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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 1 | Copyright
Nat. Hazards Earth Syst. Sci., 18, 383-396, 2018
https://doi.org/10.5194/nhess-18-383-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Jan 2018

Research article | 25 Jan 2018

Automatic detection of snow avalanches in continuous seismic data using hidden Markov models

Matthias Heck1, Conny Hammer2, Alec van Herwijnen1, Jürg Schweizer1, and Donat Fäh2 Matthias Heck et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Swiss Seismological Service (SED), ETH Zurich, Zurich, Switzerland

Abstract. Snow avalanches generate seismic signals as many other mass movements. Detection of avalanches by seismic monitoring is highly relevant to assess avalanche danger. In contrast to other seismic events, signals generated by avalanches do not have a characteristic first arrival nor is it possible to detect different wave phases. In addition, the moving source character of avalanches increases the intricacy of the signals. Although it is possible to visually detect seismic signals produced by avalanches, reliable automatic detection methods for all types of avalanches do not exist yet. We therefore evaluate whether hidden Markov models (HMMs) are suitable for the automatic detection of avalanches in continuous seismic data. We analyzed data recorded during the winter season 2010 by a seismic array deployed in an avalanche starting zone above Davos, Switzerland. We re-evaluated a reference catalogue containing 385 events by grouping the events in seven probability classes. Since most of the data consist of noise, we first applied a simple amplitude threshold to reduce the amount of data. As first classification results were unsatisfying, we analyzed the temporal behavior of the seismic signals for the whole data set and found that there is a high variability in the seismic signals. We therefore applied further post-processing steps to reduce the number of false alarms by defining a minimal duration for the detected event, implementing a voting-based approach and analyzing the coherence of the detected events. We obtained the best classification results for events detected by at least five sensors and with a minimal duration of 12 s. These processing steps allowed identifying two periods of high avalanche activity, suggesting that HMMs are suitable for the automatic detection of avalanches in seismic data. However, our results also showed that more sensitive sensors and more appropriate sensor locations are needed to improve the signal-to-noise ratio of the signals and therefore the classification.

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In this study we use hidden Markov models, a machine learning algorithm to automatically identify avalanche events in a continuous seismic data set recorded during the winter 2010. With additional post processing steps, we detected around 70 avalanche events. Although not every detection could be confirmed as an avalanche, we clearly identified the two main avalanche periods of the winter season 2010 in our classification results.
In this study we use hidden Markov models, a machine learning algorithm to automatically...
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