Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters

Over the past decades, advances in data collection and machine learning have paved the way for the development of autonomous simulation frameworks. Among these, many are capable not only of assimilating real-time data to correct their predictive shortcomings but also of improving their future performance through self-optimization. In hydrogeology, such techniques harbor great potential for informing sustainable management practices. Simulating the intricacies of groundwater flow requires an adequate representation of unknown, often highly heterogeneous geology. Unfortunately, it is difficult to reconcile the structural complexity demanded by realistic geology with the simplifying assumptions introduced in many calibration methods. The particle filter framework would provide the necessary versatility to retain such complex information but suffers from the curse of dimensionality, a fundamental limitation discouraging its use in systems with many unknowns. Due to the prevalence of such systems in hydrogeology, the particle filter has received little attention in groundwater modeling so far. In this study, we explore the combined use of dimension-reducing techniques and artificial parameter dynamics to enable a particle filter framework for a groundwater model. Exploiting freedom in the design of the dimension-reduction approach, we ensure consistency with a predefined geological pattern. The performance of the resulting optimizer is demonstrated in a synthetic test case for three such geological configurations and compared to two Ensemble Kalman Filter setups. Favorable results even for deliberately misspecified settings make us hopeful that nested particle filters may constitute a useful tool for geologically consistent real-time parameter optimization.