NAPL Soil Pollution Monitoring Based On ERT Data Mining

High-resolution imaging and monitoring for Non-Aqueous Phase Liquids(NAPLs),a serious threat to underground soil and shallow groundwater resources,has become a major scientific and social topic.Traditional geochemical analysis for borehole samplings are complicated,timeconsuming,and inefficient,as soil resistivity is affected by solid particle composition,porosity,water content,and the type of liquid in the pores.Besides,continuous physical properties for subsurface soils can’t be obtained in such case.The resistivity method can locate the electrical distribution of soil/rock,observing the electrical characteristics of the subsurface medium.Futhermore,Electrical Resistivity Tomography(ERT)is based on traditional resistivity method,will all electrodes(dozens or even hundreds of root)on the measuring point at the same time.ERT can acquire the range and depth information of underground abnormal body effectively through fast field data collection,which has the advantages of low cost,high efficiency and abundant data.Due to the underdetermination and multiple solutions of ERT inversion,it’s not so easy to reveal the migration and flow characteristics for subsurface NAPLs accurately,just by virtures of ERT inversion results.Data mining technology can obtain previously unknown,implicit and valuable information through continuous analysis on a large amount of rich data.In this paper,based on the migration and diffusion law of NAPLs in the soil,the forward model of NAPL soil pollution is established,and ERT inverse imaging is conducted to explore the abnormal change characteristics of ERT imaging of NAPL pollutants at different times.Secondly,ERT detection is conducted in a soil contaminated site in Zhejiang Province,and relevant data of borehole and geochemical analysis are compared and verified.Furthermore,a time-lapse ERT monitoring is carried out on near surface geophysics site of Zhejiang University to analyze and evaluate the migration characteristics of NAPL pollutants in underground soil quantitatively by data mining technology.The temperature,humidity,electrical conductivity and precipitation data of soil 1D along the survey line during the monitoring period are collected for correlation analysis,and the resistivity data are statistically analyzed to determine the depth of groundwater level.The influence of precipitation data on clustering analysis and the effect of different clustering methods are studied.Futhermore,the global and local horizontal clustering analysis with different depths is carried out to delineate the aggregation location and size morphology of underground pollutants.The results show that precipitation has a great influence on the underground resistivity monitored in this experiment,which is an important factor for the driving and migration of underground pollutants in the experimental site,but has little influence on the resistivity cluster analysis results.The research results are verified by borehole and subsequent relevant data.Cluster analysis can accurately delineate the aggregation location and size morphology of underground pollutants,and reveal its abnormal temporal and spatial changes,providing important data guarantee and technical support for soil pollution site investigation and soil pollution control.