Application of multivariate analysis to multi-element chemical composition data of geological samples collected from the hydrothermal fields in the Okinawa Trough

Abstract

Dimensional compression is an effective multivariate analysis technique that can find hidden structures when trying to simultaneously understand a large number of items that are difficult to intuitively understand. The chemical composition of geological samples collected in hydrothermal fields is considered to be an integral result of various geochemical processes. If we can extract and discuss these elements, we can expect to gain a better understanding of the various chemical processes in hydrothermal fields. The purpose of this study is to investigate the applicability of statistical methods to multi-element chemical composition data sets of geological samples collected over a wide area, with a view to understanding the behavior of trace amounts of useful metal elements. We performed principal component analysis and independent component analysis on the chemical composition data of geological samples collected from several hydrothermal activity areas in the Okinawa Trough and compared the results. As a result of the analysis, several elements were extracted that are considered to dominate the chemical composition, and there are components that broadly distinguish the mineral species in both methods. In the future, we would like to examine whether the parameters that characterize the behavior of useful metal elements can be found using these parameters.