Geology topics

C.J. Jenkins

Publications and source records attributed to C.J. Jenkins.

3 recordsLinked to original sources

USGS2020

Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico

Turbidity currents deliver sediment rapidly from the continental shelf to the slope and beyond; and can be triggered by processes such as shelf resuspension during oceanic storms; mass failure of slope deposits due to sediment- and wave-pressure loadings; and localized events that grow into sustained currents via self-amplifying ignition. Because these operate over multiple spatial and temporal scales, ranging from the eddy-scale to continental-scale; coupled numerical models that represent the full transport pathway have proved elusive though individual models have been developed to describe each of these processes. Toward a more holistic tool, a numerical workflow was developed to address pathways for sediment routing from terrestrial and coastal sources, across the continental shelf and ultimately down continental slope canyons of the northern Gulf of Mexico, where offshore infrastructure is susceptible to damage by turbidity currents. Workflow components included: 1) a calibrated simulator for fluvial discharge (Water Balance Model - Sediment; WBMsed ); 2) domain grids for seabed sediment textures ( dbSEABED ); bathymetry, and channelization; 3) a simulator for ocean dynamics and resuspension (the Regional Ocean Modeling System; ROMS ); 4) A simulator ( HurriSlip ) of seafloor failure and flow ignition; and 5) A Reynolds-averaged Navier–Stokes ( RANS ) turbidity current model ( TURBINS ). Model simulations explored physical oceanic conditions that might generate turbidity currents, and allowed the workflow to be tested for a year that included two hurricanes. Results showed that extreme storms were especially effective at delivering sediment from coastal source areas to the deep sea, at timescales that ranged from individual wave events (~hours), to the settling lag of fine sediment (~days).

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2012

Geologic characterization of shelf areas using usSEABED for GIS mapping, modeling processes and assessing marine sand and gravel resources

Geologic maps depicting offshore sedimentary features serve many scientific and applied purposes. Such maps have been lacking, but recent computer technology and software offer promise in the capture and display of diverse marine data. Continental margins contain landforms which provide a variety of important functions and contain important sedimentary records. Some shelf areas also contain deposits regarded as potential aggregate resources. Because proper management of coastal and offshore areas is increasingly important, knowledge of the framework geology and marine processes is critical. Especially valuable are comprehensive and integrated digital databases based on high-quality information from original sources. Products of interest are GIS maps containing thematic information, such as sediment character and texture. These products are useful to scientists modeling nearshore and shelf processes as well as planners and managers. The U.S. Geological Survey is leading a national program to gather a variety of extant marine geologic data into the usSEABED database system. This provides centralized, integrated marine geologic data collected over the past 50 years. To date, over 340,000 sediment data points from the U.S. reside in usSEABED, which combines an array of physical data and analytical and descriptive information about the sea floor and are available to the marine community through three USGS data reports for the Atlantic, Gulf of Mexico, and Pacific published in 2006, and the project web sites: (http://woodshole.er.usg s.gov/project-pages/aggregates/ and http://walrus.wr.usgs.gov/usseabed/)

Conference Paper↗

USGS2008

Seabed mapping and characterization of sediment variability using the usSEABED data base

We present a methodology for statistical analysis of randomly located marine sediment point data, and apply it to the US continental shelf portions of usSEABED mean grain size records. The usSEABED database, like many modern, large environmental datasets, is heterogeneous and interdisciplinary. We statistically test the database as a source of mean grain size data, and from it provide a first examination of regional seafloor sediment variability across the entire US continental shelf. Data derived from laboratory analyses ("extracted") and from word-based descriptions ("parsed") are treated separately, and they are compared statistically and deterministically. Data records are selected for spatial analysis by their location within sample regions: polygonal areas defined in ArcGIS chosen by geography, water depth, and data sufficiency. We derive isotropic, binned semivariograms from the data, and invert these for estimates of noise variance, field variance, and decorrelation distance. The highly erratic nature of the semivariograms is a result both of the random locations of the data and of the high level of data uncertainty (noise). This decorrelates the data covariance matrix for the inversion, and largely prevents robust estimation of the fractal dimension. Our comparison of the extracted and parsed mean grain size data demonstrates important differences between the two. In particular, extracted measurements generally produce finer mean grain sizes, lower noise variance, and lower field variance than parsed values. Such relationships can be used to derive a regionally dependent conversion factor between the two. Our analysis of sample regions on the US continental shelf revealed considerable geographic variability in the estimated statistical parameters of field variance and decorrelation distance. Some regional relationships are evident, and overall there is a tendency for field variance to be higher where the average mean grain size is finer grained. Surprisingly, parsed and extracted noise magnitudes correlate with each other, which may indicate that some portion of the data variability that we identify as "noise" is caused by real grain size variability at very short scales. Our analyses demonstrate that by applying a bias-correction proxy, usSEABED data can be used to generate reliable interpolated maps of regional mean grain size and sediment character.

Continental Shelf Research↗