EP51D-06
Bedload sediment transport embedded in seismic signals generated from a mountain stream

Friday, 18 December 2015: 09:15
2005 (Moscone West)
Danica L Roth1, Emily E Brodsky1, Noah J Finnegan2, Jens Martin Turowski3, Carlos R Wyss4, Alexandre Badoux5 and Johannes Martin Schneider6, (1)University of California Santa Cruz, Santa Cruz, CA, United States, (2)University of California Santa Barbara, Santa Barbara, CA, United States, (3)GFZ German Research Centre for Geosciences, Potsdam, Germany, (4)ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland, (5)WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland, (6)ETH Swiss Federal Institute of Technology Zurich, Department of Environmental Systems Sciences, Zurich, Switzerland
Abstract:
We examine broadband (15 – 450 Hz) seismic data from the well-studied Erlenbach stream in the Swiss Prealps, where discharge, precipitation, and bedload transport are independently constrained during flood events. We perform a general linear least squares inversion of seismic data, exploiting times with isolated discharge or rain events, to identify the spectral signals of water turbulence and raindrop impacts. This, in turn, allows us to remove the contributions of turbulence and rainfall from the seismic spectra, thereby isolating the signal of bedload transport. We use one storm to calibrate the regression for bedload transport, then use this regression along with precipitation and discharge data to predict bedload transport rates for the remainder of the two-month campaign.

Our predicted bedload transport rates correlate well with transport rates from calibrated geophones embedded in the channel (r2~0.6, p<<10-5). We also find that rain generates a roughly broadband seismic spectrum, while water turbulence and sediment transport exhibit power primarily in lower frequency bands (<100 Hz), due in part to different attenuation path lengths. We use the varying attenuation of water turbulence and bedload transport spectra to infer the locations of the primary sources generating each signal: a downstream waterfall, and the channel bed near the seismometers, respectively. Our results indicate that deconstruction of seismic signals from rivers can provide insight into the component signals generated by water turbulence, rain, and sediment transport. Further, we find that regression of seismic spectra with precipitation, discharge and bedload transport data for a single calibration period enables the estimation of transport for subsequent periods with only precipitation, discharge and seismic data. Since precipitation and discharge are significantly easier to monitor than transport, this represents a potential application for in-field monitoring of bedload activity.

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