Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-339-2021
https://doi.org/10.5194/nhess-21-339-2021
Research article
 | 
27 Jan 2021
Research article |  | 27 Jan 2021

Near-real-time automated classification of seismic signals of slope failures with continuous random forests

Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter

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Revised manuscript accepted for NHESS
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Cited articles

Abellán, A., Vilaplana, J. M., Calvet, J., García-Sellés, D., and Asensio, E.: Rockfall monitoring by Terrestrial Laser Scanning – case study of the basaltic rock face at Castellfollit de la Roca (Catalonia, Spain), Nat. Hazards Earth Syst. Sci., 11, 829–841, https://doi.org/10.5194/nhess-11-829-2011, 2011. a
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Allstadt, K. E., Matoza, R. S., Lockhart, A. B., Moran, S. C., Caplan-Auerbach, J., Haney, M. M., Thelen, W. A., and Malone, S. D.: Seismic and acoustic signatures of surficial mass movements at volcanoes, J. Volcanol. Geoth. Res., 364, 76–106, https://doi.org/10.1016/j.jvolgeores.2018.09.007, 2018. a
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Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
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