Machine-learning-assisted interrogation of sulfide mineral LA-ICP-MS data: Klaza Epithermal Deposit, Yukon, Canada
Published: 26 October 2021| Version 1 | DOI: 10.17632/pd5fxp9dk8.1
Contributor:
WELL-SHEN LEEDescription
In order to investigate the geochemical evolution of the Klaza hydrothermal system and metal distribution among sulfide minerals, pyrite, sphalerite, and arsenopyrite were mapped using LA-ICP-MS. The raster data from each map were processed using unsupervised machine learning (K-means clustering and PCA) to classify and filter pixels unrelated to the mineral of interest and to subsequently attribute primary and late features in the mineral grain.
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Institutions
Laurentian University
Categories
Geochemistry, Clustering, Principal Component Analysis, Sulfide Mineral Chemistry