Bayesian Inference of Groundwater Contamination Source

Lots of uncertainty exists in the groundwater modeling, e.g. hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This work proposes a dynamic framework in contamination source identification of groundwater. Markov Chain Monte Carlo(MCMC) is being applied to infer the possible location and magnitude of contamination source. Unlike other inverse-problem approach to provide single but maybe untrue solution, the MCMC algorithm provides distribution over estimated parameters.