HR: 14:25h
AN: NS43A-04 INVITED    [Abstracts]
TI: Joint Inversion of Hydrological and Geophysical Data: Too Much Information?
AU: * Finsterle, S
EM: SAFinsterle@lbl.gov
AF: Lawrence Berkeley National Laboratory, Earth Sciences Division, One Cyclotron Road, MS 90-1116, Berkeley, CA 94720, United States
AU: Kowalsky, M B
EM: MBKowalsky@lbl.gov
AF: Lawrence Berkeley National Laboratory, Earth Sciences Division, One Cyclotron Road, MS 90-1116, Berkeley, CA 94720, United States
AU: Oberd{ö}rster, C
EM: c.oberdoerster@fz-juelich.de
AF: ICG-4 (Agrosphere), Forschungszentrum J{ü}lich GmbH, Wilhelm-Johnen-Strasse, J{ü}lich, 52425, Germany
AU: Lehikoinen, A
EM: anssi.lehikoinen@uku.fi
AF: Department of Physics, University of Kuopio, P.O. Box 1627, Kuopio, 70211, Finland
AB: Prediction of fluid flow and contaminant transport in unsaturated, highly heterogeneous porous and fractured media requires high-resolution maps that depict spatially variable, process-specific parameters. While geophysical methods have the potential to image subsurface properties with relatively high resolution and spatial coverage, additional information is needed to (a) determine parameters of petrophysical models that relate measured geophysical data to the flow and transport properties of interest, (b) to remove, quantify, or reduce systematic errors and artifacts, and (c) to justify regularization schemes. The often complementary information contained in geophysical and hydrological data makes a joint inversion approach an attractive method to solve the estimation-identification problem in near-surface hydrology; we have implemented this approach by combining (a) a multiphase flow simulator, (b) geophysical forward models, such as ground penetrating radar and electrical resistivity tomography, and (c) nonlinear optimization algorithms. While adding new data types is generally desirable to address the ill-posedness of the inverse problem, it may lead to new challenges regarding parameterization of the problem, potential inconsistencies between the data sets, relative weighting issues, and the introduction of an estimation bias due to increased systematic errors. The value of information added to a joint inversion framework will be discussed from both a fundamental and practical perspective. This work was supported, in part, by the U.S. Dept. of Energy under Contract No. DE-AC02-05CH11231.
UR: http://www-esd.lbl.gov/iTOUGH2
DE: 0520 Data analysis: algorithms and implementation
DE: 0545 Modeling (4255)
DE: 1835 Hydrogeophysics
DE: 1846 Model calibration (3333)
SC: Near-Surface Geophysics [NS]
MN: 2007 Fall Meeting