Abstract. The analysis of grain-size distributions has a long tradition in
Quaternary Science and disciplines studying Earth surface and subsurface
deposits. The decomposition of multi-modal grain-size distributions into
inherent subpopulations, commonly termed end-member modelling analysis
(EMMA), is increasingly recognised as a tool to infer the underlying
sediment sources, transport and (post-)depositional processes. Most of the
existing deterministic EMMA approaches are only able to deliver one out of
many possible solutions, thereby shortcutting uncertainty in model
parameters. Here, we provide user-friendly computational protocols that
support deterministic as well as robust (i.e. explicitly accounting for
incomplete knowledge about input parameters in a probabilistic approach)
EMMA, in the free and open software framework of R. In addition, and going beyond previous validation tests, we compare the
performance of available grain-size EMMA algorithms using four real-world
sediment types, covering a wide range of grain-size distribution shapes
(alluvial fan, dune, loess and floodplain deposits). These were randomly
mixed in the lab to produce a synthetic data set. Across all algorithms, the
original data set was modelled with mean R2 values of 0.868 to 0.995
and mean absolute deviation (MAD) values of 0.06 % vol to 0.34 % vol. The original
grain-size distribution shapes were modelled as end-member loadings with
mean R2 values of 0.89 to 0.99 and MAD of 0.04 % vol to 0.17 % vol. End-member scores reproduced the original mixing ratios in the
synthetic data set with mean R2 values of 0.68 to 0.93 and MAD
of 0.1 % vol to 1.6 % vol. Depending on the validation criteria, all models
provided reliable estimates of the input data, and each of the models
exhibits individual strengths and weaknesses. Only robust EMMA allowed uncertainties of the end-members to
be objectively estimated and expert knowledge to be included in the end-member definition. Yet, end-member interpretation should
carefully consider the geological and sedimentological meaningfulness in
terms of sediment sources, transport and deposition as well as
post-depositional alteration of grain sizes. EMMA might also be powerful in
other geoscientific contexts where the goal is to unmix sources and
processes from compositional data sets.