THE INTCAL20 APPROACH TO RADIOCARBON CALIBRATION CURVE CONSTRUCTION: A NEW METHODOLOGY USING BAYESIAN SPLINES AND ERRORS-IN-VARIABLES
THE INTCAL20 APPROACH TO RADIOCARBON CALIBRATION CURVE CONSTRUCTION: A NEW METHODOLOGY USING BAYESIAN SPLINES AND ERRORS-IN-VARIABLES
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DOI：
10.1017/rdc.2020.46
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发表时间：
2020-08-01
期刊：
影响因子：
8.3
通讯作者：
Scott, E. Marian
中科院分区：
文献类型：
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作者：
Heaton, Timothy J.;Blaauw, Maarten;Scott, E. Marian
为了创建可靠的放射性碳校准曲线，不仅需要高质量的数据，还需要可靠的统计方法。许多校准数据的独特方面带来了相当大的建模挑战，需要一种量身定制的曲线构建方法，以准确地表示和适应这些个性，将数据集中到一条曲线中。对于IntCal 20，统计方法已经经历了一个完整的重新设计，从IntCal 04，IntCal 09和IntCal 13中使用的随机游走，到基于贝叶斯样条和变量误差的方法。新的样条方法仍然使用马尔可夫链蒙特卡罗（MCMC）进行拟合，但与以前的随机游走相比具有相当大的优势，包括更快，更可靠的曲线构建以及建模选择的灵活性和细节大大增加。本文介绍了新的方法，以及整合各种数据集所需的定制修改。对最终用户而言，关键的变化包括识别和估计C-14测定中潜在的过度分散，以及其对校准的影响，我们通过在曲线上提供预测区间来解决这一问题;改进C-14快速漂移和储层年龄/死碳分数的建模;以及希望确保MCMC的更好混合的修改，这因此增加了估计曲线的置信度。
To create a reliable radiocarbon calibration curve, one needs not only high-quality data but also a robust statistical methodology. The unique aspects of much of the calibration data provide considerable modeling challenges and require a made-to-measure approach to curve construction that accurately represents and adapts to these individualities, bringing the data together into a single curve. For IntCal20, the statistical methodology has undergone a complete redesign, from the random walk used in IntCal04, IntCal09 and IntCal13, to an approach based upon Bayesian splines with errors-in-variables. The new spline approach is still fitted using Markov Chain Monte Carlo (MCMC) but offers considerable advantages over the previous random walk, including faster and more reliable curve construction together with greatly increased flexibility and detail in modeling choices. This paper describes the new methodology together with the tailored modifications required to integrate the various datasets. For an end-user, the key changes include the recognition and estimation of potential over-dispersion in C-14 determinations, and its consequences on calibration which we address through the provision of predictive intervals on the curve; improvements to the modeling of rapid C-14 excursions and reservoir ages/dead carbon fractions; and modifications made to, hopefully, ensure better mixing of the MCMC which consequently increase confidence in the estimated curve.