Articles | Volume 21, issue 1
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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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,, 2011. a
Allen, R.: Automatic phase pickers: Their present use and future prospects, Bull. Seismol. Soc. Am., 72, S225–S242, 1982. a
Allen, S. and Huggel, C.: Extremely warm temperatures as a potential cause of recent high mountain rockfall, Global Planet. Change, 107, 59—9,, 2013. a
Allstadt, K.: Extracting source characteristics and dynamics of the August 2010 Mount Meager landslide from broadband seismograms, J. Geophys. Res.-Earth, 118, 1472–1490,, 2013. a, b
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,, 2018. a
Short summary
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.
Final-revised paper