HR: 16:00h
AN: H14C-01 INVITED    [Abstracts]
TI: Multiscale Hydrogeophysical Data Integration for Parameterization of Transport Model at Savannah River Site
AU: * Kowalsky, M B
EM: MBKowalsky@lbl.gov
AF: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M.S. 90-1116, Berkeley, CA 94720, United States
AU: Hubbard, S S
EM: SSHubbard@lbl.gov
AF: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M.S. 90-1116, Berkeley, CA 94720, United States
AU: Chen, J
EM: JChen@lbl.gov
AF: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M.S. 90-1116, Berkeley, CA 94720, United States
AU: Peterson, J E
EM: JEPeterson@lbl.gov
AF: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M.S. 90-1116, Berkeley, CA 94720, United States
AU: Flach, G P
EM: gregory.flach@srnl.doe.gov
AF: Savannah River National Laboratory, Savannah River Site, 773-42A, 211, Aiken, SC 29808, United States
AB: We developed a multiscale characterization approach that integrates various data types, collected at differing measurement scales, to improve transport predictions at the Savannah River Site. The approach uses surface- based and cross-borehole-based geophysical data, and wellbore data to provide input for a site-wide dual- domain transport model. The transport model, also being developed in the characterization effort, incorporates the key interactions between mobile and immobile regions that are expected to play a role in long-term plume evolution. Analysis of existing characterization data suggests that the study site can be described by two hydrofacies, with one that is on average more mobile than the other, and that these hydrofacies are related to two lithofacies (L=0 and L=1, respectively). A statistical model is used to estimate the unknown proportion of lithofacies (FI) in each pixel I and the unknown lithofacies type (Li) in each pixel i, given the following data sets: (1) large-scale surface- based geophysical data; (2) small-scale cross-borehole geophysical data; and (3) small-scale wellbore data, such as from geophysical logs, flowmeter logs, or core samples. Using a Bayesian framework, we derive a conditional probability distribution of the unknown variables (FI and Li) for all of their respective pixel locations. The resulting distribution depends on the probability distribution of the large-scale geophysical data given the unknown values of FI; the probability distribution of FI given the unknown values of Li; and the probability distribution of unknown Li given the small-scale cross-borehole and wellbore data. The MCMC sampling method is used to efficiently draw samples from the conditional probability distribution, so that the probability distributions of the unknowns can be inferred. We are currently applying the approach to synthetic data and petrophysical relationships that are representative of site conditions. The approach is being modified, as needed, based on ongoing collection and reduction of multi- scale hydrological and geophysical data, and on the evolving characterization objectives that are identified during development of the site-wide transport model. Once refined, the multiscale approach will be used to characterize the relevant field-scale properties at the study site and to parameterize the transport model. This approach expands the scale of hydrogeophysical investigations, traditionally restricted to local-scale regions between closely spaced boreholes (~10m), to site-wide scales that are relevant for modeling plume fate and transport.
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1835 Hydrogeophysics
DE: 3265 Stochastic processes (3235, 4468, 4475, 7857)
DE: 3275 Uncertainty quantification (1873)
SC: Hydrology [H]
MN: 2007 Fall Meeting