Evaluation of elemental mineralization rank using fractal and multivariate techniques and improving the performance by log-ratio transformation

Abstract

Background separation from anomalous populations in the polymetallic mineralization is a problematic issue in geochemical exploration. This study focuses on the applications of fractal modeling, correspondence analysis (CA), classical principal component analysis (PCA), and PCA method based on centred log-ratio (clr) transformation for the multi-elemental evaluation of geochemical data in Glojeh polymetallic mineralization, NW Iran. A Concentration-Number (C-N) multifractal modeling was applied to delineate individual self-similar patterns for elemental distributions simultaneously in terms of Au, Ag, Cu, Pb, and Zn. The differences between fractal dimensions and clockwise angle between fitted lines in the C-N Log-log plots could lead to evaluate mineralized rank corresponding to Au≥Ag≥Pb≫Cu≈Zn. A more comprehensive evaluation via CA and PCA of clr-transformed data could facilitate the vein structure identification in high-dimensional data. The PCA results of clr-transformed data revealed the potential intensification trend of (Pb, Ag, As, Te, Au)>(Mo, Zn, Be, Cu)>W>(S, Cd), that is more consistent with fractal and CA approaches. The closure effect problem was overcome by the clr transformation, and the total variance explained by the first factor increased from 28.2% in classical PCA to 43.7% in clr transformation. Accordingly, As, Sb and Cd were considered as potential pathfinder elements for Au in Glojeh deposit. The ability to handle zeros in the data matrix and determining an elemental eccentricity as a criterion are the advantages of CA method, while loading factors spread in a full circle and providing subcompositional coherence are the competitive advantages of PCA (calculated according to log-ratio transformation). However, the CA and PCA based on Log-Ratio transformation techniques showed significant potential to draw an inference in such deposits.