HR: 0800h
AN: H51D-0393    [Abstracts]
TI: Inferring Steady Turbidity Current Flow Conditions from Channel Morphology
AU: * Fildani, A E
EM: AndreaFildani@chevron.com
AF: Chevron Energy Technology Company, Quantitative Stratigraphic Team, 6001 Bollinger Canyon Road, Rm. D1192, San Ramon, CA 94583 United States
AU: Hilley, G E
EM: hilley@pangea.stanford.edu
AF: Stanford University, Department of Geological and Environmental Sciences, 450 Serra Mall, Braun Hall, Building 320, Stanford, CA 94305-2115 United States
AU: McHargue, T
H51D-0393 AF: Chevron Energy Technology Company, Quantitative Stratigraphic Team, 6001 Bollinger Canyon Road, Rm. D1192, San Ramon, CA 94583 United States
AB: Flow conditions and composition of turbidity currents in submarine canyon systems are influenced by, and interact with the topography of the ocean bottom to produce channel bedforms and levee systems that confine the turbidity currents. Thus, characterizing these flow conditions constitutes an important component of understanding flow confinement in submarine channel systems. In this research, we develop a method for simultaneously extracting turbidity current flow velocities, flow sediment concentrations, and mass loss/gain from the flow due to interactions with the surrounding clear water (i.e., flow stripping and clear-water entrainment, respectively). Specifically, we combine expressions for conservation of momentum for steady flows, and fluid and sediment mass conservation laws with measurements of channel geometries, flow super-elevations, and expressions for their spatial derivatives along the length of a submarine channel to infer the quantities of interest at each point along the channel. Thus, rather than using the conservation laws to create forward models flow evolution along the length of the channel, our approach treats the conservation laws and channel measurements as constraints on flow properties. This is achieved by using the channel geometry measurements, conservation laws, and initial estimates of flow conditions to predict flow super-elevation along the channel. The misfit between observed and predicted flow super-elevations is then minimized by refining the flow conditions using non-linear optimization algorithms. In the future, this algorithm may be easily adapted to include a variety of different datasets that may be used to refine estimates of flow conditions in these submarine channel systems. We tested the resolution of this approach using a series of synthetic datasets based on channel geometries within the Amazon submarine canyon and fan system reported in the literature. The observed relationships between channel width, height, and elevation, as well as downstream changes in these quantities were used to construct datasets on which various amounts of statistical noise were superimposed. Finally, we used our algorithms to estimate the prescribed flow conditions of these datasets after the superposition of the statistical noise. We found that our algorithm tolerates moderate amounts of statistical noise while still predicting the long-wavelength characteristics of the flow conditions. Rapid changes in velocity due to factors such as local flow constrictions are not resolved by our approach. Nonetheless, our methods may provide a means of reconstructing the gross flow properties of turbidity currents where flow super-elevation and channel geometry varies along the channel length.
DE: 1825 Geomorphology: fluvial (1625)
DE: 1856 River channels (0483, 0744)
DE: 4512 Currents
DE: 4568 Turbulence, diffusion, and mixing processes (4490)
SC: Hydrology [H]
MN: Fall Meeting 2005