| Autor: |
Lünsdorf, Nils Keno, Lünsdorf, Jan Ontje, Újvári, Gábor, Dunkl, István, Wolfram, Lukas, Hobrecht, Adrian, Laake, Lothar, von Eynatten, Hilmar |
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| Zdroj: |
Journal of Geophysical Research. Earth Surface; Dec2023, Vol. 128 Issue 12, p1-16, 16p |
| Abstrakt: |
Provenance information from recent and ancient sedimentary archives is obscured by several factors and for disentangling these intermingled signals, analysis by multiple methods is paramount. In sedimentary provenance analysis (SPA), single‐grain methods determining mineralogy, chemical composition, or radiometric ages are of key importance but are mostly applied to sand‐sized sediments or sedimentary rocks. Finer grained sediments or sedimentary rocks are usually analyzed by whole‐rock geochemical means and seldom by single‐grain methods. Considering the abundance of fine‐grained sedimentary archives, a strong need for single‐grain, multi‐method analyses of silt‐sized sediments is obvious. Thus, we propose a workflow that is optimized for sample throughput and correlative analysis of fine‐grained sediments based on machine learning methods. The feasibility of the workflow is demonstrated by differentiating three Central European loess‐paleosol‐sequences. The increased sample throughput enables access to sedimentary archives at high spatial and/or temporal resolution, which will open up new research pathways in SPA of silt‐sized sediments. Plain Language Summary: Deciphering the origin of sediments and sedimentary rocks is the main goal in sedimentary provenance analysis (SPA). This is usually done by determining the mineralogical and chemical composition or absolute age of the mineral grains that make up the sediment. However, the information gained from a single method is often insufficient to provide a complete picture in sedimentary provenance. Thus, many different methods are used, preferably on individual grains. Since analysis is easier and potentially more precise when done on larger grains, the sand‐size fraction is usually investigated. Consequently, samples of finer grain size (e.g., silt) are less often measured. This is contrasting the fact that about 50% of sedimentary rocks are of such finer grain sizes. Therefore, an approach is required that allows to apply different micro‐analytical tools to the same silt‐sized minerals and combine the gained data. Here, we propose such an approach, which is based on machine learning methods and has been optimized for sample throughput. We successfully tested it's feasibility by differentiating Central European loess sequences. The high sample throughput of the approach will allow analyzing fine‐grained sedimentary archives at high temporal resolution, thereby enabling new research pathways in SPA. Key Points: Sedimentary provenance analysis relies on information derived from single‐grain methods usually applied to sand‐sized mineralsAn innovative workflow for correlative, single‐grain analysis of silt‐sized minerals is presentedAccess to sedimentary archives at high temporal and/or spatial resolution is enabled by the workflow [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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