  With global groundwater levels undergoing rapid declines, there is an urgent
need for advanced methodologies to monitor and manage aquifers. This paper
explores the potential of seismic data as a tool for these purposes. Building
upon prior studies employing synthetic data, our investigation progresses to
analyzing 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 coupled
poroviscoelastic-viscoelastic medium, employing a three-dimensional
discontinuous Galerkin method coupled with an Adams-Bashforth time-stepping
scheme. The proposed approach is tested on both field measurements from the
Laukaa site and synthetic data, aiming to enhance methodologies for precisely
mapping water volume and providing invaluable insights for the development of
sustainable water management strategies.
