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[Türkçe .pdf]
Multivariate analysis of log-ratio transformed data and its priority in mining science, porphyry and polymetallic vein deposits case studies
Başlık: Multivariate analysis of log-ratio transformed data and its priority in mining science, porphyry and polymetallic vein deposits case studies
Yazarlar: Farshad DARABI-GOLESTAN, Ardeshir HEZARKHANI
Anahtar Kelimeler: Isometric log-ratio transformation, Subcompositional coherence, Correspondence analysis, Principal component analysis, Geochemical compositional data Polymetallic and porphyry deposits,
Özet:

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 indicates a porphyry deposit occurred in Dalli deposit. Theability 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 improving the performance of multivariate techniques using LRT.

Details

[English .pdf]
Multivariate analysis of log-ratio transformed data and its priority in mining science, porphyry and polymetallic vein deposits case studies
Title: Multivariate analysis of log-ratio transformed data and its priority in mining science, porphyry and polymetallic vein deposits case studies
Authors: Farshad DARABI-GOLESTAN, Ardeshir HEZARKHANI
Keywords: Isometric log-ratio transformation, Subcompositional coherence, Correspondence analysis, Principal component analysis, Geochemical compositional data Polymetallic and porphyry deposits,
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 indicates a porphyry deposit occurred in Dalli deposit. Theability 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 improving the performance of multivariate techniques using LRT.



 
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