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Radiometric dating of recent sediments: On the performance of Pb-210-based CRS chronologies under varying rates of supply

机译:近期沉积物的辐射测定:关于不同供应率下的PB-210基CRS时间顺序的性能

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摘要

Radiometric dating was a revolutionary contribution to the study of sedimentary processes. Empirical data from varved sediments show that unsupported (210)pb (Pb-210(exc)) fluxes vary over time while they statistically correlate with sediment accumulation rates (SAR). This contradicts the basic assumption of the Constant Rate of Supply (CRS) model, which is the most widely used technique for recent sediments. This work is aimed at assessing the CRS model-errors by quantifying the effects on chronologies of various patterns of temporal variability of fluxes. Results are discussed through applications with synthetic and real cores for which and independent varve chronology or a set of reference dates are available. Periodic harmonic and random fluctuations in fluxes tend to cancel out positive and negative deviations in CRS ages, but they produce spurious variability in SARs. Persistent changes in fluxes lead to cumulated and unacceptable deviations of the CRS ages, which cannot be satisfactorily compensated by the use of a piecewise CRS model. The raw and piecewise CRS models can still be powerful tools of widespread use when the conditions for their applicability are well understood. The paper shows how the analysis of clusters in the Pb-210(exc) vs mass depth profile, along with the estimation of equivalent constant fluxes pre and post-dating a known reference date, are powerful methods to prevent misapplications of the CRS model.
机译:辐射测定是对沉积过程研究的革命性贡献。来自变形沉积物的经验数据表明,不支持的(210)Pb(Pb-210(Exc))助熔剂随着时间的变化而变化,而它们与沉积物积累速率(SAR)统计相关。这与恒定供应率(CRS)模型的基本假设相矛盾,这是最近沉积物最广泛使用的技术。这项工作旨在通过量化对助焊剂的各种模式的各种模式的时间表的影响来评估CRS模型误差。结果是通过具有合成和真实核心的应用讨论,其中有哪些和独立变化年表或可用的一组参考日期。助势中的定期谐波和随机波动倾向于抵消CRS年龄的正面和负偏差,但它们在SARS中产生虚假变异性。持续变化的助熔剂导致CRS年龄的累积和不可接受的偏差,通过使用分段CRS模型不能令人满意地得到令人满意的补偿。当适用性的条件很好地理解时,原始和分段CRS模型仍然可以是广泛使用的强大工具。本文显示了如何在PB-210(ECC)VS质量深度分布中的簇分析以及估计当量恒定的磁通量预先和约会后的已知参考日期,是防止CRS模型的缺陷的强大方法。

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