State and Parameter Estimation with an SIR Particle Filter in a Three-Dimensional Groundwater Pollutant Transport Model

Abstract

AbstractMathematical modeling of the contaminants in the subsurface is important to predict the spread of the plume as well as for risk assessment. A three-dimensional subsurface contaminant transport model with an instantaneous input is developed to represent the high dimensionality of the real field. Because of the inherent randomness, heterogeneity of the transport process, macrodispersion, non-Fickian motion, and ergodicity, general assumptions of linearity and Gaussian distribution do not hold for the real field. Therefore, a state-space transport model for the nonlinear and non-Gaussian system is proposed in this study. In this paper, the state variable (concentration vector) and parameter (first-order decay) are updated with the simulated measurements. A particle filter, which is a sequential Monte Carlo method, provides a rigorous general framework for dynamic state estimation problems in the Bayesian scheme. In this paper, the reactive contaminant transport in the subsurface is treated as a dynam...