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
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.
Geochemistry, Clustering, Principal Component Analysis, Sulfide Mineral Chemistry