HR: 1340h
AN: H23B-1321 [WITHDRAWN]    [Abstracts]
TI: An Efficient Importance Sampling Approach for Simulating Contaminant Transport in Heterogeneous Porous Media
AU: * Lu, Z
EM: zhiming@lanl.gov
AF: Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545, United States
AU: Lu, C
EM: clu@lanl.gov
AF: Los Alamos National Laboratory, MS T003, Los Alamos, NM 87545, United States
AB: In many applications, one may be interested in estimating the probability of a released solute reaching a particular target region. The conventional Monte Carlo method can be used for such a purpose. However, if the probability to be estimated is relatively small, an extreme large number of realizations (or particles) may be required to obtain a reasonably accurate estimate. In this study, we introduce a new approach, importance sampling Monte Carlo simulation, to efficiently estimate such a small probability. In the conventional Monte Carlo simulations, the probability of interest is derived from an ensemble of all possible trajectories that are attributed to heterogeneity of the hydraulic conductivity field. In the importance sampling approach, such trajectories are taken from a modified ensemble so that more solute particles will reach the target region. We do so by adding an artificial spatially-varying velocity field to the true velocity field. Since the samples are taken from a biased ensemble, the outputs from simulations are then weighted in such a way that the bias introduced by sampling from the modified ensemble will be exactly corrected. The general procedure of this importance sampling approach as well as its applicability to subsurface transport problems has been illustrated using a simple example for which analytical solution is available. The comparison of results from the analytical solution, the conventional Monte Carlo simulations, and importance sampling approach demonstrates that the latter is computational much more efficient than the conventional Monte Carlo method, especially when the probability of interest in very small.
DE: 1805 Computational hydrology
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting