EPJ Web Conf.
Volume 140, 2017Powders and Grains 2017 – 8th International Conference on Micromechanics on Granular Media
|Number of page(s)||4|
|Section||Environmental granular processes|
|Published online||30 June 2017|
Quantifying grain shape with MorpheoLV: A case study using Holocene glacial marine sediments
1 ICube–Laboratoire des sciences de l’ingénieur, de l’informatique et de l’imagerie, University of Strasbourg and CNRS, 300 Bd Sébastien Brant, CS 10413, F-67412 Illkirch Cedex, France
2 Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77204, USA
Published online: 30 June 2017
As demonstrated in earlier works, quantitative grain shape analysis has revealed to be a strong proxy for determining sediment transport history and depositional environments. MorpheoLV, devoted to the calculation of roughness coefficients from pictures of unique clastic sediment grains using Fourier analysis, drives computations for a collection of samples of grain images. This process may be applied to sedimentary deposits assuming core/interval/image archives for the storage of samples collected along depth. This study uses a 25.8 m jumbo piston core, NBP1203 JPC36, taken from a ~100 m thick sedimentary drift deposit from Perseverance Drift on the northern Antarctic Peninsula continental shelf. Changes in ocean and ice conditions throughout the Holocene recorded in this sedimentary archive can be assessed by studying grain shape, grain texture, and other proxies. Ninety six intervals were sampled and a total of 2319 individual particle images were used. Microtextures of individual grains observed by SEM show a very high abundance of authigenically precipitated silica that obscures the original grain shape. Grain roughness, computed along depth with MorpheoLV, only shows small variation confirming the qualitative observation deduced from the SEM. Despite this, trends can be seen confirming the reliability of MorpheoLV as a tool for quantitative grain shape analysis.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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