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• W3089040932 abstract "Long-term changes in flood activity have often been reconstructed to understand their relationships to climate changes. This requires identification of flood layers according to certain characteristics (e.g., texture, geochemical composition, grain-size) and then to count them using naked-eye observation. This method is, however, time-consuming, and intrinsically characterized by a low resolution that may lead to bias and misidentification. To overcome this limitation, high-resolution analytical approaches can be used, such as X-ray fluorescence spectroscopy (XRF), X-ray computed tomography, or hyperspectral imaging (HSI). When coupled with discriminant algorithms, HSI allows for automatic identification of event layers. Here, we propose a new method of flood layers identification and counting based on the combination of both HSI and XRF core scanner analyses, applied to a Lake Bourget (French Alps) sediment sequence. We use a hyperspectral sensor from the short wave-infrared spectral range to create a discrimination model between event layers and continuous sedimentation. This first step allows the estimation of a classification map, with a prediction accuracy of 0.96, and then the automatic reconstruction of a reliable chronicle of event layers (including their occurrence and deposit thicknesses). XRF signals are then used to discriminate flood layers among all identified event layers based on site-specific geochemical elements (in the case of Lake Bourget: Mn and Ti). This results in an automatically generated flood chronicle. Changes in flood occurrence and event thickness through time reconstructed from the automatically generated floods chronicle are in good agreement with the naked-eye-generated chronicle. In detail, differences rely on a larger number of detected flood events (i.e., increase of 9% of the number of layers detected) and a more precise layer thickness estimation, thanks to a higher resolution. Therefore, the developed methodology opens a promising avenue to increase both the efficiency (timesaving) and robustness (higher accuracy) of paleoflood reconstructions from lake sediments. Also, this methodology can be applied to identify any specific layers (e.g., varve, tephra, mass-movement turbidite, tsunami) and, thereby, it has a direct implication in paleolimnology, paleoflood hydrology and paleoseismology from sediment archives." @default.
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• W3089040932 date "2020-11-01" @default.
• W3089040932 modified "2023-10-16" @default.
• W3089040932 title "XRF and hyperspectral analyses as an automatic way to detect flood events in sediment cores" @default.
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• W3089040932 doi "https://doi.org/10.1016/j.sedgeo.2020.105776" @default.
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