English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

In this work we propose an Uncertainty Quantification methodology forsedimentary basins evolution under mechanical and geochemical compactionprocesses, which we model as a coupled, time-dependent, non-linear,monodimensional (depth-only) system of PDEs with uncertain parameters. While inprevious works (Formaggia et al. 2013, Porta et al., 2014) we assumed asimplified depositional history with only one material, in this work weconsider multi-layered basins, in which each layer is characterized by adifferent material, and hence by different properties. This setting requiresseveral improvements with respect to our earlier works, both concerning thedeterministic solver and the stochastic discretization. On the deterministicside, we replace the previous fixed-point iterative solver with a moreefficient Newton solver at each step of the time-discretization. On thestochastic side, the multi-layered structure gives rise to discontinuities inthe dependence of the state variables on the uncertain parameters, that need anappropriate treatment for surrogate modeling techniques, such as sparse grids,to be effective. We propose an innovative methodology to this end which relieson a change of coordinate system to align the discontinuities of the targetfunction within the random parameter space. The reference coordinate system isbuilt upon exploiting physical features of the problem at hand. We employ thelocations of material interfaces, which display a smooth dependence on therandom parameters and are therefore amenable to sparse grid polynomialapproximations. We showcase the capabilities of our numerical methodologiesthrough two synthetic test cases. In particular, we show that our methodologyreproduces with high accuracy multi-modal probability density functionsdisplayed by target state variables (e.g., porosity).

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins