Estimation of groundwater storage from seismic data using deep learning

TL;DR

This study demonstrates that deep convolutional neural networks can effectively estimate groundwater storage and water-table levels from seismic data, offering a new approach to aquifer characterization beyond traditional seismic analysis methods.

Contribution

The paper introduces a novel framework combining wave propagation modeling with deep learning to estimate groundwater parameters from seismic data.

Findings

Deep learning accurately estimates groundwater storage and water-table levels.

Synthetic data experiments show potential for seismic-based aquifer characterization.

Method surpasses traditional seismic analysis in extracting aquifer information.

Abstract

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components such as the amount of groundwater stored in an aquifer and delineate water-table level, from active-source seismic data are investigated in this study. The data to train, validate, and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic-elastic media. A discontinuous Galerkin method is applied to model wave propagation whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns are the amount of stored groundwater and water-table level, and are estimated, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural networks-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.