Modelling dose rate to single grains of quartz in well-sorted sand samples: The dispersion arising from the presence of potassium feldspars and implications for single grain OSL dating

Abstract

N. (2015). Modelling dose rate to single grains of quartz in well-sorted sand samples: The dispersion arising from the presence of potassium feldspars and implications for single grain OSL dating. Quaternary Geochronology, 27, 52-65. DOI: 10.1016DOI: 10. /j.quageo.2014 Modelling dose rate to single grains of quartz in well-sorted sand samples: the dispersion arising from 1 the presence of potassium feldspars and implications for single grain OSL dating 2 and feldspar spheres representing a sand sample. Based on the simulation results, we explain the 24 discrepancy between intrinsic and natural overdispersion values in a well-bleached sample, thus 25 validating the model. The three parameters having the most influence on dispersion in dose rate 26 distributions, and modelled in this study, appear to be grain size, potassium content and total dose rate. 27Finally an analysis of measurement uncertainties and other sources of variations in equivalent dose 28 estimates leads us to conclude that all age models (both logged and unlogged) which include an 29 overdispersion value to weight individual D e values rely mainly on unknown parameters; this ignorance 30 may lead to an inadvertent bias in D e estimates. Assuming counting statistics make a small contribution 31 to dispersion (as is often the case), we suggest that in some cases it is most appropriate to use 32 unweighted averages of equivalent doses when dividing by commonly measured average dose rates. 33 34

Testing the accuracy of a Bayesian central-dose model for single-grain OSL, using known-age samples

“…) and are supposedly the youngest samples of the series (~2 ka, based on the production age of ceramics found in a foundation trench of the castle, corroborated by archaeo-magnetism measurements). The inter-comparison sample comes from a beachridge located in Skagen (Denmark) and it has been selected as it has been the basis for a wide, luminescence community-scale study involving a number of OSL laboratories across the world (Buylaert et al, 2006;Murray et al, 2015;Guérin et al, 2015a). We consider it as a reference sample, whose expected age (~4 ka) is taken as the average of all measurements (n=24, mainly on multi-grain aliquots) reported in the frame of the intercomparison study.…”

The complementarity of luminescence dating methods illustrated on the Mousterian sequence of the Roc de Marsal: A series of reindeer-dominated, Quina Mousterian layers dated to MIS 3

“…So if we take for the numerator of the age equation the geometric mean of doses received by the grains, we will get an age estimate equal to the square root of (1-x²) multiplied by the age A, which will be less than or equal to A. Conversely, taking for the numerator of the age equation the arithmetic mean of doses to quartz grains, our estimate of A will be unbiased. This statement can be generalised: no matter what the distribution of dose rates to individual grains is, the invariant parameter is the amount of energy available for the grains, independently of how the radioactivity is distributed in the sample (see Guérin et al, 2012b;Guérin et al, 2015c). Thus, the aim of any statistical modelling should be the average dose received by the grains, rather than the geometric mean of the distribution of doses to individual grains (distribution which is always unknown and not accessible experimentally; Guérin et al, 2015c).…”

“…This statement can be generalised: no matter what the distribution of dose rates to individual grains is, the invariant parameter is the amount of energy available for the grains, independently of how the radioactivity is distributed in the sample (see Guérin et al, 2012b;Guérin et al, 2015c). Thus, the aim of any statistical modelling should be the average dose received by the grains, rather than the geometric mean of the distribution of doses to individual grains (distribution which is always unknown and not accessible experimentally; Guérin et al, 2015c). In other words, the CDM appears to be based on a biased dose estimator (at least in cases for which the dispersion in single grain doses is important compared to other sources of dispersion in D e estimates), in contrast with all three other models discussed here (BaSar, AM-CDM and UAM).…”

“…Nevertheless, relatively low K contents (~0.5 % on average) associated with rather low total dose rates (~1 Gy.ka -1 ) are expected to give rise to non-negligible dispersion in dose rates to individual grains. Indeed, low dose rates imply that, despite relatively low K content, beta dose rate from potassium contributes to the total dose rate by a significant amount (Table 2); and the low content in potassium, together with the observation that potassium feldspar grains are present in the sediment samples, indicates that a significant source of dose rates is heterogeneously, at the sub-mm scale, distributed in hotspots (Guérin et al, 2015c). The extrinsic overdispersion values, calculated as the quadratic difference between the OD values for natural and dose recovery distributions, range from ~17 to ~27 %; such values indeed lie towards the higher end of expected dispersion values for well-sorted sand samples (cf.…”

“…From this perspective, given the commonly asymmetric distributions of dose rates (whenever hotspots, like ubiquitous K-feldspar grains, are present in the sediment -cf. Nathan et al, 2003;Brennan, 2006;Mayya et al, 2006;Cunningham et al, 2012;Guérin et al, 2015c), the AM-CDM is probably better adapted to most Quaternary sediments than the BaSar model. Nonetheless, the BaSar ages appear to be more precise (i.e., yield lower uncertainties) than the UAM and the AM-CDM ages: the average statistical uncertainty on central dose is 3 % with the Basar model, compared to respectively 5 and 6 % for the UAM and AM-CDM (see Table 3).…”