HR: 13:55h
AN: H33M-02    [Abstracts]
TI: A data domain correlation approach for joint inversion of time-lapse head, concentration, and electrical resistivity data
AU: * Johnson, T
EM: timothy.johnson@inl.gov
AF: Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-2107, United States
AU: Versteeg, R
EM: roelof.versteeg@inl.gov
AF: Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-2107, United States
AU: Huang, H
EM: hai.huang@inl.gov
AF: Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID 83415-2107, United States
AB: Timelapse electrical resistivity tomography (ERT) has been used extensively to monitor fluid movement in the subsurface. The underlying basis of this method is that changes in fluid conductivity caused by the movement of conductive (or resistive) fluids result in a change in the subsurface electrical potential distribution measured during the ERT experiment. As fluid transport is controlled in large part by hydrogeologic properties such as porosity and hydraulic conductivity, time-lapse ERT data can (theoretically) be used to constrain inverse estimates of these properties. The primary obstacle inhibiting the use of ERT data directly for inverse estimates of hydrogeologic properties is the lack of a generally applicable transform relating bulk conductivity to fluid conductivity. In general, while such transforms exist, they will be formation dependent. In order to eliminate the necessity of a petrophysical transform in estimating hydrogeologic properties from timelapse ERT data we have developed an approach which utilizes a proxy predicted transient change in electrical conductivity, which is highly correlated to observed transient changes in electrical potential. This correlation is based upon the correlation between changes in fluid conductivity and changes in bulk conductivity. We formulate the inversion to find a hydraulic conductivity distribution that maximizes the correlation between this proxy predicted data and the observed potential data (in addition to hydraulic head and fluid conductivity measurements). Thus, our joint inversion is formulated to estimate hydraulic conductivity conditional to head, fluid concentration, and resistivity measurements, where the resistivity term of the objective function is based on maximizing the correlation between the proxy and observed ERT data. In our paper we will describe the proxy ERT data formulation and demonstrate the correlation between the proxy and observed ERT data. We will also present synthetic examples of our approach.
DE: 1829 Groundwater hydrology
DE: 1835 Hydrogeophysics
DE: 1846 Model calibration (3333)
DE: 1847 Modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting