New methods for unmixing sediment grain size data

Abstract

Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End‐member analysis (EMA), which fits one set of end‐members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end‐members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single‐specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end‐members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data.

Deglaciation history of Lake Ladoga (northwestern Russia) based on varved sediments

“…Grain-size data were converted to three end members (EM) using the MatLab-based software Analy-Size (Paterson & Heslop 2015). The measurement was performed using a Laser Diffraction Particle Size Analyzer LS 13 320 (Beckman Coulter, USA) equipped with an Auto Prep Station.…”

End-member modeling of the grain-size record of Sikouzi fine sediments in Ningxia (China) and implications for temperature control of Neogene evolution of East Asian winter monsoon

“…In this study, we reanalyzed the Sikouzi grain-size data composed of 3398 samples [15] using a new developed GUI software of AnalySize for processing and unmixing grain size data [19]. In the correlation map between multiple correlation coefficient (R 2 ) and end-member number (Fig 2A), end-member modeling improved greatly from 2 to 3 end members, but improved fairly less from 3 to 4 end members.…”

ENSO‐Like Modulated Tropical Pacific Climate Changes Since 2.36 Myr and Its Implication for the Middle Pleistocene Transition

“…The different end-members and their variations are corresponding to: (1) different controlling mechanisms of sediment transport and/or supplied from different sources and/or (2) optional mechanisms which change systematically the grain-size distribution along the transport and deposition of the sediment from the source area (Boulay et al, 2007;Prins et al, 2000;Prins & Weltje, 1999). In this study, we applied recently developed algorithms for end-member modeling of compositional data (Paterson & Heslop, 2015) to the grain-size distributions of the detrital sediment in core MD06-3050. Those studies applied the EMA method to identify the turbiditic, fluvial, eolian, or ice-rafted end-members based on their site character.…”

“…Geochemistry, Geophysics, Geosystems 10.1002/2017GC007247 specimen unmixing techniques, represent an improvement over existing algorithms for addressing major issues in identifying grain-size subpopulations (Paterson & Heslop, 2015).…”