HR: 1340h
AN: H13A-0398    [Abstracts]
TI: General approach for evaluation parameter uncertainty based on inverse model sensitivities
AU: * Vesselinov, V V
EM: vvv@lanl.gov
AF: Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87505 United States
AB: In the most general case, inverse model sensitivities represent partial derivatives of inverse estimates of model parameters in respect to various other parameters related to underlying conceptual, numerical and inverse models. In this definition, we distinguish two separate sets of model parameters: the former set represents parameters estimated in the inverse process; the latter set represents parameters that are excluded from the inverse process; however these parameters impact the inverse estimates and we have some knowledge about their uncertainty. The latter set of parameters can include calibration targets, computational grid resolution, accuracy of the numerical solver, priors, boundary condition terms, etc. The uncertainties in these parameters can be efficiently propagated to the uncertainty of inverse estimates using the inverse model sensitivities approach (Vesselinov, 2004). In this case a covariance matrix of estimation errors is computed based on Jacobian matrix consisting of inverse model sensitivities and a covariance matrix of errors of the unestimated model parameters. The proposed methodology can be applied for problems related to model development, optimization of data collection strategies, design of monitoring networks, etc. Its implementation is computationally intensive but can be performed efficiently through parallelization. Results based on synthetic and real case inverse problems are presented and discussed.
DE: 3260 Inverse theory
DE: 1829 Groundwater hydrology
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting