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Realizing the potential of fluvial archives using robust OSL chronologies

机译:利用强大的OSL时序来挖掘河流档案的潜力

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Optically Stimulated Luminescence (OSL) dating has enormous potential for interpreting fluvial sediments, because the mineral grains used for OSL dating are abundant in fluvial deposits. However, the limited light exposure of mineral grains during fluvial transport and deposition often leads to scatter and inaccuracy in OSL dating results. Here we present a statistical protocol which aims to overcome these difficulties. Rather than estimating a single burial age for a sample, we present ages as likelihood functions created by bootstrap re-sampling of the equivalent-dose data. The bootstrap likelihoods incorporate uncertainty from age-model parameters and plausible variation in the input data. This approach has the considerable advantage that it permits Bayesian methods to be used to interpret sequences containing multiple samples, including partially bleached OSL data. We apply the statistical protocol to both single-grain and small-aliquot OSL data from samples of recent fluvial sediment. The combination of bootstrap likelihoods and Bayesian processing may greatly improve OSL chronologies for fluvial sediment, and allow OSL ages from partially bleached samples to be combined with other age information.
机译:光学激发发光(OSL)测年法在解释河流沉积物方面具有巨大潜力,因为用于OSL测年的矿物质在河流沉积物中丰富。但是,在河床运输和沉积过程中矿物颗粒的有限光照通常会导致OSL测年结果发生散射和不准确。在这里,我们提出了旨在克服这些困难的统计协议。而不是估计样本的单个丧葬年龄,我们将年龄表示为通过对等效剂量数据进行自举重新采样而创建的似然函数。自举可能性包括年龄模型参数的不确定性和输入数据中可能出现的变化。该方法具有相当大的优势,它允许使用贝叶斯方法来解释包含多个样本(包括部分漂白的OSL数据)的序列。我们将统计协议应用于来自最近河流沉积物样本的单颗粒和小等分OSL数据。自举可能性和贝叶斯处理的结合可以大大改善河流沉积物的OSL时间序列,并使部分漂白样品的OSL年龄与其他年龄信息结合在一起。

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