Compositional Balance Analysis: An Elegant Method of Geochemical Pattern Recognition and Anomaly Mapping for Mineral Exploration

Abstract

Geochemical pattern recognition and anomaly mapping are always involved in the fields of environmental and exploration geochemistry. Principal component analysis (PCA) and factor analysis (FA) are most commonly used to reveal underlying geochemical associations for the purpose of spatial distribution pattern analysis. However, the methods of PCA and FA cannot eliminate correlations between different principal components/factors, meaning that geochemical associations revealed by PCA or FA could be simultaneously influenced by two or more principal components/factors, as can be observed from biplot analysis. Such problem provides a challenge for interpretation of geochemical/geological processes. In the present study, we demonstrated a simple method, termed compositional balance analysis (CoBA), to interpret critical geochemical/geological processes. Comparative studies between CoBA and compositional factor analysis, as well as data- and knowledge-driven CoBA, were considered to discuss the advantage and practicability of the CoBA in geochemical pattern recognition and anomaly mapping based on a case study in the Nanling belt, South China. The results indicate that the CoBA has greater efficiency in enhancing weak or concealed geochemical anomalies and suppressing spurious geochemical anomalies relative to multivariate dimensionality reduction analysis; especially, knowledge-driven CoBA provides more robust interpretation of geochemical/geological processes relative to data-driven CoBA.