Combining Multi-temporal Electric Resistivity Tomography and Predictive Algorithms for supporting aquifer monitoring and management

Summary This work presents the results of geophysical data prediction by applying statistical and predictive algorithms to a multi-temporal Electric Resistivity Tomography dataset. A cross-hole time-lapse resistivity survey was carried out during an experiment addressed to monitor a tracer diffusion in a real aquifer. In order to retrieve a number of “predicted” pseudo sections of apparent resistivity values, we applied the Vector Autoregressive (VAR) and Recurrent Neural Network (RNN) algorithms over a sequence of 18 ERT surveys. Real and predicted dataset allow to delineate plume evolution under 30 m depth, describing a complex transport pathway influenced by hydraulic properties of the studied aquifer.