Structure-coupled joint inversion of geophysical and hydrological data

TL;DR

This paper presents a structural coupling approach for joint inversion of geophysical and hydrological data, enhancing the resolution of hydraulic conductivity estimates without relying on petrophysical relations.

Contribution

The paper introduces a model coupling strategy based on structural similarity constraints that improves joint inversion of geophysical and hydrological data without requiring petrophysical relations.

Findings

Synthetic data tests showed improved resolution of hydraulic conductivity.

Field data application yielded hydraulic conductivities consistent with flowmeter data.

Structural coupling enhances parameter estimation accuracy.

Abstract

In groundwater hydrology, geophysical imaging holds considerable promise for improving parameter estimation, due to the generally high resolution and spatial coverage of geophysical data. However, inversion of geophysical data alone cannot unveil the distribution of hydraulic conductivity. Jointly inverting geophysical and hydrological data allows us to benefit from the advantages of geophysical imaging and, at the same time, recover the hydrological parameters of interest. We have applied a coupling strategy between geophysical and hydrological models that is based on structural similarity constraints. Model combinations, for which the spatial gradients of the inferred parameter fields are not aligned in parallel, are penalized in the inversion. This structural coupling does not require introducing a potentially weak, unknown, and nonstationary petrophysical relation to link the models. The method was first tested on synthetic data sets and then applied to two combinations of geophysical/hydrological data sets from a saturated gravel aquifer in northern Switzerland. Crosshole ground-penetrating radar (GPR) traveltimes were jointly inverted with hydraulic tomography data, as well as with tracer mean arrival times, to retrieve the 2D distribution of GPR velocities and hydraulic conductivities. In the synthetic case, incorporating the GPR data through a joint inversion framework improved the resolution and localization properties of the estimated hydraulic conductivity field. For the field study, recovered hydraulic conductivities were in general agreement with flowmeter data.