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:
,
,
, 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.

Files

Institutions

Nanjing University

Categories

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

Licence