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The origin of laterally accreted deposits in ancient deep marine successions is often controversial. Indeed, not always do these features imply the occurrence of meanders or high-sinuosity turbidite channels, but they can be generated by other causes, such as sediment-gravity-flow dynamics controlled by the morphology of tectonically confined mini-basins. This work discusses laterally accreted deposits composed of sharp-based, normally graded beds in a very small tectonically controlled mini-basin. These beds, characterized by a well-defined asymmetrical cross-current facies tract, form well-developed lateral-accretion surfaces dipping in directions ranging between W and SW, and perpendicular to the paleocurrents directed towards the N. For this reason, these deposits have always been interpreted as point bars related to meandering channels. A new detailed stratigraphic framework and facies analysis have led to an alternative interpretation, namely that these deposits record lateral deflections of small volume, longitudinally segregated turbidite dense flows against a structurally controlled morphological high. This interpretation is also supported by a comparison to other tectonically controlled turbidite systems that are characterized by higher degrees of efficiency but show similar laterally accreted deposits and cross-current facies tracts.

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.