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

Featured Application In this work, Vector Autoregressive and Recurrent Neural Network algorithms are used to predict time-space evolution of a saline water plume in homogeneous and real aquifers. 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 of this approach is described. Then, the proposed approach is applied to real geoelectric datasets recorded through experiments at different spatial and temporal scales. These include a sequence of cross-hole resistivity surveys aimed at monitoring a tracer diffusion in a real aquifer as well as in a laboratory experimental set. Multiple predictive methods were applied to both datasets, including Vector Autoregressive (VAR) and Recurrent Neural Network (RNN) algorithms, over the entire sequence of ERT monitor surveys. In both field and lab experiments, the goal was to retrieve a determined number of predicted pseudo sections of apparent resistivity values. By inverting both real and predicted datasets, it is possible to define a dynamic model of time-space evolution of the water plume contaminated by a tracer injected into the aquifer system(s). This approach allowed for describing the complex fluid displacement over time conditioned by the hydraulic properties of the aquifer itself.

Automated summary

This work discusses the use of Vector Autoregressive and Recurrent Neural Network algorithms to predict the evolution of a saline water plume in aquifers. An integrated workflow combining time-lapse Electric Resistivity Tomography and predictive algorithms is proposed and applied to real geoelectric datasets. The results show that this approach can effectively describe the complex fluid displacement in aquifers over time.