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Seismic Modeling of Bedload Transport in a Gravel‐Bed Alluvial Channel

推移质 地质学 冲积层 泥沙输移 冲积扇 高含沙水流 频道(广播) 地貌学 水文学(农业) 岩土工程 沉积物 工程类 构造盆地 电气工程
作者
Loc Luong,Daniel Cadol,S. L. Bilek,J. Mitchell McLaughlin,Jonathan B. Laronne,Jens M. Turowski
出处
期刊:Journal Of Geophysical Research: Earth Surface [Wiley]
卷期号:129 (9)
标识
DOI:10.1029/2024jf007761
摘要

Abstract Recent theoretical models and field observations suggest that fluvial bedload flux can be estimated from seismic energy measured within appropriate frequency bands. We present an application of the Tsai et al. (2012, https://doi.org/10.1029/2011gl050255 ) bedload seismic model to an ephemeral channel located in the semi‐arid southwestern US and incorporate modifications to better estimate bedload flux in this environment. To test the model, we collected streambank seismic signals and directly measured bedload flux during four flash‐floods. Bedload predictions calculated by inversion from the Tsai model underestimated bedload flux observations by one‐to‐two orders of magnitude at low stages. However, model predictions were better for moderate flow depths (>50 cm), where saltation is expected to dominate bedload transport. We explored three differences between the model assumptions and our field conditions: (a) rolling and sliding particles have different impact frequencies than saltating particles; (b) the velocity and angle of impact of rolling particles onto the riverbed differ; and (c) the fine‐grained alluvial character of this and similar riverbeds leads to inelastic impacts, as opposed to the originally conceptualized elastic impacts onto rigid bedrock. We modified the original model to assume inelastic bed impacts and to incorporate rolling and sliding by adjusting the statistical distributions of bedload impact frequency, velocity, and angle. Our modified “multiple‐transport‐mode bedload seismic model” decreased error relative to observations to less than one order of magnitude across all measured flow conditions. Further investigations in other environmental settings are required to demonstrate the robustness and general applicability of the model.
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