AI-driven anomaly detection for water seepage monitoring using electrical resistivity tomography

The F-Area Seepage Basins at the U.S. Department of Energy (DOE)’s Savannah River Site (SRS) have been a long-standing source of groundwater contamination due to the disposal of low-level radioactive waste during the Cold War. To mitigate further contamination, the basins were dewatered and sealed with low-permeability caps designed to prevent rainwater infiltration and minimize migration of contaminants into the groundwater. Monitoring the structural integrity of these caps is crucial to maintaining the effectiveness of remediation efforts. Electrical Resistivity Tomography (ERT) has been deployed to detect water seepage through the cap by measuring changes in electrical conductivity beneath the cap. However, although ERT data collection and processing can be automated, analysis of the resultant images requires human effort, which is time-consuming and resource-intensive over the timescales required for long-term monitoring. An AI-based automated system was developed to process and analyze ERT images in real time. This system identifies anomalies in conductivity values, locates potential seepage areas, and evaluates their severity. The results are automatically visualized in an interactive 3D model, enabling stakeholders to efficiently assess cap integrity and determine whether further investigation is warranted. By eliminating the need for daily manual data analysis, this approach significantly reduces monitoring costs and enhances early detection capabilities. The AI system is adaptable to any ERT-based monitoring application beyond the F-Area Seepage Basin caps, enabling the detection, localization, and assessment of anomalies across a wide range of domains, both within and beyond the DOE complex.