Approaches to improve the robustness of the Comparison Model Method for the inverse problem of groundwater hydrology

Abstract

Indirect inversion approaches are widely used in Geosciences, and in particular also for the identification of the hydraulic properties of aquifers. Nevertheless, their application requires a substantial number of model evaluation (forward problem) runs, a task that for complex problems can be computationally intensive. Reducing this computational burden is an active research topic, and many solutions, including the use of hybrid optimization methods, the use of physical proxies or again machine-learning tools allow to avoid considering the full physics of the problem when running a numerical implementation of the forward problem.