Multivariate analysis of log-ratio transformed data and its priority in mining science: Porphyry and polymetallic vein deposits case studies

Year 2019, Volume: 159 Issue: 159, 185 - 200, 15.08.2019

Farshad Darabı-golestan Ardeshir Hezarkhanı

https://doi.org/10.19111/bulletinofmre.456958

Cited By: 1

Abstract

Each mineralization style is
characterized by typical signature associations between elements due to
elemental interactions, therefore the coherence and closure effects problem
must be overcome in geochemical processing. The coherence indicates the ratios
between two components (rows or columns) remains the same whether they are
considered in a subcomposition or in the full composition. The log-ratio
transformation (LRT) has recognized as a standard procedure to support
subcompositional coherence. The log-transformed data is applicable for
geochemical data to unveil such associations, prior to applying the
multivariate analysis like correspondence analysis (CA) and principal component
analysis (PCA). At the present study, subcompositional coherence is overcome by
inverse iso-metric log-ratio transformation for geochemical compositional data
at two polymetallic and porphyry deposits. Based on Ilr-transformed data, Ag,
Au, As, Pb, Te, Mo and rather S, W, Cu are enriched as polymetallic elements at
Glojeh, while Au-Cu-(Mo) compositions indicate a porphyry deposit occurred in
Dalli deposit. The ability to handle zero values in the data matrix and
determining an elemental eccentricity from the center of each axis based on
Euclidean distances are the advantages of CA method, with compression to LRT.
Whereas, loading factors which spread in every direction and providing
subcompositional coherence are the competitive advantages of PCA based on LRT,
for both case studies. Results with these techniques show significant ability
to draw an inference in such geochemical data, and in improving the performance
of multivariate techniques using LRT.

 

Keywords

Isometric log-ratio transformation, Subcompositional coherence, Correspondence analysis, Principal component analysis, Geochemical compositional data, Polymetallic and porphyry deposits

Thanks

The authors are grateful to the Iranian Mines and Mining Industries Development and Renovation Organization (IMIDRO) for their permission to have access to Glojeh deposit dataset. The authors are also thankful to Mr. Fattahi and Mr. Hemmati for their encouragement and valuable help hugely.

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