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Inversion of provenance data and sediment load into spatially varying erosion rates
Earth Surface Processes and Landforms ( IF 3.3 ) Pub Date : 2020-09-25 , DOI: 10.1002/esp.5008
Fien De Doncker 1 , Frédéric Herman 1 , Matthew Fox 2
Affiliation  

Sediment fingerprinting methods aim to determine the relative contribution of different source areas in detrital sediments based on natural properties – fingerprints – of the source areas. Here, we use U/Th–Pb age signatures as fingerprints, assuming that the age signal is not altered during erosion–transportation–deposition events, and given that recent technological advances enable precise dating of large amounts of grains. We introduce a formal inversion method that allows to disentangle the amalgamation of source contributions in detrital zircon data and enables to convert this information into an erosion rate map starting from the spatial distribution of zircon age signatures. Relying on the least‐squares method and using prior and covariance information to deal with non‐uniqueness, we show, using synthetic and natural examples, that we are able to retrieve erosion rate patterns of a catchment when the age distribution and zircon fertility for each source area are well known. Moreover, we show that not only zircon age fingerprints but also other tracers such as mineral content can be used. Furthermore, we found that adding data from samples taken at the outlet of tributaries improves the estimation of erosion rate patterns. We conclude that the least squares inverse model applied to detrital data has great potential for investigating erosion rates. © 2020 John Wiley & Sons, Ltd.

中文翻译:

将物源数据和沉积物负荷转化为空间变化的侵蚀率

沉积物指纹法旨在根据源区的自然特征(指纹)确定碎屑沉积物中不同源区的相对贡献。在这里,我们使用U / Th-Pb年龄标记作为指纹,假设在侵蚀-运输-沉积过程中年龄信号没有改变,并且考虑到最新的技术进步可以对大量谷物进行精确的年代测定。我们引入了一种正式的反演方法,该方法可以消除碎屑锆石数据中源贡献的合并,并使该信息从锆石年龄特征的空间分布开始转换为侵蚀速率图。依靠最小二乘法,并使用先验和协方差信息来处理非唯一性,我们使用合成和自然示例说明 当每个来源地区的年龄分布和锆石肥力众所周知时,我们就能获得流域的侵蚀速率模式。此外,我们表明不仅可以使用锆石年龄指纹,而且可以使用其他示踪剂(例如矿物质含量)。此外,我们发现,从支流出口处采集的样本中添加数据可以改善对侵蚀速率模式的估算。我们得出的结论是,应用于碎屑数据的最小二乘反演模型具有研究侵蚀速率的巨大潜力。分级为4 +©2020 John Wiley&Sons,Ltd. 我们发现,从支流出口处采集的样本中添加数据可以改善对侵蚀速率模式的估算。我们得出的结论是,应用于碎屑数据的最小二乘反演模型具有研究侵蚀速率的巨大潜力。分级为4 +©2020 John Wiley&Sons,Ltd. 我们发现,从支流出口处采集的样本中添加数据可以改善对侵蚀速率模式的估算。我们得出的结论是,应用于碎屑数据的最小二乘反演模型具有研究侵蚀速率的巨大潜力。分级为4 +©2020 John Wiley&Sons,Ltd.
更新日期:2020-09-25
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