Lahivaara, TimoPasanen, AnttiKarkkainen, LeoHuttunen, Janne MJHesthaven, Jan S.Malehmir, Alireza2018-06-242018-06-242018-06-24201910.1111/1365-2478.12831https://infoscience.epfl.ch/handle/20.500.14299/146953WOS:000477189800001We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic-elastic media. A discontinuous Galerkin method is used to model wave propagation whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns, the amount of stored groundwater and water-table level, are estimated, while the remaining parameters, assumed to be of less of interest, are successfully marginalized in the convolutional neural networks-based solution.text::journal::journal article::research article