Compositional Analysis in the Study of Mineralization Based on Stream Sediment Data

Abstract

Stream sediment data, widely used in geochemical exploration and environmental studies, are typical compositional data that should be opened prior to data analysis. In this study, the closure problem with geochemical exploration data is addressed from three aspects of univariate, bivariate and multivariate data analysis in a case study from southwestern Fujian depression belt in China. The results show that: (1) the robust estimators, such as median and median absolute deviation are useful to measure the center and spread of data for univariate analysis; (2) the isometric logratio (ilr) information should be applied to measure the variability and stability between the two variables in bivariate analysis; and (3) the robust principal component analysis should be applied for the ilr transformed data to reduce the dimensionality of multiple variables and to obtain a mineralization-linked principal component in multivariate analysis.