Author Posting. © The Author(s), 2007. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Continental Shelf Research 28 (2008): 614-633, doi:10.1016/j.csr.2007.11.011.
We present a methodology for statistical analysis of randomly-located marine
sediment point data, and apply it to the U.S. 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 regionallydependent
conversion factor between the two. Our analysis of sample regions on the U.S.
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.
The authors thank the Office of Naval Research for support under grants
N00014-05-1-0079 (JAG) and N00014-05-1-0080 (CJJ), and the USGS/Coastal and Marine
Geology Program (SJW).
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