increases. Therefore, this manifests as a decrease in the rate of change of formation resistivity and an increase in the rate of change of pollution plume diffusion area. By analyzing the relationship between the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area, we can identify the state of rapid migration of the pollution plume. This is because when the driving force of the underground flow field increases, both the pollution migration velocity and the pollution diffusion range increase simultaneously. Therefore, this manifests as a synchronous increase in the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area.

[0086] Furthermore, the underground monitoring parameters are dynamically adjusted based on the type of underground impact. Specifically, if the underground impact type is enhanced compression of the underground medium, a joint analysis is performed based on the rate of change of seismic wave propagation velocity and the rate of change of pollution diffusion velocity to obtain the comprehensive impact value of underground medium compression. This comprehensive impact value is then used to match the sampling interval adjustment value and the seismic wave scanning frequency adjustment value at monitoring points, thereby reducing the sampling interval distance at monitoring points and increasing the seismic wave scanning frequency. If the underground impact type is enhanced loosening of the underground medium, the rate of change of formation resistivity is matched with a preset formation loosening response rule to obtain the pollution plume scanning range adjustment value and the velocity sampling frequency adjustment value, thereby increasing the pollution plume scanning range and / or increasing the velocity sampling frequency. If the underground impact type is enhanced rapid pollution plume... For rapid migration, the difference ratios of the pollution diffusion velocity change rate and the pollution plume diffusion area change rate with the corresponding thresholds are analyzed to obtain the corresponding diffusion velocity difference ratios and diffusion area difference ratios. The diffusion velocity difference ratios and diffusion area difference ratios are then used to perform diffusion trend analysis to obtain diffusion trend determination results. The diffusion trend determination results include rapid vertical penetration of pollution, slow horizontal diffusion of pollution, and rapid overall diffusion of pollution. Based on the diffusion trend determination results, diffusion detection is adjusted, including adjustments to the flow direction update cycle, monitoring point coverage density, and scanning range. The underground monitoring parameters include the monitoring point sampling interval distance, seismic wave scanning frequency, pollution plume scanning range, flow velocity sampling frequency, flow direction update cycle, monitoring point coverage density, and scanning range.

[0087] In this embodiment, preset reference values ​​for the rate of change of seismic wave propagation velocity and the rate of change of pollution diffusion velocity are obtained from the database. These are then combined with the rates of change of seismic wave propagation velocity and the rate of change of pollution diffusion velocity to obtain the comprehensive impact value of underground medium compression. The specific formula for obtaining the comprehensive impact value of underground medium compression is as follows:

[0088] ;

[0089] In the formula, This represents the comprehensive impact value of underground medium compression. This represents the rate of change of the propagation velocity of seismic waves. This represents the reference value for the rate of change of seismic wave propagation velocity. Indicates the rate of change in the speed of pollution diffusion. This represents a reference value for the rate of change in the pollution diffusion speed. The weights represent the influence of the rate of change of seismic wave propagation velocity. The weighting represents the impact of the rate of change in the pollution diffusion speed.

[0090] The influence weights of the seismic wave propagation velocity change rate and the pollution diffusion velocity change rate can be obtained through database analysis. For example, data on the seismic wave propagation velocity change rate, pollution diffusion velocity change rate, and corresponding actual pollution plume migration results for historical subsurface media change events can be obtained. A subsurface media compression influence database can be established based on historical pollution plume migration results, recording different seismic wave propagation velocity change rates, different pollution diffusion velocity change rates, and the corresponding subsurface media compression degrees. Historical data can be statistically graded according to the subsurface media compression degree, and the contribution ratio of each parameter to the pollution plume migration degree can be analyzed. Principal component analysis can be used to calculate the contribution of the seismic wave propagation velocity change rate and the pollution diffusion velocity change rate. The calculated contribution results can be normalized to obtain the influence weights of the seismic wave propagation velocity change rate and the pollution diffusion velocity change rate. Specifically, the more significant the influence of the seismic wave propagation velocity change rate on the subsurface media compression result, the greater its influence weight; similarly, the more significant the influence of the pollution diffusion velocity change rate on the pollution plume migration change, the greater its influence weight.

[0091] The adjustment values ​​for the sampling interval and seismic wave scanning frequency of monitoring points are obtained based on the matching of the comprehensive influence value of underground medium compression. The specific method is as follows: The current comprehensive influence value of underground medium compression is obtained, and the underground monitoring parameter adjustment mapping table in the database is called. The underground monitoring parameter adjustment mapping table is established based on monitoring experimental data from historical underground collapse areas, underground medium compression areas, and areas with abnormal pollution migration. It records the optimal sampling interval adjustment value and the optimal seismic wave scanning frequency adjustment value corresponding to different comprehensive influence values ​​of underground medium compression. The current comprehensive influence value of underground medium compression is normalized and matched to the corresponding underground medium compression influence interval. The target interval location of the current comprehensive influence value of underground medium compression is determined. If the current comprehensive influence value of underground medium compression is located between two historical intervals, the corresponding sampling interval adjustment value and seismic wave scanning frequency adjustment value are calculated using linear interpolation.

[0092] The sampling interval distance of monitoring points is reduced and the seismic wave scanning frequency is increased. Specifically, the current sampling interval distance of monitoring points is reduced by subtracting the adjustment value of the sampling interval distance of monitoring points to obtain the reduced sampling interval distance of monitoring points, and the current seismic wave scanning frequency is increased by adding the adjustment value of the seismic wave scanning frequency to obtain the increased seismic wave scanning frequency.

[0093] The plume scanning range adjustment value and the flow velocity sampling frequency adjustment value are obtained by matching the formation resistivity change rate with a preset formation loose response rule. Specifically, the current formation resistivity change rate is compared with a preset formation resistivity change rate threshold. If the formation resistivity change rate is less than the threshold, the plume scanning range adjustment value (including length, width, and depth adjustment values) is obtained based on the formation resistivity change rate. In this case, the flow velocity sampling frequency adjustment value is zero (i.e., no increase in flow velocity sampling frequency is needed). If the formation resistivity change rate is above the formation resistivity change rate threshold, the preset maximum value of the plume scanning range is used as the plume scanning range adjustment value. The formation resistivity change rate threshold is subtracted from the formation resistivity change rate to obtain the formation resistivity change rate difference. The plume scanning range adjustment value is then obtained based on the formation resistivity change rate. Specifically, the current formation resistivity change rate is obtained, and the plume scanning range adjustment mapping table in the database is called. The pollution plume scanning range adjustment mapping table is established based on historical experimental data on changes in underground media loosening. It records the optimal pollution plume scanning range adjustment value corresponding to different rates of change in formation resistivity. The current rate of change in formation resistivity is normalized and mapped to the corresponding historical variation interval. The target interval location of the current rate of change in formation resistivity is determined. If the current rate of change in formation resistivity is between two historical intervals, the corresponding pollution plume scanning range adjustment value is calculated using linear interpolation. Based on the calculated pollution plume scanning range adjustment value, the underground pollution plume monitoring area is dynamically expanded. Specifically, the smaller the rate of change in formation resistivity, the more significant the change in the water-bearing channels or fracture structures within the underground media, indicating a higher degree of loosening of the underground media. Pollutants are more likely to diffuse laterally along the loose underground areas. Therefore, it is necessary to increase the pollution plume scanning range to improve the coverage of the lateral diffusion boundary of the underground pollution plume, avoiding incomplete identification of the pollution plume diffusion path due to insufficient monitoring area, thereby improving the accuracy of subsequent reverse tracing of pollution sources.

[0094] The flow velocity sampling frequency adjustment value is obtained based on the difference in the formation resistivity change rate. Specifically, the method is as follows: Obtain the difference between the current formation resistivity change rate and the historical stable-state formation resistivity change rate; call the flow velocity sampling frequency adjustment mapping table in the database. This table is established based on historical underground permeability enhancement experimental data and records the optimal flow velocity sampling frequency adjustment value corresponding to different formation resistivity change rate differences; normalize the current formation resistivity change rate difference (normalization aims to eliminate the influence of units) and map it to the corresponding historical difference interval; if the current formation resistivity change rate difference is between two historical intervals, linear interpolation is used to calculate the corresponding flow velocity sampling frequency adjustment value; a larger formation resistivity change rate difference indicates a more significant change in the conductivity of the underground medium, usually indicating a change in the underground aquifer structure or underground seepage channels. Therefore, it is necessary to increase the flow velocity sampling frequency to enhance the capture capability of dynamic changes in the underground flow field.

[0095] Increase the plume scanning range and / or increase the velocity sampling frequency. Specifically, add the plume scanning range adjustment value to the current plume scanning range, and simultaneously add the velocity sampling frequency adjustment value to the current velocity sampling frequency (the velocity sampling frequency adjustment value can be zero, i.e., not increased), as the adjusted plume scanning range and velocity sampling frequency.

[0096] The corresponding diffusion rate difference ratio and diffusion area difference ratio are obtained by the following methods: the absolute difference between the pollution diffusion rate change rate and the preset pollution diffusion rate change rate threshold is processed to obtain the absolute difference of the pollution diffusion rate change rate; the absolute difference of the pollution diffusion rate change rate is divided by the pollution diffusion rate change rate threshold to obtain the diffusion rate difference ratio; the absolute difference between the pollution plume diffusion area change rate and the preset pollution plume diffusion area change rate threshold is processed to obtain the absolute difference of the pollution plume diffusion area change rate; the absolute difference of the pollution plume diffusion area change rate is divided by the pollution plume diffusion area change rate threshold to obtain the diffusion area difference ratio.

[0097] The diffusion trend determination result is obtained as follows: The diffusion speed difference ratio and diffusion area difference ratio are normalized and matched to obtain the diffusion speed difference influence value and diffusion area difference influence value, respectively. The absolute difference between the diffusion speed difference influence value and the diffusion area difference influence value is processed to obtain the diffusion difference value. This diffusion difference value is compared with a preset diffusion difference threshold. If the diffusion difference value is less than or equal to the diffusion difference threshold, the diffusion trend determination result is rapid overall pollution diffusion; otherwise, the diffusion speed difference influence value and the diffusion area difference influence value are compared. If the diffusion speed difference influence value is greater than the diffusion area difference influence value, the diffusion trend determination result is rapid vertical penetration of pollution; if the diffusion speed difference influence value is less than the diffusion area difference influence value, the diffusion trend determination result is slow horizontal diffusion of pollution.

[0098] The method involves normalizing and matching the proportions of differences in diffusion velocity and diffusion area to obtain the impact values ​​of diffusion velocity difference and diffusion area difference. Specifically, the proportions of differences in diffusion velocity and diffusion area are obtained separately, and then normalized using a minimum-maximum normalization method to unify data of different dimensions within the same analytical range. A diffusion impact database is established based on historical rapid diffusion events of pollution plumes, recording different proportions of differences in diffusion velocity, diffusion area, and their corresponding actual pollution diffusion intensities. The current normalized proportions of differences in diffusion velocity and diffusion area are then matched with historical data in the database to obtain the impact values ​​of diffusion velocity difference and diffusion area difference. A larger proportion of differences in diffusion velocity indicates a more abnormal advance speed of the pollution plume front, thus resulting in a larger impact value for diffusion velocity difference. Similarly, a larger proportion of differences in diffusion area indicates a more pronounced lateral diffusion range of the pollution plume, also resulting in a larger impact value for diffusion area difference.

[0099] Furthermore, diffusion detection adjustments are made as follows: If the diffusion trend indicates rapid vertical penetration of pollution, the interlayer spacing of vertical monitoring points is adjusted based on the diffusion rate difference ratio. This reduces the interlayer spacing of vertical monitoring points by subtracting the adjusted value from the current interlayer spacing. If the diffusion trend indicates slow horizontal diffusion of pollution, the boundary sampling resolution is adjusted based on the diffusion area difference ratio. This increases the boundary sampling resolution by adding the adjusted value to the current resolution. If the diffusion trend indicates rapid overall diffusion of pollution, a comprehensive diffusion impact value is obtained based on a combined analysis of the diffusion rate difference ratio and the diffusion area difference ratio. The diffusion monitoring sampling frequency is then adjusted based on this comprehensive impact value. This increases the sampling frequency by adding the adjusted value to the current frequency. Data is then updated accordingly.

[0100] By adjusting the sampling interval of monitoring points and the seismic wave scanning frequency under conditions of enhanced compression of the underground medium, the ability to identify structural changes in dense underground regions can be improved, thereby enabling precise monitoring of compressed underground medium regions. By adjusting the scanning range of the pollution plume and the flow velocity sampling frequency under conditions of enhanced loose underground medium, the coverage of loose and infiltrated regions can be expanded and the ability to acquire changes in the underground flow field can be improved. By analyzing the proportion of differences in diffusion velocity and diffusion area under conditions of rapid migration of the pollution plume, the pollution diffusion trend can be identified and the monitoring strategy can be dynamically adjusted, thereby enabling continuous tracking of rapidly migrating pollution plumes.

[0101] In this embodiment, as Figure 3 As shown, Figure 3 This is a flowchart of the underground impact type identification and dynamic monitoring adjustment process of the present invention. The flowchart describes the underground impact type identification and dynamic monitoring adjustment process, including the acquisition of underground fluctuation impact parameter set, underground impact type identification, and corresponding dynamic adjustment of monitoring parameters, which is used to improve the adaptability of underground pollution plume migration process monitoring and the accuracy of underground pollution source tracing results.

[0102] The interlayer spacing adjustment value of vertical monitoring points is obtained based on the diffusion velocity difference ratio matching. The specific method is as follows: obtain the current diffusion velocity difference ratio and call the vertical monitoring parameter adjustment mapping table in the database; the vertical monitoring parameter adjustment mapping table is established based on historical pollution longitudinal penetration experimental data, which records the optimal vertical monitoring point interlayer spacing adjustment value corresponding to different diffusion velocity difference ratios; normalize the current diffusion velocity difference ratio and map it to the corresponding historical interval; if the current diffusion velocity difference ratio is between two historical intervals, the corresponding vertical monitoring point interlayer spacing adjustment value is calculated by linear interpolation; where, when the diffusion velocity difference ratio is larger, it indicates that the pollution plume has a stronger penetrating ability in the vertical direction, so it is necessary to reduce the interlayer spacing of vertical monitoring points to improve the identification accuracy of deep pollution plume migration paths.

[0103] The boundary sampling resolution adjustment value is obtained based on the diffusion area difference ratio matching. The specific method is as follows: obtain the current diffusion area difference ratio and call the boundary sampling resolution adjustment mapping table in the database; the boundary sampling resolution adjustment mapping table is established based on historical pollution plume lateral diffusion experimental data, which records the optimal boundary sampling resolution adjustment value corresponding to different diffusion area difference ratios; normalize the current diffusion area difference ratio and match it to the corresponding historical interval; if the current diffusion area difference ratio is between two historical intervals, the corresponding boundary sampling resolution adjustment value is calculated using linear interpolation; where, when the diffusion area difference ratio is larger, it indicates that the lateral diffusion range of the pollution plume is wider, so it is necessary to improve the boundary sampling resolution to enhance the recognition accuracy of pollution plume boundary changes.

[0104] Based on a comprehensive analysis of the diffusion rate difference ratio and the diffusion area difference ratio, the overall diffusion impact value is obtained. The specific method is as follows: Preset baseline values ​​for the diffusion rate difference ratio and the diffusion area difference ratio are obtained; then, the diffusion rate difference ratio and the diffusion area difference ratio are compared with their corresponding baseline values ​​to obtain the comparison analysis results. These results are then weighted and coupled to obtain the overall diffusion impact value. The specific calculation formula is as follows:

[0105] ;

[0106] In the formula, This represents the overall diffusion impact value. Indicates the proportion of the difference in diffusion rate. This represents the baseline value indicating the proportion of differences in diffusion rates. This indicates the proportion of the difference in diffusion area. This represents the baseline value indicating the proportion of differences in diffusion area. This indicates that the proportion of the difference in diffusion rate affects the weight. This indicates the weighting of the difference in diffusion area.

[0107] The weights of the diffusion velocity difference ratio and the diffusion area difference ratio can be obtained from a database. For example, data on the diffusion velocity difference ratio, diffusion area difference ratio, and final pollution diffusion range in historical rapid diffusion events can be retrieved. A diffusion trend influence database can be accessed, which records different diffusion velocity difference ratios, different diffusion area difference ratios, and corresponding pollution plume diffusion intensity levels. A random forest feature contribution analysis method can be used to analyze the influence of the diffusion velocity difference ratio and the diffusion area difference ratio on the pollution plume diffusion intensity. The analyzed influence levels are then normalized to obtain the weights of the diffusion velocity difference ratio and the diffusion area difference ratio. Specifically, the more significant the influence of the diffusion velocity difference ratio on the rapid migration of the pollution plume, the greater its weight; similarly, the more significant the influence of the diffusion area difference ratio on the lateral diffusion of the pollution plume, the greater its weight.

[0108] Based on the matching of the comprehensive diffusion impact value, the sampling frequency adjustment value is obtained. The specific method is as follows: obtain the current comprehensive diffusion impact value and call the sampling frequency adjustment mapping table in the database; the sampling frequency adjustment mapping table is established based on historical pollution plume rapid diffusion monitoring data, which records the optimal sampling frequency adjustment value corresponding to different comprehensive diffusion impact values; normalize the current comprehensive diffusion impact value and map it to the corresponding historical impact interval; if the current comprehensive diffusion impact value is between two historical intervals, the corresponding sampling frequency adjustment value is calculated using linear interpolation; where, the larger the comprehensive diffusion impact value, the more obvious the overall migration activity of the pollution plume, so it is necessary to increase the sampling frequency to enhance the continuous monitoring capability of the dynamic migration process of the pollution plume, thereby improving the real-time performance and accuracy of the pollution source tracing results.

[0109] By adjusting the interlayer spacing of vertical monitoring points under conditions of rapid vertical penetration of pollution, the monitoring capability of deep vertical migration of pollutants is enhanced, thus avoiding the loss of depth monitoring when pollutants penetrate across layers. By adjusting the boundary sampling resolution under conditions of slow lateral diffusion of pollution, the spatial identification accuracy of the pollution plume boundary region is improved, thus enhancing the ability to capture changes in the pollution plume diffusion boundary. By adjusting the sampling frequency under conditions of rapid overall pollution diffusion, the real-time acquisition capability of dynamic changes in pollution migration is improved, thus avoiding data lag during the rapid diffusion of the pollution plume. Therefore, a differentiated detection adjustment strategy is adopted for different diffusion trends, so that monitoring resources are concentrated on the areas with the most obvious pollution changes, improving the accuracy of pollution migration path identification and the reliability of subsequent reverse tracing of pollution sources.

[0110] Furthermore, historical residual pollution areas and newly added pollution source areas are identified through the following methods: Time-series data of pollution concentration in the corresponding areas of each suspected pollution source area are obtained within a second preset time period; concentration change trend analysis is performed based on the pollution concentration time-series data to obtain the pollution concentration change rate; pollution plume boundary change data for the corresponding areas of each suspected pollution source area are obtained within the second preset time period, and pollution diffusion boundary stability analysis is performed to obtain the pollution plume boundary change rate; underground flow field direction change data for the corresponding areas of each suspected pollution source area are obtained within the second preset time period, and underground flow field dynamic correlation analysis is performed to obtain the underground flow field correlation change rate; when the pollution concentration change rate of a suspected pollution source area is less than a preset concentration change rate threshold, and the pollution plume boundary change rate is less than a preset boundary change rate threshold, the corresponding area is identified as a historical residual pollution area; when the pollution concentration change rate of a suspected pollution source area is greater than a preset concentration change rate threshold, and the underground flow field correlation change rate is greater than a preset flow field correlation change rate threshold, the corresponding area is identified as a newly added pollution source area. Otherwise, the suspected pollution source area is removed from the label.

[0111] In this embodiment, the pollution concentration time series data is collected by online monitoring sensors deployed in the suspected source area at a fixed frequency during a second preset time period, forming a series that changes over time.

[0112] The specific method for obtaining the rate of change in pollution concentration is as follows:

[0113] ;

[0114] In the formula, Indicates the rate of change in pollution concentration. Indicates the monitoring time. Indicates pollution concentration. This represents the total number of sampling points.

[0115] The data on changes in the pollution plume boundary are obtained by periodically extracting the spatial coordinates of the pollution plume edge in the three-dimensional dynamic model within the second preset time period, and calculating the pollution plume area at the first and last moments of the second preset time period based on the spatial coordinates.

[0116] The rate of change of the pollution plume boundary is calculated as follows:

[0117] ;

[0118] In the formula, Indicates the rate of change of the pollution plume boundary. This indicates the area of ​​the pollution plume at the end of the second preset time period. This indicates the area of ​​the pollution plume at the first moment within the second preset time period. This indicates the time interval within the second preset time period.

[0119] Data on changes in the direction of underground flow can be obtained by retrieving real-time groundwater flow direction monitoring data, recording the groundwater flow vector and the centroid movement vector of the pollutant plume, and used to measure the consistency between local flow field fluctuations and concentration migration paths. The specific calculation method is as follows:

[0120] ;

[0121] In the formula, Indicates the rate of change of the underground flow field. Represents the groundwater flow vector. This represents the vector representing the movement of the centroid of the contamination plume.

[0122] By analyzing the relationship between the rate of change of pollution concentration, the rate of change of pollution plume boundary, and the rate of change associated with the underground flow field, we can distinguish between historically residual pollution areas and newly added pollution source areas, thereby achieving dynamic identification of different pollution source states. When the rate of change of pollution concentration and the rate of change of pollution plume boundary are both small, it indicates that the pollution diffusion state in the corresponding area is stable in the long term, and the pollutants mainly originate from historically residual pollution; therefore, it can be identified as a historically residual pollution area. When the rate of change of pollution concentration and the rate of change associated with the underground flow field are both large, it indicates that there is a synchronous reinforcing relationship between the pollutant concentration and the changes in the underground flow field, suggesting that the underground flow field is driving the continuous diffusion of new pollutants; therefore, it can be identified as a newly added pollution source area.

[0123] Furthermore, reverse tracing of pollution sources is conducted to obtain newly added pollution source areas. The specific method is as follows: obtain the pollution diffusion direction and pollution diffusion speed of the newly added pollution source area at each time point within the third preset time period; reverse the pollution propagation path based on the pollution diffusion direction and pollution diffusion speed at each time point to obtain pollution source propagation path data at each time point; analyze the path intersection area based on the pollution source propagation path data at each time point to obtain the pollution propagation convergence area; verify the path consistency of the pollution propagation convergence area based on the dynamic change data of the underground flow field and the distribution data of the underground medium structure to obtain the newly added pollution source area.

[0124] In this embodiment, the direction and velocity of pollution diffusion can be determined by deploying multiple groundwater monitoring wells in the target area, and installing multi-parameter water quality sensors and electromagnetic flow meters inside the wells. The multi-parameter water quality sensors are used to measure the concentration of pollutants in the groundwater in real time, and the electromagnetic flow meters are used to measure the groundwater flow velocity. Subsequently, fluorescent or salinity tracers are injected into the target area, and the pollutant migration path is analyzed by observing the changes in tracer concentration at corresponding locations of different monitoring wells. The pollution diffusion velocity is calculated based on the time difference and spatial relationship of the peak pollutant concentrations between each monitoring well. The direction of pollution diffusion is analyzed based on the direction of the pollutant concentration gradient and the angle of groundwater flow. A higher pollution diffusion velocity indicates a higher level of activity in the underground pollution plume migration, and a more stable pollution diffusion direction indicates a more stable underground flow field structure.

[0125] The pollution source propagation path data for each time point is obtained as follows: Within the third preset time period, taking the newly added pollution source area as the starting point, a random particle reverse tracking algorithm is executed in the opposite direction of the diffusion direction to obtain the reverse vector displacement at each time point. The specific formula is as follows:

[0126] ;

[0127] In the formula, This represents the reverse vector displacement at the k-th time point. Indicates the rate of pollution spread. Indicates the direction of pollution spread.

[0128] Based on the pollution source propagation path data at each time point, the path intersection area is analyzed to obtain the pollution propagation convergence area. The specific method is as follows: calculate the minimum distance point of multiple inferred paths in three-dimensional space to form the pollution propagation convergence area. Through the probability envelope surface intersection method, the spatial grid with the highest degree of intersection is the suspected source.

[0129] To verify the consistency of the diffusion path, newly identified pollution source areas were identified. The specific method involved retrieving dynamic data on the underground flow field in the convergence area (to verify whether the flow field could support the diffusion path) and data on the distribution of underground media structure (to verify the existence of permeable channels or media that allow pollutants to accumulate). The dynamic data on the underground flow field included groundwater velocity, groundwater flow direction angle, groundwater level change, hydraulic gradient, and the rate of change of groundwater seepage direction. Groundwater velocity and flow direction angle were obtained using electromagnetic flow meters in underground monitoring wells; groundwater level change was obtained using a water level gauge; the hydraulic gradient was calculated using the groundwater level difference between adjacent monitoring wells and the distance between wells; and the rate of change of groundwater seepage direction was obtained by analyzing the changes in the groundwater flow direction angle over a continuous time period. The data on the distribution of subsurface media structure include formation resistivity, seismic wave propagation velocity, formation pore pressure, subsurface porosity, and subsurface permeability coefficient. Among them, formation resistivity is obtained through resistivity tomography; seismic wave propagation velocity is obtained through shallow seismic exploration equipment; formation pore pressure is obtained through pore pressure sensors; subsurface porosity is obtained through core sampling experiments; and subsurface permeability coefficient is obtained through pumping tests and permeability tests. When tracing pollution sources, the process begins by analyzing the migration direction and velocity of underground pollution plumes over different time periods based on dynamic data of underground flow fields. Then, it analyzes the distribution data of underground media structures to identify high-permeability channels, fissure structures, or areas prone to pollutant migration. If the pollution propagation path aligns with the direction of the underground flow field and a high-permeability channel exists in the corresponding area, the path is deemed to possess the conditions for actual pollution propagation. Subsequently, the intersection areas of pollution propagation paths at multiple time points are analyzed to obtain pollution convergence areas. Finally, the distribution data of underground media structures is used to verify whether these convergence areas possess the conditions for long-term pollutant storage. If the underground flow field migration patterns and underground media permeability conditions are met, the area is identified as a newly added pollution source area.

[0130] By acquiring the pollution diffusion direction and velocity at each time point within a third preset time period for newly added pollution source areas, and performing reverse deduction of pollution propagation paths, pollution source propagation path data for each time point is obtained, thereby enabling reverse tracing of the pollution source area. Through path intersection area analysis of pollution source propagation path data at multiple time points, the convergence relationship of pollution migration paths at different times can be used to narrow the pollution source location range and improve the accuracy of pollution source location. Furthermore, by combining dynamic changes in underground flow fields and underground media structure distribution data for path consistency verification, it is possible to verify whether the underground flow field direction and underground media infiltration conditions support the pollution propagation path, thereby improving the authenticity and reliability of newly added pollution source area identification and enhancing the ability to trace pollution sources in complex underground environments.

[0131] like Figure 4 As shown, Figure 4 The system architecture diagram of this invention is shown below. The groundwater pollution plume source tracing and identification system based on three-dimensional dynamic monitoring provided in this application includes the following modules: a data processing module, used to acquire groundwater monitoring data and stratum monitoring data at different spatial locations in the target area, and analyze them to construct a three-dimensional dynamic model of the groundwater pollution plume; a prediction and inversion module, used to acquire the current underground flow field state and combine it with the three-dimensional dynamic model of the groundwater pollution plume to predict the migration of the groundwater pollution plume, obtaining the predicted pollution plume state and the inverted location of the initial pollution source; a pollution migration anomaly analysis module, used to continuously acquire real-time groundwater monitoring data and analyze it to obtain the real-time pollution plume state; perform difference analysis between the real-time pollution plume state and the predicted pollution plume state within a first preset time period to obtain the pollution migration anomaly analysis result; and an impact type analysis module, used to, when the pollution migration anomaly analysis result indicates that there is no pollution migration anomaly, then identify the initial pollution source... The inversion location is used as a suspected pollution source area; otherwise, the set of underground fluctuation impact parameters is obtained and the coupling correlation change analysis of the underground fluctuation impact parameter set is performed to identify the corresponding underground impact type. The suspected pollution source area analysis module is used to issue an early warning and maintain real-time underground monitoring when the underground impact type is underground state stable; otherwise, the underground monitoring parameters are dynamically adjusted based on the underground impact type, and the groundwater monitoring data is re-acquired after adjustment to update the three-dimensional dynamic model of the underground pollution plume, thus obtaining each suspected pollution source area. The pollution source location confirmation module is used to perform historical pollution and new pollution analysis on the corresponding areas of each suspected pollution source area within a second preset time period to obtain historical residual pollution areas and new pollution source areas. The new pollution source area confirmation module is used to perform reverse tracking of pollution sources based on the dynamic migration path of the underground pollution plume corresponding to the new pollution source area to obtain the new pollution source area.

[0132] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for tracing and identifying groundwater pollution plumes based on three-dimensional dynamic monitoring, characterized in that: Includes the following steps: Acquire groundwater monitoring data and stratigraphic monitoring data at different spatial locations in the target area, and analyze them to construct a three-dimensional dynamic model of the underground pollution plume; Based on the three-dimensional dynamic model of the underground pollution plume, the migration prediction of the underground pollution plume is carried out to obtain the predicted state of the pollution plume and the inversion location of the initial pollution source. Continuously acquire real-time groundwater monitoring data and analyze the real-time pollution plume status; By performing a difference analysis between the real-time pollution plume status and the predicted pollution plume status within the first preset time period, the pollution migration anomaly analysis results are obtained. When the pollution migration anomaly analysis result is that there is no pollution migration anomaly, the initial pollution source inversion location is taken as the suspected pollution source area; otherwise, the underground fluctuation impact parameter set is obtained and the coupling correlation change analysis of the underground fluctuation impact parameter set is performed to identify the corresponding underground impact type. When the underground impact type is underground state stable, an early warning is issued and real-time underground monitoring is maintained. Otherwise, the underground monitoring parameters are dynamically adjusted based on the underground impact type, and the groundwater monitoring data is reacquired after the adjustment to update the three-dimensional dynamic model of the underground pollution plume and obtain the areas of each suspected pollution source. Historical and new pollution analyses were performed on the corresponding areas of each suspected pollution source area during the second preset time period to obtain the historical residual pollution areas and the new pollution source areas. The pollution source areas are obtained by reverse tracing the underground pollution plume dynamic migration path corresponding to the newly added pollution source areas. The specific method for constructing a three-dimensional dynamic model of underground pollution plumes is as follows: Acquire groundwater monitoring data at different spatial locations in the target area. The groundwater monitoring data includes pollutant concentration values, groundwater flow velocity values, and groundwater flow direction angles. Acquire stratigraphic monitoring data at different spatial locations underground in the target area. The stratigraphic monitoring data includes stratigraphic resistivity values, seismic wave propagation velocity values, and stratigraphic pore pressure values. By performing three-dimensional fusion processing on groundwater monitoring data and formation monitoring data, a three-dimensional dynamic model of the underground pollution plume is obtained. The method for identifying the corresponding underground impact type is as follows: Obtain the set of subsurface wave impact parameters at the current time point, which includes seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area; Based on the set of parameters affecting underground fluctuations and the corresponding historical benchmark monitoring data, the rates of change of seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area were obtained. Based on the correlation analysis of seismic wave propagation velocity change rate and pollution diffusion velocity change rate, when the seismic wave propagation velocity change rate is greater than the seismic wave propagation velocity change rate threshold and the pollution diffusion velocity change rate is less than the pollution diffusion velocity change rate threshold, the subsurface impact type is subsurface medium compression enhancement. Based on the correlation analysis of formation resistivity change rate and pollution plume diffusion area change rate, when the formation resistivity change rate is less than the formation resistivity change rate threshold and the pollution plume diffusion area change rate is greater than the pollution plume diffusion area change rate threshold, the subsurface influence type is subsurface medium loosening enhancement. Based on the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area, a correlation analysis of pollution migration and diffusion is conducted. When the rate of change of pollution diffusion velocity is greater than the threshold of the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area is greater than the threshold of the rate of change of pollution plume diffusion area, the underground impact type is rapid migration of pollution plume. Otherwise, the underground influence type is underground state stable.

2. The groundwater pollution plume source identification method based on three-dimensional dynamic monitoring according to claim 1, characterized in that: The method for predicting the migration of underground pollution plumes to obtain the predicted plume state and the initial pollution source inversion location is as follows: Extract pollution plume diffusion boundary data and pollution concentration diffusion trend data from the three-dimensional dynamic model of the underground pollution plume; Data on the direction of groundwater dynamic migration is extracted based on the current underground flow field status data; Based on pollution plume diffusion boundary data, pollution concentration diffusion trend data and groundwater monitoring data, the migration path of the underground pollution plume is predicted to obtain the initial pollution diffusion direction and the initial pollution migration speed. A concentration gradient vector was constructed using pollution concentration diffusion trend data, and then weighted and fused with groundwater dynamic migration direction data to obtain the initial pollution diffusion direction. Based on the initial pollution diffusion direction and initial pollution migration velocity, reverse path analysis of pollution sources is performed to obtain the predicted pollution plume state and the inverted location of the initial pollution source. The predicted plume state includes initial plume boundary data and initial plume diffusion depth.

3. The groundwater pollution plume source identification method based on three-dimensional dynamic monitoring according to claim 2, characterized in that: The specific method for obtaining the pollution migration anomaly analysis results is as follows: Update the data on the pollution plume diffusion boundary and pollution concentration diffusion trend at the actual preset location and compare them with the predicted values ​​at the corresponding preset location in the three-dimensional dynamic model of the underground pollution plume to obtain the pollution plume boundary offset distance. Based on the analysis of the three-dimensional dynamic model of the underground pollution plume, the predicted pollution migration rate at the preset location is obtained; Depth migration analysis was performed based on real-time pollution diffusion depth data and predicted pollution diffusion depth data to obtain pollution plume depth migration values. The real-time boundary position of the pollution plume is obtained at each time point within the first preset time period, and the real-time migration path change analysis is performed to obtain the real-time pollution concentration migration speed. Migration deviation analysis was performed based on the real-time pollution concentration migration rate and the predicted pollution migration rate to obtain the pollution migration rate change rate. Anomaly correlation analysis was performed based on the pollution plume boundary offset distance, pollution plume depth offset value, and pollution migration velocity change rate to obtain pollution migration anomaly analysis results. When the boundary offset distance of the pollution plume is greater than the preset threshold for the boundary offset distance of the pollution plume, and the depth offset value of the pollution plume is greater than the preset threshold for the depth offset of the pollution plume, the pollution migration anomaly analysis result indicates that there is an abnormal influence from underground structures. When the rate of change of pollution migration velocity is greater than the preset threshold for the rate of change of pollution migration velocity, and the deflection angle of the pollution plume migration direction is greater than the preset threshold for the deflection angle of the pollution plume migration direction, the pollution migration anomaly analysis result indicates that there is an abnormal influence of the underground flow field. Otherwise, the pollution migration anomaly analysis result will be that no pollution migration anomaly exists; The results of the pollution migration anomaly analysis, which show the presence of both underground structural anomalies and underground flow field anomalies, are jointly labeled as indicating the presence of pollution migration anomalies.

4. The groundwater pollution plume source identification method based on three-dimensional dynamic monitoring according to claim 1, characterized in that: The specific method for dynamically adjusting underground monitoring parameters based on underground impact types is as follows: If the underground impact type is underground medium compression enhancement, then a joint analysis is performed based on the rate of change of seismic wave propagation velocity and the rate of change of pollution diffusion velocity to obtain the comprehensive impact value of underground medium compression. Based on the comprehensive impact value of underground medium compression, the sampling interval adjustment value of monitoring points and the seismic wave scanning frequency adjustment value are obtained, thereby reducing the sampling interval distance of monitoring points and increasing the seismic wave scanning frequency. If the type of subsurface influence is subsurface medium loosening enhancement, the pollutant plume scanning range adjustment value and flow velocity sampling frequency adjustment value are obtained by matching the formation resistivity change rate with the preset formation loosening response rule, thereby increasing the pollutant plume scanning range and / or increasing the flow velocity sampling frequency. If the underground impact type is rapid migration of the pollution plume, then the difference ratio analysis is performed based on the change rate of pollution diffusion velocity and the change rate of pollution plume diffusion area with the corresponding thresholds to obtain the corresponding diffusion velocity difference ratio and diffusion area difference ratio. Diffusion trend analysis is performed on the ratio of differences in diffusion rate and the ratio of differences in diffusion area to obtain diffusion trend determination results. The diffusion trend determination results include rapid vertical penetration of pollution, slow horizontal diffusion of pollution, and rapid overall diffusion of pollution. The diffusion detection is adjusted based on the diffusion trend determination results. The diffusion detection adjustment includes adjusting the flow direction update cycle, adjusting the monitoring point coverage density, and adjusting the scanning range.

5. The groundwater pollution plume source tracing and identification method based on three-dimensional dynamic monitoring according to claim 4, characterized in that: The specific method for adjusting the diffusion detection is as follows: If the diffusion trend is determined to be that the pollution penetrates rapidly in the vertical direction, the interlayer spacing adjustment value of the vertical monitoring points is obtained based on the proportional matching of the diffusion rate difference, thereby reducing the interlayer spacing of the vertical monitoring points. If the diffusion trend is determined to be a slow horizontal diffusion of pollution, the boundary sampling resolution adjustment value is obtained based on the proportional matching of the diffusion area difference, and the boundary sampling resolution is adjusted accordingly. If the diffusion trend is determined to be a rapid overall diffusion of pollution, a comprehensive diffusion impact value is obtained based on a comprehensive analysis of the diffusion rate difference ratio and the diffusion area difference ratio. Based on the matching of the comprehensive diffusion impact value, a sampling frequency adjustment value is obtained, thereby increasing the sampling frequency.

6. The groundwater pollution plume source identification method based on three-dimensional dynamic monitoring according to claim 1, characterized in that: The specific method for obtaining historical residual pollution areas and newly added pollution source areas is as follows: Obtain time series data of pollution concentration in the corresponding areas of each suspected pollution source area within a second preset time period; The pollution concentration change rate is obtained by analyzing the concentration change trend based on the time series data of pollution concentration. Data on the change of pollution plume boundaries in the corresponding areas of each suspected pollution source area during the second preset time period were obtained, and the stability analysis of the pollution diffusion boundary was performed to obtain the pollution plume boundary change rate. Data on the change of underground flow field direction in the corresponding areas of each suspected pollution source area during the second preset time period were obtained, and dynamic correlation analysis of the underground flow field was performed to obtain the correlation change rate of the underground flow field. When the rate of change of pollution concentration in a suspected pollution source area is less than the preset rate of change of concentration, and the rate of change of pollution plume boundary is less than the preset rate of change of boundary boundary, the corresponding area is determined to be a historical residual pollution area. When the rate of change of pollution concentration in a suspected pollution source area is greater than the preset rate of change of concentration, and the rate of change of underground flow field is greater than the preset rate of change of flow field, the corresponding area is determined to be a newly added pollution source area. Otherwise, the area suspected of being a source of pollution will be removed from the list.

7. The groundwater pollution plume source identification method based on three-dimensional dynamic monitoring according to claim 1, characterized in that: The method for reverse tracing of pollution sources to obtain newly added pollution source areas is as follows: Obtain the direction and speed of pollution diffusion in the newly added pollution source area at each time point within the third preset time period; Based on the pollution diffusion direction and pollution diffusion speed at each time point, the pollution propagation path is reversed to obtain the pollution source propagation path data at each time point. Based on the pollution source propagation path data at each time point, the path intersection area is analyzed to obtain the pollution propagation convergence area. Based on the dynamic changes in the underground flow field and the distribution data of the underground media structure, the path consistency of the pollution propagation and convergence area is verified, and the newly added pollution source area is obtained.

8. A system applying the groundwater pollution plume source tracing and identification method based on three-dimensional dynamic monitoring as described in any one of claims 1-7, characterized in that: Includes the following modules: The data processing module is used to acquire groundwater monitoring data and stratum monitoring data from different spatial locations in the target area, and analyze them to construct a three-dimensional dynamic model of the underground pollution plume. The prediction and inversion module is used to obtain the current underground flow field state and combine it with the three-dimensional dynamic model of the underground pollution plume to predict the underground pollution plume migration, thereby obtaining the predicted pollution plume state and the initial pollution source inversion location. The pollution migration anomaly analysis module is used to continuously acquire real-time groundwater monitoring data and analyze the real-time pollution plume status. By performing a difference analysis between the real-time pollution plume status and the predicted pollution plume status within the first preset time period, the pollution migration anomaly analysis results are obtained. The impact type analysis module is used to identify the initial pollution source inversion location as a suspected pollution source area when the pollution migration anomaly analysis result shows that there is no pollution migration anomaly; otherwise, it obtains the underground fluctuation impact parameter set and performs coupling correlation change analysis on the underground fluctuation impact parameter set to identify the corresponding underground impact type. The suspected pollution source area analysis module is used to issue an early warning and maintain real-time underground monitoring when the underground impact type is underground state stable; otherwise, it dynamically adjusts the underground monitoring parameters based on the underground impact type, and re-acquires groundwater monitoring data after adjustment to update the three-dimensional dynamic model of underground pollution plume and obtain each suspected pollution source area. The pollution source location confirmation module is used to analyze the historical pollution and new pollution in the corresponding areas of each suspected pollution source area within the second preset time period, and to obtain the historical residual pollution area and the new pollution source area. A new pollution source area confirmation module is added, which is used to reverse track pollution sources based on the dynamic migration path of underground pollution plumes corresponding to the newly added pollution source areas to obtain the newly added pollution source areas. The method for identifying the corresponding underground impact type is as follows: Obtain the set of subsurface wave impact parameters at the current time point, which includes seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area; Based on the set of parameters affecting underground fluctuations and the corresponding historical benchmark monitoring data, the rates of change of seismic wave propagation velocity, pollution diffusion velocity, formation resistivity, and pollution plume diffusion area were obtained. Based on the correlation analysis of seismic wave propagation velocity change rate and pollution diffusion velocity change rate, when the seismic wave propagation velocity change rate is greater than the seismic wave propagation velocity change rate threshold and the pollution diffusion velocity change rate is less than the pollution diffusion velocity change rate threshold, the subsurface impact type is subsurface medium compression enhancement. Based on the correlation analysis of formation resistivity change rate and pollution plume diffusion area change rate, when the formation resistivity change rate is less than the formation resistivity change rate threshold and the pollution plume diffusion area change rate is greater than the pollution plume diffusion area change rate threshold, the subsurface influence type is subsurface medium loosening enhancement. Based on the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area, a correlation analysis of pollution migration and diffusion is conducted. When the rate of change of pollution diffusion velocity is greater than the threshold of the rate of change of pollution diffusion velocity and the rate of change of pollution plume diffusion area is greater than the threshold of the rate of change of pollution plume diffusion area, the underground impact type is rapid migration of pollution plume. Otherwise, the underground influence type is underground state stable.

Images