Interactive multi-objective inverse groundwater modeling for the WIPP site

This paper presents ongoing research on building an interactive and multi-objective framework to solve the groundwater inverse problem. Our research has shown that the inherent instability and non-uniqueness of this problem can be improved by incorporating expert knowledge about the hydro-geology of the site. The interactive multi-objective genetic algorithm (IMOGA) considers user preference (for different transmissivity fields) as an additional objective along with quantitative calibration measures, converging to a set of Pareto optimal solutions representing the best trade-off among all (qualitative as well as quantitative) objectives. An important part of groundwater inversion is assessing parameter uncertainty and its effect upon model predictions. To assess the uncertainty in prediction we a multi-level sampling approach is used that incorporates uncertainty in both large-scale trends and the small-scale stochastic variability. The large-scale uncertainty is modeled using a Bayesian approach where both calibration error and a prior transmissivity field (as specified by the expert through the interactive rankings of the IMOGA) are considered. A geostatistical approach is adopted for the small-scale uncertainty, which is considered to be unconditional and auto-correlated with a specified covariance structure. The prediction model is run using all simulated fields to get the distribution of predictions. This methodology is being applied to a field-scale case study based on the Waste Isolation Pilot Plant (WIPP) situated in Carlsbad, New Mexico. This work is still in progress and results will be presented at the EWRI conference.