Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities
Article first published online: 9 NOV 2010
Copyright 2010 by the American Geophysical Union.
Water Resources Research
Volume 46, Issue 11, November 2010
How to Cite
2010), Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities, Water Resour. Res., 46, W11514, doi:10.1029/2009WR008340., and (
- Issue published online: 9 NOV 2010
- Article first published online: 9 NOV 2010
- Manuscript Accepted: 1 JUL 2010
- Manuscript Revised: 17 JUN 2010
- Manuscript Received: 1 JUL 2009
- tracer test;
 Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.