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

It is now widely recognised that geochemical survey data are compositional in nature in that the components show their relative importance as parts of a whole. Compositional Data Analysis (CoDA) using log-ratio transformations can be used to reduce the ‘closure problem’ caused by the lack of scale invariance of the classical covariance of geochemical compositional data. This study explores the use of two data-driven CoDA approaches, clr-biplot analysis and a compositional balance approach, to investigate associations between elements for potential mineral exploration in the Lhasa area of Tibet, China. The use of the CoDA approach reveals meaningful results in that: (1) the compositional balance approach, using hierarchical cluster and a sequential binary partition (SBP) technique, excellently reflects the range of rocks and metal deposits in the area; (2) the clr-biplot indicates the relationships between elements and a consistency is found between PC1 and PC2 and key compositional balances (Balance 1 and 4); (3) comparison with traditional integrated geochemical mapping, which is a kind of knowledge-driven method, proves the validity of compositional balance and clr-biplot. These results provide metallogenic and petrogenetic information and crucial evidence for further geological and geochemical exploration in this area. The improved knowledge provided by this approach demonstrates the importance of using a CoDA approach for geochemical data before performing further statistical analysis.