Integrating CoDA and PCA for enhanced characterization of fluvial depositional processes: a case study of the Shendi formation, Sudan

Abstract

Traditional approaches to fluvial sedimentary analysis often face challenges in deciphering complex, multivariate datasets. This study combines compositional data analysis (CoDA) with principal component analysis (PCA) to enhance the characterization of depositional processes and sub-environments within the Shendi Formation. The PCA applied to centered log ratio (clr)-transformed grain size distributions, reveals three principal components with ~ 91.86% of the data variance explained, representing distinct processes: bedload-dominated channel-bar dynamics, overbank deposition, and high-energy flood events. Specific lithofacies associations strongly correlate to each principal component. This integrated approach enables the identification of subtle yet significant patterns within the complex sedimentological record. The Shendi Formation exhibits characteristics of a dynamic fluvial setting with variations in flow energy, channel migration, and periodic flooding. Our findings demonstrate the power of CoDA-PCA in refining the understanding of fluvial depositional systems and highlight its potential for broader applications.