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Discrimination of Mineralized Rock Types in a Copper-Rich Volcanogenic Massive Sulfide Deposit Through Fast Independent Component and Factor Analysis

  • Saeid Hajsadeghi
  • Omid AsghariEmail author
  • Mirsaleh Mirmohammadi
  • Seyed Ahmad Meshkani
Original Paper
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Abstract

Multivariate methods are useful for simplifying the interpretation of variables in geochemical data and are widely used to uncover relationships between elements that are associated with geological and mineralization processes. Among these approaches, factor analysis (FA) is one of the most popular, whereas independent component analysis (ICA) has only been employed in a few cases. This study compared the effectiveness of these methods in distinguishing rock types and detecting mineralization signatures based on data on core samples obtained from the Nohkouhi copper deposit. The FA and ICA discriminated four known rock categories, namely barren rhyodacite, mineralized rhyodacite, barren black shale, and mineralized black shale. Stepwise linear discriminant analysis was used to compare the results of the FA and ICA and select components that effectively enable the discrimination of rock types and mineralization. First, rock types were distinguished with reference to the scores calculated via FA and ICA. The results showed that the FA and ICA achieved overall accuracy levels of 94% and 96% in rock-type discrimination, respectively. Second, each rock type was classified as either mineralized or barren. The FA exhibited classification accuracies of 76% for black shales and 70% for rhyodacites, whereas the ICA yielded classification accuracies of 79% and 88% for the two rock types, respectively.

Keywords

Fast independent component analysis Factor analysis Discriminant analysis Nohkouhi copper deposit 

Notes

Acknowledgments

The authors would like to thank the Zarmesh Group and Amin Karmania Company. We also acknowledge Prof. Carranza, editor-in-chief of Natural Resources Research, for the expert handling of our manuscript. Finally, our gratitude goes to the two anonymous reviewers for their constructive comments.

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Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.School of Mining Engineering, College of EngineeringUniversity of TehranTehranIslamic Republic of Iran
  2. 2.Simulation and Data Processing Laboratory, School of Mining Engineering, College of EngineeringUniversity of TehranTehranIslamic Republic of Iran

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