Abstract
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.
Highlights
Quantitative grain shape analysis is a strong proxy for determining sediment transport history and depositional environments
We present MorpheoLV, a well-documented and publicly available code written in Matlab devoted to the computation of roughness coefficients from unique grain images
Note that images input to MorpheoLV are assumed to come with a jpeg or a tiff extension, while MorpheoLV’s output images are png or Matlab figures
Summary
Quantitative grain shape analysis is a strong proxy for determining sediment transport history and depositional environments. As demonstrated in earlier works, grain angularity can be quantified by determining the roughness coefficient (Rc) from Fourier harmonics range [2, 3, 5, 8, 11]. We present MorpheoLV, a well-documented and publicly available code written in Matlab devoted to the computation of roughness coefficients from unique grain images. The new key feature is the opportunity to analyse a collection of samples of grain images all at once to compute roughness variations along a distance or a duration, for instance. Benchmark images proposed in [1] are processed for both validation and interpretation purposes
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