Numerical methodologies and tools for efficient and flexible unmixing of single-sample grain-size distributions: Application to late Quaternary aeolian sediments from the desert-loess transition zone of the Tengger Desert

Parametric curve-fitting techniques are routinely adopted to derive the components of individual grain-size distributions of sediment samples. However, due to the lack of methodologies and calculation platforms to efficiently generate reproducible unmixing solutions, and appropriate statistical techniques to analyze and visualize unmixing results characterized by complex and variable structures, the technique has seldom been extended to the systematic analysis of massive grain-size distributions in sedimentological and paleoenvironmental research. In this study, we use numerical techniques to develop a novel strategy enabling the efficient and flexible unmixing of single-sample grain-size distributions. The method was applied to a large set of grain-size distributions of late Quaternary aeolian sediments from the desert-loess transition zone around the Tengger Desert in North China. The potential structures of the pooled unmixed subpopulations were derived using a finite mixture model, taking variances into account, together with a pattern recognition (clustering) method. Five unimodal endmembers were identified by the finite mixture model and four prevalent structural patterns were recognized by the clustering method. The significances of the endmembers and structural patterns are discussed in detail. We demonstrate that the single-sample unmixing method can be used to complement the widely applied end-member modelling to reveal potential provenances of large datasets of grain-size distributions, and that the component structures of grain-size distributions may contain valuable information that can be used to constrain the associated paleoenvironmental conditions. • A reproducible framework for unmixing massive single-sample GSDs. • New methods for analyzing and visualizing pooled unmixing results. • Efficient and flexible open-source R functions for processing large GSD datasets. • Application to aeolian sediments from the desert-loess transition zone of the Tengger Desert. • The component structures of GSDs contain valuable paleoenvironmental information.