Coupled modeling of hydrogeochemical and electrical resistivity data for exploring the impact of recharge on subsurface contamination
- Lawrence Berkeley National Laboratory (LBNL)
- ORNL
- University of Tennessee, Knoxville (UTK)
The application of geophysical methods, in particular, electrical resistivity measurements, may be useful for monitoring subsurface contamination. However, interpreting geophysical data without additional data and without considering the associated hydrogeochemical processes is challenging since the geophysical response is sensitive to not only heterogeneity in rock properties but also to the saturation and chemical composition of pore fluids. We present an inverse modeling framework that incorporates the simulation of hydrogeochemical processes and time-lapse electrical resistivity data and apply it to various borehole and cross-borehole data sets collected in 2008 near the S-3 Ponds at the U.S. Department of Energy's Oak Ridge Integrated Field Research Challenge site, where efforts are underway to better understand freshwater recharge and associated contaminant dilution. Our goal is to show that the coupled hydrogeochemical-geophysical modeling framework can be used to (1) develop a model that honors all the available data sets, (2) help understand the response of the geophysical data to subsurface properties and processes at the site, and (3) allow for the estimation of petrophysical parameters needed for interpreting the geophysical data. We present a series of cases involving different data sets and increasingly complex models and find that the approach provides useful information about soil properties, recharge-related transport processes, and the geophysical response. Spatial heterogeneity of the petrophysical model can be described sufficiently with two layers, and its parameters can be estimated concurrently with the hydrogeochemical parameters. For successful application of the approach, the parameters of interest must be sensitive to the available data, and the experimental conditions must be carefully modeled.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1037055
- Journal Information:
- Water Resources Research, Vol. 47; ISSN 0043-1397
- Country of Publication:
- United States
- Language:
- English
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