Distributed soil moisture from crosshole ground-penetrating radar travel times using stochastic inversion

TL;DR

This paper introduces a Bayesian stochastic inversion method using GPR traveltime data to estimate high-resolution soil moisture distribution, demonstrating improved accuracy and convergence over traditional methods through synthetic and real-world case studies.

Contribution

The paper presents a novel Bayesian inversion framework with DCT parameterization for high-resolution soil moisture estimation from GPR data, outperforming classical approaches.

Findings

DCT parameterization yields more accurate soil moisture estimates.

The Bayesian approach provides a more comprehensive uncertainty quantification.

Lateral anisotropy is crucial for reliable soil moisture variability estimation.

Abstract

Geophysical methods offer several key advantages over conventional subsurface measurement approaches, yet their use for hydrologic interpretation is often problematic. Here, we introduce theory and concepts of a novel Bayesian approach for high-resolution soil moisture estimation using traveltime observations from crosshole Ground Penetrating Radar (GPR) experiments. The recently developed Multi-try DiffeRential Evolution Adaptive Metropolis with sampling from past states, MT-DREAM(ZS) is being used to infer, as closely and consistently as possible, the posterior distribution of spatially distributed vadose zone soil moisture and/or porosity under saturated conditions. Two differing and opposing model parameterization schemes are being considered, one involving a classical uniform grid discretization and the other based on a discrete cosine transformation (DCT). We illustrate our approach using two different case studies involving geophysical data from a synthetic water tracer infiltration study and a real-world field study under saturated conditions. Our results demonstrate that the DCT parameterization yields the most accurate estimates of distributed soil moisture for a large range of spatial resolutions, and superior MCMC convergence rates. In addition, DCT is admirably suited to investigate and quantify the effects of model truncation errors on the MT-DREAM(ZS) inversion results. For the field example, lateral anisotropy needs to be enforced to derive reliable soil moisture variability. Our results also demonstrate that the posterior soil moisture uncertainty derived with the proposed Bayesian procedure is significantly larger than its counterpart estimated from classical smoothness-constrained deterministic inversions.