Coupled inversion of hydraulic and self‐potential data from transient outflow experiments to estimate soil petrophysical properties

Abstract

Hydraulicproperties of soils could play an important role in affecting the partitioning of precipitation in the critical zone. In addition to traditional approaches, in the last two decades, many geophysical methods have been used to aid the hydrologic characterization and measurement of geological materials. In particular, the self-potential (SP) method shows great potential in these hydrogeophysical applications. The objective of this study is to evaluate whether the addition of SP data can improve the estimation of hydraulic properties of soils in an outflow experiment. A stochastic, coupled hydrogeophysical inversion was developed, in which the governing equations were solved using the finite volume method and the parameter estimation was conducted using a Bayesian approach associated with the Markov chain Monte Carlo technique. The results show that the addition of SP data in the inversion could reduce the uncertainty related to the estimated hydraulic parameters of soils and the length of the associated 95% confidence interval can be shortened by ∼1/3. It is also shown that the electrical properties of soils at saturated and unsaturated conditions may also be estimated from the outflow experiment when SP data are available. Compared with hydraulic parameters, the accuracy of the estimated electrical properties is slightly lower. Among them, the saturated streaming potential coupling coefficient C sat has the highest accuracy and lowest uncertainty since C sat directly influences the magnitude of SP signals. The accuracy of other electrical parameters is lower than that of C sat (and hydraulic parameters), and the associated uncertainty can be one order of magnitude larger.

Developing a soil column system to measure hydrogeophysical properties of unconsolidated sediment

“…It should be addressed that the corrected Δ V response is similar to the simulated SP signal of the synthetic sand during drainage, as reported in Xie et al. (2021).…”

“…The transient responses of the sand during drainage ( Q , h p , and Δ V ) are used to estimate the saturated/unsaturated properties of the sample using the coupled, stochastic hydrogeophysical inversion code developed in Xie et al. (2021). The inversion uses Markov Chain Monte Carlo (MCMC) technique to generate samples that follow the posterior distributions of the model parameters (Vrugt et al., 2003).…”