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 screening procedure to identify relevant parameters and their assumed probability distributions. After selecting a restricted set of important parameters these are calibrated against available information, i.e., the depth of interpreted stratigraphic surfaces. Because of the large costs associated with SFM simulations, probability distributions of model parameters and outputs are obtained through a data driven reduced complexity model. Our study demonstrates the numerical approaches by considering a portion of the Porcupine Basin, Ireland. Results of the analysis are postprocessed to assess (i) the uncertainty and practical identifiability of model parameters given a set of observations, (ii) spatial distribution of lithologies. We analyse here the occurrences of sand bodies pinching against the continental slope, these systems likely resulting from gravity driven processes in deep sea environment.

Sensitivity of modelled passive margin stratigraphy to variations in sea level, sediment supply and subsidence

“…More recently, inversion techniques (Lessenger & Lerche, 1999) have been applied in conjunction with forward stratigraphic models to extract information about the environmental and tectonic conditions responsible for the development of stratigraphic sections (Bornholdt et al, 1999;Cross et al, 1999;Imhof & Sharma, 2006a;Karssenberg et al, 2007;Lessenger & Cross, 1996), and validated with synthetic or small-scale data sets (e.g. Crombez et al, 2020;Ducros et al, 2023;Imhof & Sharma, 2006b;Karssenberg et al, 2001;Mahmudova et al, 2023;Patani et al, 2021;Sacchi et al, 2015;Wijns et al, 2004). The inverse modelling aligns or calibrates the forward model with target data and produces an estimate of the most probable input parameters.…”