GEOSCAN Search Results: Fastlink


TitlePredictability of seabed texture: spatial scaling of grain size and bathymetry on glaciated and non-glaciated shelves
AuthorKostylev, V E
SourceAmerican Geophysical Union Fall Meeting, abstracts; 2010 p. 1
LinksOnline - En ligne
Alt SeriesEarth Sciences Sector, Contribution Series 20100248
Meeting2010 AGU Fall Meeting; San Francisco; US; December 13-17, 2010
Mediaon-line; digital
File formathtml
ProvinceEastern offshore region
AreaGulf of Mexico; Caribbean Coast
Subjectsmarine geology; geophysics; seafloor topography; seabottom topography; bathymetry; oceanographic surveys; oceanography; grain size analyses; modelling
ProgramOffshore Geoscience
Released2010 01 01
AbstractIn mapping seabed texture inherent predictability of sediment grain size is the key to the success of spatial interpolations. With low and stationary spatial variance the variable should be easy to predict while otherwise the results of interpolation are untrustworthy. Conceptual model known as 1/f noise offers a compelling way to describe predictability of environmental patterns. I have studied spatial spectra of mean grain size of surficial seabed sediments from two datasets roughly corresponding to glaciated (Canadian) and non-glaciated (US) shelves. Data for the Atlantic Canadian waters were obtained from Natural Resources Canada Expedition Database (23666 samples). For United States waters the Gulf of Mexico and Caribbean coastal and offshore data (17765 samples) as well as Atlantic Coast offshore surficial sediment data (33907 samples) were obtained from the usSEABED database. Spatial variances of mean grain size were calculated for samples separated by distance bins ranging from 1 to 1000 km. On scales from kilometers to hundreds of kilometers power spectrum of mean grain size in the studied datasets may be characterized as white noise (B = 0), indistinguishable from uniform random distribution (B =0.097 for Canada and B =0.009 for US). Power spectrum of grain size co-varies with bathymetry in US dataset across all scales. In the Canadian dataset this relationship holds up to 100 km, after which bathymetric and grain size variability become dissociated, and bathymetric variability increasing while sediment variability being relatively constant. Variance of US grain size data exhibits rapid increase in spatial variance on scales of 100 km and higher (B = 1.501). This exponent value falls between pink (B = 1) and red (Brownian, B = 2) noise and indicates that spatial patterns at larger sample separations are more predictable. There are several possible explanations for high spatial variance in grain size at relatively small sample separation (1 - 100 km) such as positional errors, varying sampling efficiency of different sampling tools, sample processing biases and errors in databases, and lastly the sought after natural heterogeneity of glaciated shelves leading to complex spatial patterns. On broader spatial scale the association of bathymetric and grain size variance in US dataset suggests similarity of dynamic oceanographic processes responsible for sediment distribution patterns across broad bathymetric gradients. In the Canadian dataset which covers formerly glaciated shelves complexity of spatial pattern remains similar regardless of scale and is likely defined by the interaction of current and historical processes responsible for sediment distribution. It is concluded that empirical data-driven spatial interpolation of textural sediment data is more challenging in formerly glaciated shelves than on non-glaciated temperate and subtropical shelves.

Date modified: