Estimation of groundwater storage from seismic data using deep learning
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.
Cite
@article{arxiv.1806.08375,
title = {Estimation of groundwater storage from seismic data using deep learning},
author = {Timo Lähivaara and Alireza Malehmir and Antti Pasanen and Leo Kärkkäinen and Janne M. J. Huttunen and Jan S. Hesthaven},
journal= {arXiv preprint arXiv:1806.08375},
year = {2019}
}