Sediments recovered during Leg 154 exhibit an unusually wide variation of interbedded lithologies down to the deepest layers recovered (900-m burial depth). We report here on relations of properties as a function of lithology and burial depth. Lithologies have been characterized by a large number of carbonate determinations on samples used for discrete physical properties measurements. Carbonate determinations have been supplemented by quartz determinations using infrared spectroscopy, and by shipboard determinations of biogenic opal content. Physical properties show coherent patterns with carbonate content over discrete zones that average several hundred meters of thickness. The relation between seismic velocities and carbonate content is strong enough to detect a velocity calibration problem for an interval of Site 925 shipboard measurements.
The shallowest Ceara Rise sediments show a weak positive dependency of bulk density and a more pronounced positive relationship of seismic velocities on carbonate content. The difference between bulk-density and carbonate relations on Ceara Rise and well-known relations established in siliceous settings can be explained by the high quartz contents (15%-16% on a carbonate-free basis) of the Pleistocene section.
For each type of physical property, inversions are encountered, where the sense of the correlation between property and lithology changes. For example, both bulk densities and seismic velocities become negatively correlated to carbonate content at depths of 100-250 meters below seafloor on the Ceara Rise, but then become positively correlated on further burial. These inversions are interpreted to result from the competition between mechanical and chemical compaction in pelagic sediments. Bulk density, in particular, shows a complex evolution with depth that often results in a parabolic relation of density to carbonate content in more deeply buried Ceara Rise sediments. Nevertheless, the relations between physical properties and carbonate content on Ceara Rise are sufficiently systematic that predictive models can be generated to relate lithology to signals sensed by borehole logging tools and by seismic imaging.
1Shackleton, N.J., Curry, W.B., Richter, C., and Bralower, T.J. (Eds.), 1997. Proc. ODP, Sci. Results, 154: College Station, TX (Ocean Drilling Program).
2Department of Geological Sciences, Brown University, Providence, RI 02912, U.S.A. Timothy_Herbert@brown.edu
3Geoscience Research Division, Scripps Institution of Oceanography, La Jolla, CA 92093-0215, U.S.A.
4Centre des Faibles Radioactivites, 91198 Gif-sur-Yvette, France.
5GEOMAR, Wischhofstraße 1-3, D-24148 Kiel, Federal Republic of Germany.
6Bedford Institute of Oceanography, New Bedford, Canada.
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