Technical Note: Improved Sampling of Behavioral Subsurface Flow Model Parameters Using Active Subspaces

Abstract

Abstract. In global sensitivity analysis and ensemble-based model calibration it is essential to create a large enough sample of model simulations with different parameters, which all yield plausible model results. This can be difficult if a-priori plausible parameter combinations frequently yield non-behavioral model results. In a previous study (Erdal and Cirpka, 2019), we developed and tested a parameter-sampling scheme based on active subspace decomposition. While in principle this scheme worked well, it still implied testing a substantial fraction of parameter combinations that ultimately had to be discarded because of implausible model results. This technical note presents an improved sampling scheme and illustrates its simplicity and efficiency by a small test case. The new sampling scheme can be tuned to either outperform the original implementation by improving the sampling efficiency while maintaining the accuracy of the result, or by improving the accuracy of the result while maintaining the sampling efficiency.