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Abstract

End-member modelling analysis is a statistic approach to unmixing multimodal grain size distributions to identify and quantify processes of sediment generation, transport and deposition. While the different computational implementations have been extensively benchmarked and show similarly high reliability characteristics, there is a series of unknowns regarding the applicability, quality and limitations of the method from a practical point of view. This study explores these important unknowns using both empirical and synthetic samples along with Monte Carlo tests. Under ideal conditions (all avail able samples, randomly mixed components, 116 grain-size classes), EMMA is able to model the grain-size distributions of input end-members (loadings) with R2 between 0.63 and 0.98 and their relative contributions to each sample (scores) with R2 between 0.71 and 0.81, thus setting the baseline for model quality. Inappropriate model parameter settings cause severe drops in R2. EMMA is able to detect an end-member even if it is present in only one sample or when it contributes less than 10 vol.-%. With 20 to 40 samples or more, stable, high quality model results are possible. With 15 or more grain-size classes, model results also reach such stable high reproducibility levels. EMMA can depict originally multimodal end-members (R2 between 0.78 and 0.99). End-members with identical relative grain-size distribution shape can overlap significantly without causing quality drops; R2 of identical distributions are invariantly high until mode positions are less than three grain-size classes apart from each other. Gradually widening end-member distributions do not affect the results significantly. However, shifting mode positions have a severe impact. Post-depositional mixing causes drastic deviations of the modelled scores, whereas the loadings are virtually unaffected. In light of these tests, EMMA is a reliable, mostly unbiased tool to identify and quantify sediment generation/transport/deposition regimes from mixed sediment deposits, given it is used in a geoscientifically meaningful context.