Integrating a Turbidity Current Process Model in Source to Sink Analyses: The EuroSEDS Sediment Budget Estimator App

Abstract

A strength of the source-to-sink approach has been that it made the ultimate simplification of the process of sediment transport, while still yielding robust and informative answers. Sediment is simply distributed from the source to the sink, and the various depositional sub-systems that are passed along the pathway act to extract a certain fraction of the available sediment budget. This may be counterintuitive when observed parallel to the development of process-based modelling efforts that seek increasingly more detailed and complex treatments of sediment transport. This attention to increasing complexity may not be the path towards delivering a process-model of turbidity currents that can contribute to constraining sediment budget estimates in sedimentary system analyses. Our aim is to package state-of-the-art knowledge on the dynamics of turbulent suspended sediment transport within a tool that relies solely on input of geological constraints such as basin configuration and characteristic feeder channel architectural dimensions. The result is a process-based turbidity current model that predicts sediment budget transferred from the slope to basin floor fans over geological timescales based on simple and easily obtainable parameters from geological datasets such as seismic or well-logs. The model performs turbidity current flow reconstructions based on a dozen quantitative relations that together form a system describing suspended sediment transport in channelized turbidity currents. Model outputs are twofold. Firstly, the model provides estimations of the velocity and concentration profiles of turbidity currents, and secondly, it supplies histograms of the system's total sediment budget. To derive these parameters all that is required is an estimation of flow recurrence, duration, and how long the system was geologically active. These inputs can be based on the user's understanding of their particular system, or on default values for system styles suggested in literature. The simplicity of the model allows computation of 10's of thousands of turbidity currents in seconds. This makes the app suited to consider multitudes of scenarios, resulting in simple statistics such as min, max, best guess, median, 10th and 90th percentiles, and makes the app suitable to integrate process-based predictive capabilities in stochastic modelling.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017