Joint inversion in hydrogeophysics and near-surface geophysics

Abstract

The near-surface environment is often too complex to enable inference of hydrological and environmental variables using one geophysical data type alone. Joint inversion and coupled inverse modeling involving numerical flow- and transport simulators have, in the last decade, played important roles in pushing applications towards increasingly challenging targets. Joint inversion of geophysical data that is based on structural constraints is often favored over model coupling based on explicit petrophysical relationships. More specifically, cross-gradient joint inversion has been applied to a wide range of near-surface applications and geophysical data types. To infer hydrological subsurface properties, the most appropriate approach is often to use temporal changes in geophysical data that can be related to hydrological state variables. This allows using geophysical data as indirect hydrological observables, while the coupling with a flow- and transport simulator ensures physical consistency. Future research avenues include investigating the validity of different coupling strategies at various scales, the spatial statistics of near-surface petrophysical relationships, the influence of the model conceptualization, fully probabilistic joint inversions, and how to include complex prior information in the joint inversion.