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  • Machine-learning  (1)
  • temporal variability  (1)
  • 1
    Publication Date: 2023-07-20
    Description: The major-element chemical composition of garnet provides valuable petrogenetic information, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk-rock composition and mineral paragenesis of the initial garnet-bearing host-rock is absent. This prevents the application of chemical thermo-barometric techniques and calls for quantitative empirical approaches. Here we present a garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 chemical analyses of garnet that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) 〉 95% concerning the setting, that is either mantle, metamorphic, igneous, or metasomatic; (ii) 〉 84% concerning the metamorphic facies, that is either blueschist/greenschist, amphibolite, granulite, or eclogite/ultrahigh-pressure; and (iii) 〉 93% concerning the host-rock bulk composition, that is either intermediate–felsic/metasedimentary, mafic, ultramafic, alkaline, or calc–silicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization.
    Description: deutsche forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: Georg-August-Universität Göttingen (1018)
    Description: http://134.76.17.86:443/garnetRF/
    Keywords: ddc:549 ; Garnet major-element composition ; Database ; Host-rock discrimination ; Machine-learning ; Provenance ; Web app
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2024-02-12
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Sediment composition in modern fluvial settings is commonly assessed regarding spatial but rarely temporal variability, potentially leading to a bias of unknown extent. Here, we present the grain‐size distribution, bulk chemical and mineralogical composition of a time‐series set of 36 suspended sediment samples from the Brahmaputra river, as well as clay and heavy mineral analysis of selected samples. Sampling covers the June–November 2021 period, which included two major flooding events. We show that the two flooding events are characterized by contrasting grain size, with the first event characterized by a grain‐size minimum and the second by a grain‐size maximum. Although grain sizes of the first flood and the period after the second are similar, their compositions differ significantly, highlighted by a factor‐two decrease of biotite largely compensated by an increase in quartz. By contrast, the content of garnet, clinopyroxene, sillimanite, and rutile increased compared to epidote and amphibole during the second flood event. By relating the results to spatio‐temporal rainfall and discharge patterns and basin morphology, we conclude that the first flooding primarily mobilized hydraulically pre‐sorted sediments from the exposed sandbars of the floodplains, while those sandbars are already submerged during the second flooding in a single‐channel system, resulting in higher sediment contributions from highland tributaries draining igneous and high‐grade metamorphic rocks. Such temporal variations pose constraints on the interpretation of compositional differences between individual samples regarding sediment provenance and dispersal and should be considered in studies of modern drainage basins as well as ancient sediment routing systems.〈/p〉
    Description: Plain Language Summary: Sediment provenance, which refers to where the sediment in a river comes from, is important to understand because it can tell us about the geology of an area, various earth‐surface processes and how the landscape is changing over time. However, sediment provenance is typically studied at a spatial scale in present day river basins, and temporal variability is rarely considered. This study examines the physical, chemical and mineralogical properties of sediment in the Brahmaputra river during two major flooding events that occurred in the same season. The results show that the sediment composition varies between the events, indicating a change in the relative proportions of distinct sources. This emphasizes the importance of considering temporal variations in sediment composition when interpreting sediment provenance signals.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Time‐series analysis of sediment composition during two major flooding events of a single monsoon season is presented〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The two flooding events show contrasting grain‐size, chemical and mineralogical composition〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Temporal variations in sediment composition pose constraints on the interpretation of provenance and dispersal based on individual samples〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: DAAD
    Description: German Ministry of Education and Research
    Description: https://doi.org/10.5281/zenodo.7588054
    Description: http://flood.umd.edu/
    Keywords: ddc:551.3 ; sediment provenance ; temporal variability ; intra‐seasonal ; Brahmaputra ; eastern Himalaya
    Language: English
    Type: doc-type:article
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