Input uncertainty and conditioning in siliciclastic process modelling

Abstract

Abstract Deterministic forward sedimentary process models enhance our quantitative understanding of sedimentary systems. They are also being used increasingly to assist in the reconstruction of the geological past and the inference of the present configuration of sedimentary deposits. Such usage presents the challenge of having to establish the initial and boundary conditions that will cause the model’s output to match present-day observations. This constitutes an inversion problem that can sometimes be handled by traditional optimization methods. Clastic sedimentation models, however, often incorporate complex non-linear relationships that preclude the use of these techniques. The problem must then be handled statistically by relaxing the requirement of honouring exact observations and matching only the spatial variability of the observed deposits. Recent advances in control of non-linear dynamic systems may also shed light on possible solutions to the inversion problem in siliciclastic models. This paper reviews known approaches to problems related to input uncertainty and conditioning, and presents original preliminary results on control of sedimentation models.