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Automatic detection of wet-snow avalanche seismic signals

Author

Listed:
  • Conny Hammer

    (ETH Zürich)

  • Donat Fäh

    (ETH Zürich)

  • Matthias Ohrnberger

    (University of Potsdam)

Abstract

Avalanche activity is an important factor when estimating the regional avalanche danger. Moreover, a complete and detailed picture of avalanche activity is needed to understand the processes that lead to natural avalanche release. Currently, information on avalanche activity is mainly obtained through visual observations. However, this involves large uncertainties in the number and release times, influencing the subsequent analysis. Therefore, alternative methods for the remote detection of snow avalanches in particular in non-observed areas are highly desirable. In this study, we use the excited ground vibration to identify avalanches automatically. The specific seismic signature of avalanches facilitates the objective detection by a recently developed classification procedure. A probabilistic description of the signals, called hidden Markov models, allows the robust identification of corresponding signals in the continuous data stream. The procedure is based upon learning a general background model from continuous seismic data. Then, a single reference waveform is used to update an event-specific classifier. Thus, a minimum amount of training data is required by constructing such a classifier on the fly. In this study, we processed five days of continuous data recorded in the Swiss Alps during the avalanche winter 1999. With the restriction of testing large wet-snow avalanches only, the presented approach achieved very convincing results. We successfully detect avalanches over a large volume and distance range. Ninety-two percentage of all detections (43 out of 47) could be confirmed as avalanche events; only four false alarms are reported. We see a clear dependence of recognition capability on run-out distance and source–receiver distance of the observed events: Avalanches are detectable up to a source-receiver distance of eight times the avalanche length. Implications for analyzing a more comprehensive data set (smaller events and different flow regimes) are discussed in detail.

Suggested Citation

  • Conny Hammer & Donat Fäh & Matthias Ohrnberger, 2017. "Automatic detection of wet-snow avalanche seismic signals," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(2), pages 601-618, March.
  • Handle: RePEc:spr:nathaz:v:86:y:2017:i:2:d:10.1007_s11069-016-2707-0
    DOI: 10.1007/s11069-016-2707-0
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    References listed on IDEAS

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    1. M. Arattano, 1999. "On the Use of Seismic Detectors as Monitoring and Warning Systems for Debris Flows," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 197-213, November.
    2. Martin Laternser & Martin Schneebeli, 2002. "Temporal Trend and Spatial Distribution of Avalanche Activity during the Last 50 Years in Switzerland," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 27(3), pages 201-230, November.
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