See more from this Session: Advances In Soil and Vadose Zone Hydrology: The Contributions of Glendon Gee: II
Monday, October 17, 2011
Henry Gonzalez Convention Center, Hall C, Street Level
In this study, an efficient Bayesian method based on the adaptive sparse grid interpolation is used to solve the contaminant source identification problem. The unknown parameters that characterize the contaminant source are treated as random variables and identified by the Bayesian approach. To alleviate the computational burden in the standard Markov Chain Monte Carlo (MCMC) method, a surrogate system with the polynomial form is constructed by the interpolation, which is based on the sparse grids rather than those based on the full tensor product. Furthermore, an adaptive scheme is used to take the different importance of the parameters into account. Therefore, the computational burden is significantly reduced. Once the surrogate system is determined, the likelihood can be evaluated by sampling the surrogate system directly, with very little computational cost. Numerical cases show that, under certain conditions, this method can greatly reduce the computational burden in the MCMC sampling and accurately identify the characteristics of the contaminant source.