Geochemical trends in sedimentary environments using PCA approach

Abstract

Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identification of underlying patterns in geochemical fluctuations. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset. In this study, we focus on the utilization of Principal Component Analysis (PCA), a multivariate statistical technique, to uncover the underlying sedimentary processes influencing distinct geochemical dataset. Specifically, our attention is directed towards the examination of geochemical data from the previously published geochemical data of metasediments from Shimla and Chail group (referred to as SCM) and the mudflat sediments of Diu Island (referred to as DMS). Our PCA outcomes reveal that the initial three principal components (PC1, PC2, and PC3) account for 52.51% and 79.30% of the total variance within the SCM and DMS geochemical data, respectively. Notably, the negative loading of SiO₂, alongside positive loadings of incompatible elements and those associated with mafic rocks on PC1 within the SCM dataset, indicates sediment origins ranging from felsic to intermediate sources. Additionally, the coexistence of Th, U, Zr, and Sc, exhibiting positive loadings in PC1 and PC2, suggests a significant influence of reworking and recycling from felsic to intermediate sources. In the context of the DMS dataset, PCA analysis highlights the dominant influence of in-situ productivity and mafic sediment sources along the positive axis of PC1. Conversely, the negative axis of PC1 is shaped by intermediate and potentially other sources. Further granularity in interpretation reveals the positive axis of PC2 being attributed to weathering proxies, while the dominance of plagioclase minerals in the clayey fraction controls the positive axis of PC3. Through this investigation, our study underscores the essential role of PCA-assisted geochemical data analysis in unraveling the intricate web of processes contributing to the variance observed within sedimentary systems. By effectively distilling the multifaceted factors driving geochemical variability, this approach emerges as a pivotal asset in enhancing our understanding of sedimentary dynamics.