REFINING PREDICTIVE MODELS FOR PASSIVE MARGIN STRATIGRAPHY BY INVERTING THE SEDIMENTARY RECORD

Numerical models that explicitly link source to sink (S2S) are critical for inferring past tectonic and climatic perturbations to Earth’s surface from the sedimentary record. While models seeking predictive power must simulate surface processes at both source and sink, substantially more model formulations exist for landscape evolution than for seascape evolution and the building of stratigraphy in marine basins. Most marine sedimentation models operating at basin spatial scales and geologic temporal scales use local linear diffusion approximations for sediment transport. Such approximations produce realistic stratigraphy in shallow marine environments, but do not replicate observed deep marine deposition patterns. Full closure of S2S sediment budgets requires models with predictive power over the development of both shallow and deep marine stratigraphy.

We present a new marine sedimentation model that incorporates two simple modifications allowing non-local sediment transport: 1) the possibility of sediment bypass on steep slopes, and 2) long-distance transport, even over near-zero slopes. We constrain, using Bayesian inference techniques, the four model parameters by comparing modeled against observed stratigraphy from 130 Ma of passive margin evolution in the Orange Basin, southern Africa. Best-fit modeled stratigraphy captures the form of the observed record. Best-fit parameter values imply important roles for both model elements we introduced: sediment bypass on steep slopes and long-distance transport over very gentle slopes. We attribute remaining model-data misfit to additional transport processes—hemipelagic sedimentation, grain size variations, or ocean bottom currents—that are not captured by our simple model but might be required to explain sediment runout distances longer than those produced by our best-fit model. Results suggest that predictive modeling of marine sedimentation requires departing from local diffusion approximations, even over ocean-basin-filling timescales. Simple treatment of nonlocal transport dynamics can improve S2S model predictive power, and may enable better recovery of environmental signals from the long-term stratigraphic record.