Stochastic modelling of multi-grain equivalent dose ( De) distributions: Implications for OSL dating of sediment mixtures

Abstract

A number of recent optically stimulated luminescence (OSL) studies have cited post-depositional mixing as a dominant source of equivalent dose ( D e) scatter across a range of sedimentary environments, including those previously considered ‘best suited’ for OSL dating. The potentially insidious nature of sediment mixing means that this problem may often only be identifiable by careful statistical analysis of D e data sets. This study aims to address some of the important issues associated with the characterisation and statistical treatment of mixed D e distributions at the multi-grain scale of analysis, using simulated D e data sets produced with a simple stochastic model. Using this Monte Carlo approach we were able to generate theoretical distributions of single-grain D e values, which were then randomly mixed together to simulate multi-grain aliquot D e distributions containing a known number of mixing components and known corresponding burial doses. A range of sensitivity tests were undertaken using sediment mixtures with different aged dose components, different numbers of mixing components, and different types of dose component distributions (fully bleached, heterogeneously bleached and significantly overdispersed D e distributions). The results of our modelling simulations reveal the inherent problems encountered when dating mixed sedimentary samples with multi-grain D e estimation techniques. ‘Phantom’ dose components (i.e. discrete dose populations that do not correspond to the original single-grain mixing components) are an inevitable consequence of the ‘averaging’ effects of multi-grain D e analysis, and prevent the correct number of mixing components being identified with the finite mixture model (FMM) for all of the multi-grain mixtures tested. Our findings caution against use of the FMM for multi-grain aliquot D e data sets, even when the aliquots consist of only a few grains.