2022 CSDMS meeting-030

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"2) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part I" is not in the list (1) Modeling Water Movement and Reservoir Operations with mosartwmpy, 3) Introduction to Landlab, 4) Xarray for Scalable Scientific Data Analysis, 5) Will not attend a clinic) of allowed values for the "CSDMS meeting select clinics2 2022" property. "3) Image Segmentation using Deep Learning and Human-In-the-Loop Machine Learning - Part II" is not in the list (1) Component Creation with Landlab, 2) Rapid hypothesis testing and analysis with the open-source delta model pyDeltaRCM, 4) Publishing Reproducible Computational Research with the Whole Tale, 5) Will not attend a clinic) of allowed values for the "CSDMS meeting select clinics3 2022" property.

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Inverting passive margin stratigraphy for marine sediment transport dynamics over geologic time

Charles Shobe, (he/him),West Virginia University Morgantown West Virginia, United States. Charles.shobe@mail.wvu.edu
Jean Braun, GFZ Potsdam Potsdam , Germany. jbraun@gfz-potsdam.de
Xiaoping Yuan, China University of Geosciences Wuhan , China. xpyuan1@hotmail.com
Benjamin Campforts, CSDMS Boulder Colorado, United States. Benjamin.campforts@colorado.edu
Boris Gailleton, GFZ Potsdam Potsdam , Germany. boris.gailleton@gfz-potsdam.de
Guillaume Baby, University of Rennes Rennes , France.
François Guillocheau, University of Rennes Rennes , France.
Cécile Robin, University of Rennes Rennes , France.

Passive margin stratigraphy contains time-integrated records of landscapes that have long since vanished. Quantitatively reading the stratigraphic record using coupled landscape evolution and stratigraphic forward models (SFMs) is a promising approach to extracting information about landscape history. However, the most commonly used SFM, which is based on an approximation of local, linear slope-dependent sediment transport, fails to produce diagnostic features of passive margin stratigraphy such as long-distance transport from the continental shelf and slope onto the abyssal plain. There is no consensus about the optimal form of simple SFMs because there has been a lack of direct tests against observed stratigraphy in well constrained test cases. Here we develop a nonlocal, nonlinear one-dimensional SFM that incorporates slope bypass and long-distance sediment transport, both of which have been previously identified as important model components but not thoroughly tested. Our model collapses to the local, linear model under certain parameterizations such that best-fit parameter values can be indicative of optimal model structure. Using seven detailed seismic sections from the South African Margin, we invert the stratigraphic data for best-fit model parameter values and demonstrate that best-fit parameterizations are not compatible with the local, linear diffusion model. Fitting the observed stratigraphy requires parameter values consistent with important contributions from slope bypass and long-distance transport processes. The nonlocal, nonlinear model yields improved fits to the data regardless of whether the model is compared against only the modern bathymetric surface or the full set of seismic reflectors identified in the data. Results suggest that processes of sediment bypass and long-distance transport are required to model realistic passive margin stratigraphy, and are therefore important to consider when inverting the stratigraphic record to infer past perturbations to source regions.