Using microcomputed tomography (?CT) to count varves in lake sediment sequences: Application to Lake Sagtjernet, Eastern Norway

QUATERNARY GEOCHRONOLOGY(2023)

引用 0|浏览5
暂无评分
摘要
Varved lake sediments are one of the most important natural archives that allow annual resolution paleoclimate reconstructions. Conventional varve counting techniques use thin sections to manually identify lamina. However, this technique is destructive, labour intensive and limited to a 2D representation of complex 3D features which may lead to misidentification of varve boundaries. This study presents the use of microcomputed tomography (mu CT) scans in constructing varve chronologies, utilizing scanning resolutions of -50 mu m (binned to -200 mu m) for core sections up to 150 cm long. To evaluate this method, we cored and analysed Lake Sagtjernet in Eastern Norway - revealing a 593 cm-long sediment record of the past 10274 (+220/-329) years, with continuous laminations from 84 to 533 cm depth (75% of the sediments). Through limnological monitoring and microfacies analyses we demonstrate that the laminations are rare ferrogenic varves, with an annual deposition pattern comprised of seasonal changes in biogenic production superimposed on seasonal precipitates of iron and manganese hydroxides. The floating mu CT-counted varve chronology presented here is the first non-glacial varve chronology in Norway and covers 4023 +113/-185 years. We find that mu CT scans allow for a very fast and nondestructive way of counting varves with sufficient detail of varve boundaries. In the few sections where varve boundaries are too vague to resolve, we recommend using complimentary techniques such as thin sections in parallel. The varve chronology is in good agreement with the 95% confidence interval of the independent radiocarbon chronology based on 16 14C dates, and 210Pb and 137Cs activity peaks, indicating that the varve chronology can be equated to calendar age.
更多
查看译文
关键词
Lake sediments,Varve chronology,Bayesian age modelling,Microcomputed tomography,Geochronology,Holocene
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要