Combining a robust PCA of logratio transformed data and geostatistical sequential Gaussian simulation approach for geochemical characterization of orogenic gold deposits: a case study from the Alut area, NW of Iran

摘要

Robust principal component analysis (PCA) has been proved to be an efficient multivariate statistical method for extraction of a multivariate structure of geochemical data containing outlier values. Furthermore, because of the compositional characteristics of geochemical data, logratio transformation approaches also have to be implemented to transform the data from the simplex space into real space. In order to model the extracted principal component of transformed data, we used sequential Gaussian simulation (SGS) to overcome the drawbacks associated with the traditional kriging method, e.g. bias conditionally relevant to underestimation and overestimation, the smoothing effect and so on.