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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Sedimentary structure discrimination with hyperspectral imaging in sediment cores

Authors: Jacq, K��vin; Rapuc, William; Benoit, Alexandre; Coquin, Didier; Fanget, Bernard; Perrette, Yves; Sabatier, Pierre; +4 Authors

Sedimentary structure discrimination with hyperspectral imaging in sediment cores

Abstract

The LDB17_P11Ax (IGSN: TOAE0000000243); Datation Age 1040 +/- 30 to 2017 CE by core correlation, 14C, lamina counting) core from the Bourget Lake (France) was analyzed in 2018 by hyperspectral imaging. We studied the potential of hyperspectral sensor to image a sediment cores and created machine learning models. The hyperspectral images were acquired in order to develop quantitative (estimating particle size and loss on ignition) and qualitative (detection of instantaneous events or lamina) methods. All these methods allow to reconstruct the past environment and climate at high resolution (pixel size: 50-250 microns) and without destroying the sample for archiving for future analysis. These images have been valorized in publications for the detection of instantaneous events with hyperspectral and combined with XRF data, for the combination of the two images into a composite image. image (.hdr, .dat, .jpg)

Keywords

Hyperspectral imaging, Machine learning, Automatic detection, Discrimination methods, Visible and near-infrared spectroscopy, Sedimentary deposits

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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