Dataset for: Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data

Published: 3 July 2019| Version 1 | DOI: 10.17632/t6t82b2h7h.1
Contributors:
huizhen hao,
Ruohua Guo,
Qing Gu,
Xiumian Hu

Description

Among them, three tables include the EDS data of analyzed elements including 90-second elemental data of 2255 grains, 40-second elemental data of 492 grains and 6- second elemental data of 320 grains, respectively. The tables 4-7 include the confusion matrixes based on datasets of different analyzing times and different decision attributes.

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Institutions

Nanjing University

Categories

Geology, Artificial Intelligence, Geochemistry, Mineralogy, Sedimentology, Machine Learning, Heavy Mineral

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