# Joint inversion in hydrogeophysics and near-surface geophysics

## TL;DR

This paper reviews joint inversion techniques in hydrogeophysics and near-surface geophysics, emphasizing structural constraints, temporal data, and future research directions for improved subsurface property inference.

## Contribution

It highlights the importance of structural constraint-based joint inversion and discusses future research avenues in coupling strategies and probabilistic methods.

## Key findings

- Cross-gradient joint inversion is widely applied in near-surface geophysics.
- Temporal geophysical data can serve as indirect hydrological observables.
- Future research includes probabilistic joint inversions and complex prior information integration.

## 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.

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Source: https://tomesphere.com/paper/1701.01626