Open Access Repository

Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada

Downloads

Downloads per month over past year

Ahmed, AD, Hood, SB ORCID: 0000-0002-5680-7597, Cooke, DR ORCID: 0000-0003-3096-5658 and Belousov, I 2020 , 'Unsupervised clustering of LA-ICP-MS raster map data for geological interpretation: A case study using epidote from the Yerington district, Nevada' , Applied Computing and Geosciences, vol. 8 , pp. 1-16 , doi: 10.1016/j.acags.2020.100036.

[img]
Preview
PDF (Published version)
141185 - Unsupe...pdf | Download (8MB)

| Preview

Abstract

Raster element concentration maps created using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) can be used to interpret microscale compositional and textural domains within mineral grains. Raster maps are typically evaluated element by element; however, application of statistical techniques (such as cluster analysis) can enhance the generation of geochemical domains to support interpretation of growth zones, core-rim relationships, sector zones, and compositional-textural associations. Clustered LA-ICP-MS map data can be assessed within individual samples and between multiple samples, and can extend insight from the microscopic scale to the regional scale to better understand geological paragenesis of an area.Our workflow (1) applies a centred log transformation to selected elements in a raster map dataset; (2) uses principal component analysis (PCA) applied to the multi-sample, mono-mineralic dataset to group similar elements in epidote based on geochemical character; (3) applies unsupervised clustering to separate different types and generations of epidote in chemical feature space; (4) presents clustered LA-ICP-MS raster map results for interpretation of inter- and intra-mineral chemical zones; and (5) plots results spatially, across a regional map area, to investigate geological paragenesis.The workflow is illustrated using samples of epidote from the Yerington porphyry-skarn Cu (Mo–Au) district. In the case study area, six clusters are defined by unique mineral compositions: (1) low U; (2) elevated Pb, Mn and low Fe and Sr; (3) elevated Ce, U and low Mn and Pb; (4) elevated U, Ce; low Mn, Pb; (5) elevated Sr, Fe and low Mn, Pb; and (6) elevated Mn, Sr, and Fe and low Ce and U. The regional distribution of these groups is presented as indicating proximity to the porphyry environment (lower concentrations of Ce, U, As and Sb and higher concentrations of Mn, Sr and Fe) versus retrograde skarn (elevated Ce, U, As and Sb).

Item Type: Article
Authors/Creators:Ahmed, AD and Hood, SB and Cooke, DR and Belousov, I
Keywords: geochemistry, PCA, unsupervised learning, LA-ICP-MS, k-means clustering, epidote, Ann Mason, Nevada
Journal or Publication Title: Applied Computing and Geosciences
Publisher: Elsevier
ISSN: 2590-1974
DOI / ID Number: 10.1016/j.acags.2020.100036
Copyright Information:

© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP