University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

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  • UserLorenzo Tamellini (Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI))
  • ClockWednesday 14 February 2018, 11:00-13:00
  • HouseSeminar Room 2, Newton Institute.

If you have a question about this talk, please contact info@newton.ac.uk.

UNQ - Uncertainty quantification for complex systems: theory and methodologies

This presentation is joint work of:   Ivo Colombo, Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy Fabio Nobile, CSQI MATHICSE, Ecole Polytechnique Fédérale de Lausanne, Switzerland Giovanni Porta, Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy Anna Scotti, MOX , Dipartimento di Matematica, Politecnico di Milano, Italy Lorenzo Tamellini, CNR Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Pavia, Italy     In this work we propose an Uncertainty Quantification methodology for the evolution of sedimentary basins undergoing mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, non-linear, monodimensional (depth-only) system of PDEs with uncertain parameters.   Specifically, we consider multi-layered basins, in which each layer is characterized by a different material. The multi-layered structure gives rise to discontinuities in the dependence of the state variables on the uncertain parameters. Because of these discontinuites, an appropriate treatment is needed for surrogate modeling techniques such as sparse grids to be effective.   To this end, we propose a two-steps methodology which relies on a change of coordinate system to align the discontinuities of the target function within the random parameter space. Once this alignement has been computed, a standard sparse grid approximation of the state variables can be performed. The effectiveness of this procedure is due to the fact that the physical locations of the interfaces among layers feature a smooth dependence on the random parameters and are therefore amenable to sparse grid polynomial approximations.   We showcase the capabilities of our numerical methodologies through some synthetic test cases.  


This talk is part of the Isaac Newton Institute Seminar Series series.

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