Abstract. Statistical analysis has become increasingly important in optically
stimulated luminescence (OSL) dating since it has become possible to measure
signals at the single-grain scale. The accuracy of large chronological
datasets can benefit from the inclusion, in chronological modelling, of
stratigraphic constraints and shared systematic errors. Recently, a number
of Bayesian models have been developed for OSL age calculation; the R
package “BayLum” presented herein allows different models of this type to be implemented,
particularly for samples in stratigraphic order which share systematic
errors. We first show how to introduce stratigraphic constraints in
BayLum; then, we focus on the construction, based on measurement
uncertainties, of dose covariance matrices to account for systematic errors
specific to OSL dating. The nature (systematic versus random) of errors
affecting OSL ages is discussed, based – as an example – on the dose rate
determination procedure at the IRAMAT-CRP2A laboratory (Bordeaux). The
effects of the stratigraphic constraints and dose covariance matrices are
illustrated on example datasets. In particular, the benefit of combining
the modelling of systematic errors with independent ages, unaffected by
these errors, is demonstrated. Finally, we discuss other common ways of
estimating dose rates and how they may be taken into account in the
covariance matrix by other potential users and laboratories. Test datasets
are provided as a Supplement to the reader, together with an R
markdown tutorial allowing the reproduction of all calculations and figures
presented in this study.
Isolation of Quartz Grains for Optically Stimulated Luminescence (OSL) Dating of Quaternary Sediments for Paleoenvironmental Research
