Publication Date:
2015-11-24
Description:
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 co-variability within a data set and can be considered as a non-parametric approach. Available EMA algorithms, however, either produce sub-optimal 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. Non-parametric EMA is often unable to identify unimodal grain size sub-populations 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 non-uniqueness 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 sub-populations 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. This article is protected by copyright. All rights reserved.
Electronic ISSN:
1525-2027
Topics:
Chemistry and Pharmacology
,
Geosciences
,
Physics
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