HR: 10:50h
AN: H42B-03    [Abstracts]
TI: Estimating hydrogeological parameters in the vadose zone using tomographic GPR first- arrival traveltime data – applications of the eikonal solver within an MCMC-Bayesian inversion framework
AU: * Hou, Z
EM: hou2@buffalo.edu
AF: University at Buffalo, University at Buffalo, Buffalo, NY 14260, United States
AU: Rubin, Y
EM: rubin@ce.berkeley.edu
AF: University of California, Berkeley, University of California, Berkeley, Berkeley, CA 94720, United States
AU: Chen, J
EM: jchen@lbl.gov
AF: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, United States
AB: Tomographic ground-penetrating radar (GPR) techniques have been widely used for shallow subsurface characterization. One of the common practices using tomographic GPR is to use GPR first-arrival traveltimes for hydrological parameter estimation through hydrogeophysical inversion. Its applicability depends on the accuracy and efficiency of the numerical method which is used to calculate GPR traveltimes, as well as on the robustness and efficiency of the inversion approach. In this study, we adopt a finite-difference method for computing the first- arrival traveltimes by solving the eikonal equation in the celerity domain. This algorithm computes the head and diffraction wave paths and incorporates a fast sweeping method to obtain accurate first-arrival times in complex velocity models. We also introduce a Bayesian inversion framework based on Markov Chain Monte Carlo (MCMC) sampling methods. We test intensively the Bayesian inversion framework, together with the eikonal solver, through infiltration experiments under various flow conditions with different field geometry, soil heterogeneity, and flow initial and boundary conditions. We also evaluate the method by combining GPR first-arrival travletime data with neutron probe data collected at different times during the infiltration experiments. Results show that the inversion approach can effectively estimate the hydraulic parameters of interest and demonstrates the great benefits of coupling different yet complementary information for hydrogeological site characterization.
DE: 1829 Groundwater hydrology
DE: 1835 Hydrogeophysics
DE: 1869 Stochastic hydrology
DE: 1875 Vadose zone
DE: 4499 General or miscellaneous
SC: Hydrology [H]
MN: 2007 Fall Meeting