Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention an...Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.展开更多
Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is w...Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients.This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system.The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective,record the real-time SP signal generated by the redox reaction,as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources.The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model.The measured SP data were quantitatively interpreted using the simulated annealing algorithm,and the inversion results closely match the real model.This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers.The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.展开更多
基金Projects(42174170,41874145,72088101)supported by the National Natural Science Foundation of ChinaProject(CX20200228)supported by the Hunan Provincial Innovation Foundation for Postgraduate,China。
文摘Landfill leaks pose a serious threat to environmental health,risking the contamination of both groundwater and soil resources.Accurate investigation of these sites is essential for implementing effective prevention and control measures.The self-potential(SP)stands out for its sensitivity to contamination plumes,offering a solution for monitoring and detecting the movement and seepage of subsurface pollutants.However,traditional SP inversion techniques heavily rely on precise subsurface resistivity information.In this study,we propose the Attention U-Net deep learning network for rapid SP inversion.By incorporating an attention mechanism,this algorithm effectively learns the relationship between array-style SP data and the location and extent of subsurface contaminated sources.We designed a synthetic landfill model with a heterogeneous resistivity structure to assess the performance of Attention U-Net deep learning network.Additionally,we conducted further validation using a laboratory model to assess its practical applicability.The results demonstrate that the algorithm is not solely dependent on resistivity information,enabling effective locating of the source distribution,even in models with intricate subsurface structures.Our work provides a promising tool for SP data processing,enhancing the applicability of this method in the field of near-subsurface environmental monitoring.
基金Supported by National High Technology Research and Development Program of China （863 Program） （2007AA04Z239） and National Natural Science Foundation of China （60621001, 60975060）
基金Project(42174170)supported by the National Natural Science Foundation of China。
文摘Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients.This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system.The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective,record the real-time SP signal generated by the redox reaction,as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources.The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model.The measured SP data were quantitatively interpreted using the simulated annealing algorithm,and the inversion results closely match the real model.This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers.The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.
