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Published January 2019 | public
Journal Article

Particle transport mechanics and induced seismic noise in steep flume experiments with accelerometer-embedded tracers

Abstract

Recent advances in fluvial seismology have provided solid observational and theoretical evidence that near‐river seismic ground motion may be used to monitor and quantify coarse sediment transport. However, inversions of sediment transport rates from seismic observations have not been fully tested against independent measurements, and thus have unknown but potentially large uncertainties. In the present study, we provide the first robust test of existing theory by conducting dedicated sediment transport experiments in a flume laboratory under fully turbulent and rough flow conditions. We monitor grain‐scale physics with the use of 'smart rocks' that consist of accelerometers embedded into manufactured rocks, and we quantitatively link bedload mechanics and seismic observations under various prescribed flow and sediment transport conditions. From our grain‐scale observations, we find that bedload grain hop times are widely distributed, with impacts being on average much more frequent than predicted by existing saltation models. Impact velocities are observed to be a linear function of average downstream cobble velocities, and both velocities show a bed‐slope dependency that is not represented in existing saltation models. Incorporating these effects in an improved bedload‐induced seismic noise model allows sediment flux to be inverted from seismic noise within a factor of two uncertainty. This result holds over nearly two orders of magnitude of prescribed sediment fluxes with different sediment sizes and channel‐bed slopes, and particle–particle collisions observed at the highest investigated rates are found to have negligible effect on the generated seismic power. These results support the applicability of the seismic‐inversion framework to mountain rivers, although further experiments remain to be conducted at sediment transport near transport capacity.

Additional Information

© 2018 John Wiley & Sons, Ltd. Received 29 November 2017; Revised 14 August 2018; Accepted 20 August 2018; Issue Online: 17 January 2019. This work was supported by NSF grant EAR‐1558479, the Terrestrial Hazard Observation and Reporting Center at the California Institute of Technology and the ANR grant ANR‐17‐CE01‐0008‐01. We thank Anne‐Sophie Drouet for help in processing the smart rock data, Fanny Brun and Jeff Prancevic for help in conducting preliminary flume and smart rock experiments, and Bismark Wong for help in inferring sediment flux at high rates from video analysis.

Additional details

Created:
August 22, 2023
Modified:
October 19, 2023