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Machine Learning on STEM-EDS Data for Quantifying Overlapping Deep-Mantle Rock Assemblages

Published online by Cambridge University Press:  30 July 2020

Hui Chen ,
Farhang Nabiei ,
James Badro ,
Duncan TL Alexander  and
Cécile Hébert
Hui Chen
Affiliation:
École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, Switzerland
Farhang Nabiei
Affiliation:
University of Cambridge, Cambridge, England, United Kingdom
James Badro
Affiliation:
Institute de Physique du Globe de Paris, Paris, Ile-de-France, France
Duncan TL Alexander
Affiliation:
École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, Switzerland
Cécile Hébert
Affiliation:
École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, Switzerland

Abstract

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Type
Call of the Wild: Advances in Microanalysis and Microscopy of Geological and Extraterrestrial Materials
Information
Microscopy and Microanalysis , Volume 26 , Supplement S2 , August 2020 , pp. 1878 - 1880
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Copyright
Copyright © Microscopy Society of America 2020

References

Irifune, T et al. . Science, 327 (2010), p. 193–195.10.1126/science.1181443CrossRefGoogle Scholar
Schmidt, J et al. ., npj Computational Materials 5 (2019), no. 83.Google Scholar
de la Pena, F et al. ., Microscopy and Microanalysis, 23 (2017), p. 214–215.10.1017/S1431927617001751CrossRefGoogle Scholar
https://imagej.net/Trainable_Weka_SegmentationGoogle Scholar
https://imagej.net/Graph_CutGoogle Scholar