HR: 14:55h
AN: NS43A-06 INVITED    [Abstracts]
TI: Conditional stochastic simulation using high-resolution geophysical data for the local-scale characterization of heterogeneous aquifers
AU: * Holliger, K
EM: klaus.holliger@unil.ch
AF: Institute of Geophysics, University of Lausanne, Lausanne, 1015, Switzerland
AU: Dafflon, B
EM: baptiste.dafflon@unil.ch
AF: Institute of Geophysics, University of Lausanne, Lausanne, 1015, Switzerland
AU: Irving, J
EM: james.irving@unil.ch
AF: Institute of Geophysics, University of Lausanne, Lausanne, 1015, Switzerland
AB: Simulated annealing (SA) provides a flexible means of integrating diverse types of data for the purpose of conditional stochastic simulation. Although the SA technique has been widely used in reservoir characterization studies, relatively little work has been done on the application of this method to hydrogeophysical problems. We present research that builds on previous work involving the use of simulated annealing for near-surface geophysical and hydrological data integration. Here, we introduce a new SA algorithm that provides a significant advancement in the way that large-scale structural information from a geophysical experiment is incorporated into the output realizations. Our SA algorithm contains two key features. First, model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter, conditioned to the available geophysical data. This is the only place where geophysical information is utilized in our algorithm, and is in contrast to more traditional SA approaches where model perturbations are made through the swapping of values in the simulation grid, and agreement with soft data is enforced through a correlation coefficient constraint. The second major feature of our SA algorithm is the way in which stochastic information is introduced into the output realizations. Instead of constraining realizations to match a target covariance model at a wide range of spatial lags, we let the perturbation approach of drawing from a conditional distribution control the large-scale subsurface structure, and we stochastically constrain the output realizations only at smaller lags where the available data cannot provide enough information. With this strategy, we allow the geophysical data to have more due control over the output realizations in comparison with previously published SA algorithms. In addition, since the only objective function required in our approach is a covariance constraint at small lags, the algorithm has improved convergence and increased computational speed over more traditional SA methods. We show the results of using our procedure to integrate porosity log and crosshole georadar data to generate realizations of the subsurface porosity field. We do this for a synthetic example, and then for a field data set collected at the Boise Hydrogeophysical Research Site.
DE: 1829 Groundwater hydrology
DE: 1835 Hydrogeophysics
DE: 3252 Spatial analysis (0500)
DE: 3275 Uncertainty quantification (1873)
DE: 6982 Tomography and imaging (7270, 8180)
SC: Near-Surface Geophysics [NS]
MN: 2007 Fall Meeting