Compositional data analysis of regional geochemical data in the Lhasa area of Tibet, China

Abstract It is now widely recognized that geochemical survey data are compositional in nature, with each component having relative importance as part of a whole. Compositional data analysis (CoDA) based on log-ratio transformations can be used to deal with the ‘sum to one’ data constraint. This study explored the use of two data-driven CoDA approaches, centered log-ratio (clr)-biplot analysis and a compositional-balance approach, to investigate associations between elements for potential mineral exploration in the Lhasa area of Tibet, China. Results revealed that (1) the compositional-balance approach, based on a hierarchical cluster and sequential binary partition (SBP) technique, well reflects the range of rocks and metal deposits in the area; (2) the clr-biplot indicates associated relationships between elements, and a consistency is found between principal component (PC1 and PC2) and key compositional balances (Balance 1 and 4); and (3) comparison with traditional integrated geochemical mapping, a knowledge-driven method, proves the validity of the compositional-balance and clr-biplot methods. These results provided metallogenic and petrogenetic information and crucial evidence supporting further geological and geochemical exploration. The improved knowledge demonstrates the importance of using a CoDA approach for geochemical data before performing further statistical analysis.