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.
Large-vocabulary speaker-independent continuous speech recognition with semi-continuous hidden Markov models