Optimization of Aquifer Monitoring through Time-Lapse Electrical Resistivity Tomography Integrated with Machine-Learning and Predictive Algorithms

In this paper, an integrated workflow aimed at optimizing aquifer monitoring and management through time-lapse Electric Resistivity Tomography (TL-ERT) combined with a suite of predictive algorithms is discussed. First, the theoretical background approach described. Then, proposed applied to real geoelectric datasets recorded experiments different spatial temporal scales. These include sequence cross-hole resistivity surveys tracer diffusion in as well laboratory experimental set. Multiple methods were both datasets, including Vector Autoregressive (VAR) Recurrent Neural Network (RNN) algorithms, over entire ERT monitor surveys. field lab experiments, goal was retrieve determined number “predicted” pseudo sections apparent values. By inverting predicted it possible define dynamic model time-space evolution water plume contaminated by injected into system(s). This allowed for describing complex fluid displacement time conditioned hydraulic properties itself.