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With global groundwater levels undergoing rapid declines, there is an urgentneed for advanced methodologies to monitor and manage aquifers. This paperexplores the potential of seismic data as a tool for these purposes. Buildingupon prior studies employing synthetic data, our investigation progresses toanalyzing measured data acquired from a field site in Laukaa, Finland.Utilizing neural networks, we directly recover water volume from seismic data,eliminating the need for independent determinations of geometry and porosity.The study involves simulating wave propagation through a coupledporoviscoelastic-viscoelastic medium, employing a three-dimensionaldiscontinuous Galerkin method coupled with an Adams-Bashforth time-steppingscheme. The proposed approach is tested on both field measurements from theLaukaa site and synthetic data, aiming to enhance methodologies for preciselymapping water volume and providing invaluable insights for the development ofsustainable water management strategies.

Seismic monitoring of water volume in a porous storage: A field-data study