Modelling parametric uncertainty in large-scale stratigraphic simulations.

ABSTRACT: We combine forward stratigraphic models with a suite of uncertainty quantification and stochastic model calibration algorithms for the characterization of sedimentary successions in large scale systems. The analysis focuses on the information value provided by a probabilistic approach in the modelling of large-scale sedimentary basins. Stratigraphic forward models (SFMs) require a large number of input parameters usually affected by uncertainty. Thus, model calibration requires considerable time both in terms of human and computational resources, an issue currently limiting the applications of SFMs. Our work tackles this issue through the combination of sensitivity analysis, model reduction techniques and machine learning-based optimization algorithms. We first employ a two-step parameter

SUBMITTER: Mahmudova A 

PROVIDER: S-EPMC9842771 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature