Speaker: Amalia Kokkinaki (PhD, Assistant Professor, Environmental Science, Environmental Engineering, University of San Francisco)
Title: Large-scale inverse problems and data assimilation in hydrogeology: developments and challenges
Abstract: In hydrogeology, inverse problems and data assimilation are used to estimate the properties of the subsurface using noisy, indirect measurements, as well as to track fluid movement through the soil or rock matrix. Applications include characterization for improved site cleanup, water resources management, and identification of contaminant sources. The corresponding inverse problems range from weakly nonlinear, such as pressure dissipation in mildly heterogeneous formations, to strongly nonlinear, such as multiphase flow in heterogeneous formations. A variety of techniques have been used over the last two decades to tackle such inverse problems, including deterministic/regularization based techniques to stochastic Bayesian estimation techniques. In this talk, a review of these methods will be presented, focusing on methods that are applicable for large scale systems with thousands to millions of unknowns, and specifically addressing the tradeoff between computational efficiency and estimation accuracy. The challenges associated with strongly non-linear problems, and Kalman Filtering variants that can address such problems will be discussed. The talk will close with an overview of current research needs for inverse modeling methods in the field of hydrogeology.