HR: 1340h
AN: H13B-0407    [Abstracts]
TI: Inverse and forward modeling under uncertainty using MRE-based Bayesian approach
AU: * Hou, Z
EM: hou@berkeley.edu
AF: CEE, U.C. Berkeley, 2108 Shattuck Ave., Berkeley, CA 94704 United States
AU: Rubin, Y
EM: rubin@ce.berkeley.edu
AF: CEE, U.C. Berkeley, 2108 Shattuck Ave., Berkeley, CA 94704 United States
AB: A stochastic inverse approach for subsurface characterization is proposed and applied to shallow vadose zone at a winery field site in north California and to a gas reservoir at the Ormen Lange field site in the North Sea. The approach is formulated in a Bayesian-stochastic framework, whereby the unknown parameters are identified in terms of their statistical moments or their probabilities. Instead of the traditional single-valued estimation /prediction provided by deterministic methods, the approach gives a probability distribution for an unknown parameter. This allows calculating the mean, the mode, and the confidence interval, which is useful for a rational treatment of uncertainty and its consequences. The approach also allows incorporating data of various types and different error levels, including measurements of state variables as well as information such as bounds on or statistical moments of the unknown parameters, which may represent prior information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. The approach is tested in field sites for flow parameters identification and soil moisture estimation in the vadose zone and for gas saturation estimation at great depth below the ocean floor. Results indicate the potential of coupling various types of field data within a MRE-based Bayesian formalism for improving the estimation of the parameters of interest.
DE: 1899 General or miscellaneous
DE: 1829 Groundwater hydrology
DE: 1866 Soil moisture
DE: 1869 Stochastic processes
DE: 1875 Unsaturated zone
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting