Debris flow velocity and volume estimations based on seismic data
Open Access
- 10 June 2022
- journal article
- research article
- Published by Copernicus GmbH in Natural Hazards and Earth System Sciences
- Vol. 22 (6), 1955-1968
- https://doi.org/10.5194/nhess-22-1955-2022
Abstract
The estimation of debris flow velocity and volume is a fundamental task for the development of early warning systems and the design of control structures and of other mitigation measures. Debris flow velocity can be calculated using seismic data recorded at two monitoring stations located along the channel, and previous analysis of the seismic energy produced by debris flows showed that the peak discharge of each surge can be estimated based on the maximum amplitude of the seismic signal. This work provides a first approach for estimating the total volume of debris flows from the integrated seismic energy detected with simple, low-cost geophones installed along a debris flow channel. The developed methods were applied to seismic data collected from 2014 to 2018 in three different test sites in the European Alps: Gadria and Cancia (Italy) and Lattenbach (Austria). An adaptable cross-correlation time window was used to calculate the velocity of the different surges, which can offer a better estimation of the velocity compared to a constant window length. The analyses of the seismic data of 14 debris flows show the strong control of the sampling rate and of the inter-station distance on velocity estimation. A linear relationship between the squares of seismic amplitudes – a proxy for seismic energy – and independent measurements of the debris flow volume is proposed for a first-order estimation of the latter. Uncertainties in the volume estimations are controlled by flow properties – such as liquid or viscous surges generating low-amplitude signals and thus underestimating the calculated volume – but in most cases (9 out of 11 events of the test dataset of the Illgraben basin, CH) the order of magnitude of the debris flow volume is correctly predicted.This publication has 32 references indexed in Scilit:
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