HR: 0800h
AN: H41E-0459    [Abstracts]
TI: Potential of Electrical Resistivity Tomography Characterizing Transport Processes in Groundwater - Synthetic Case Studies
AU: * Englert, A
EM: a.englert@fz-juelich.de
AF: Agrosphere Institute (ICG IV), Research Centre Jülich, Wilhelm-Johnen-Straße, Jülich, 52425 Germany
AU: Zhu, J
EM: jfzhu@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, 1133 E. North Campus Drive, Tucson, 85721
AU: Kemna, A
EM: a.kemna@fz-juelich.de
AF: Agrosphere Institute (ICG IV), Research Centre Jülich, Wilhelm-Johnen-Straße, Jülich, 52425 Germany
AU: Vanderborght, J
EM: j.vanderborght@fz-juelich.de
AF: Agrosphere Institute (ICG IV), Research Centre Jülich, Wilhelm-Johnen-Straße, Jülich, 52425 Germany
AU: Vereecken, H
EM: h.vereecken@fz-juelich.de
AF: Agrosphere Institute (ICG IV), Research Centre Jülich, Wilhelm-Johnen-Straße, Jülich, 52425 Germany
AU: Yeh, J
EM: ybiem@mac.hwr.arizona.edu
AF: Department of Hydrology and Water Resources, University of Arizona, 1133 E. North Campus Drive, Tucson, 85721
AB: Recent field studies showed that electrical (el.) resistivity tomography potentially is a viable tool for characterizing subsurface transport processes of solutes with el. conductivities different from the background. Approaches for interpreting the el. field measurements often rely on inversion procedures that use regularization. The use of regularization avoids the non-uniqueness issue associated with ill-posed el. resistivity inversion problem and leads to the smoothest solution. While the estimated el. conductivity field is smooth, it may not reflect the variability of the true el. conductivity field induced by the solute in the subsurface. Furthermore, it does not take advantage of our knowledge of solute transport processes and direct measurement of solute concentrations. As a consequence, the inversion approach may not maximize the usefulness of available information. Recently developed geostatistically based inversion approach (i.e., successive linear estimator, SLE) appears to be a promising tool that can overcome the non-uniqueness issue and maximize the utility of available information. It uses our prior knowledge of the spatial statistics of a solute plume (which can be derived from a stochastic hydraulic conductivity model using first-order approximations of stochastic flow and transport equations), direct measurements of solute concentrations, and electric field measurements to condition the el. conductivity estimates. In this study, we compare the inverse approach using regularization and the SLE for detecting a solute plume in the subsurface under different electrode sampling arrays. Our results show that as expected, with only sparse field measurements the SLE that includes statistical characteristics of the subsurface el. conductivity distribution and the direct measurements enhances the quality of the inversion. With increasing density electrode measurements, the effect of conditioning however diminishes. For a large-scale field problem where a dense sampling network is not available, information fusion using the SLE appears to be promising.
DE: 1829 Groundwater hydrology
DE: 1835 Hydrogeophysics
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: Fall Meeting 2005