HR: 1340h
AN: NG43B-0569    [Abstracts]
TI: Three-Dimensional Bayesian Inversion of Hydrothermal Data Using Automatic Differentiation
AU: * Rath, V
EM: v.rath@geophysik.rwth-aachen.de
AF: Applied Geophysics RWTH Aachen University, Lochnerstr. 4-20, Aachen, D-52056 Germany
AU: Wolf, A
EM: wolf@sc.rwth-aachen.de
AF: Scientific Computing RWTH Aachen University, Seffenter Weg 23, Aachen, D-52074 Germany
AU: Bücker, M
EM: buecker@sc.rwth-aachen.de
AF: Scientific Computing RWTH Aachen University, Seffenter Weg 23, Aachen, D-52074 Germany
AB: We have developed an Bayesian inverse modeling tool for the joint inversion of hydraulic and thermal data. It is based on a well-known and well-tested forward modeling code, which solves the coupled steady state equations of heat and mass transfer. Because of the heat and pressure dependence of most petrophysical properties, this is a nonlinear problem. Different nonlinearities of petrophysical properties and pore-space models can be employed. The forward code was automatically differentiated to obtain partial derivative information. This information was used in several optimization schemes (Gauss-Newton, Quasi-Newton, Nonlinear Conjugate Gradients) to find the minimum of the Bayesian objective function. Additionally, this approach produces estimates of posterior covariances and related measures of uncertainty. These are complemented by a Monte Carlo module for special studies on subproblems. In this contribution, we will present several verification examples for the inverse code, and instructive synthetic investigations demonstrating the power of the approach. Additionally, some field results will be given, and further developments will be discussed, including the use of sensitivity information for the optimization of experimental design.
DE: 1846 Model calibration (3333)
DE: 3225 Numerical approximations and analysis (4260)
DE: 3260 Inverse theory
DE: 3275 Uncertainty quantification (1873)
DE: 8130 Heat generation and transport
SC: Nonlinear Geophysics [NG]
MN: Fall Meeting 2005