Effective sampling of behavioral subsurface parameter realizations assisted by surrogate models

Global sensitivity analysis and uncertainty quantification of nonlinear models may be performed using ensembles of model runs. However, already in moderately complex models many combinations of parameters, which appear reasonable by prior knowledge, can lead to unrealistic model outcomes, like perennial rivers that fall dry in the model or simulated severe floodings that have not been observed in the real system. We denote these parameter combinations with implausible outcome as “non-behavior”. Creating a sufficiently large ensemble of behavioral model realizations can be computationally prohibitive, if the individual model runs are expensive and only a small fraction of the parameter space is behavioral. In this work, we design a stochastic, sequential sampling engine that utilizes fast and simple surrogate models trained on past realizations of the original, complex model. Our engine uses the surrogate model to estimate whether a candidate realization will turn out to be behavioral or not. Only parameter sets that with a reasonable certainty of being behavioral (as predicted by the surrogate model) are simulated using the original, complex model. For a subsurface flow model of a small south-western German catchment, we can show high accuracy in the surrogate model predictions regarding the behavioral status of the parameter sets. This increases the fraction of behavioral model runs (actually computed with the original, complex model) over total complex-models runs to 20-90%, compared to 0.1% without our method (e.g., using brute-force Monte Carlo sampling).  This notable performance increase depends on the choice of surrogate modeling technique. Towards this end, we consider both Gaussian Process Emulation (GPE) and models based on polynomials of active variables determined by Active Subspace decomposition as surrogate models. For the GPE-based surrogate model, we also compare random search and active learning strategies for the training of the surrogate model.