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The classification of seismic signals of volcanic origin (SSVOs) is an important task in the context of monitoring active volcanoes. The number and the size of certain types of seismic events usually increase before periods of volcanic crisis and are a key index of forthcoming activity. However, the task of classifying SSVOs is in most cases still carried out manually during daily routine work. The implementation of an automatic classification system not only would allow the processing of large amounts of data in short time, but would also have the advantage of providing a consistent, objective and time-invariant classification.

Techniques for automatic detection and classification of seismic events have been of great interest to the seismological community since the introduction of digital seismic monitoring. Nowadays, automatic detection or picking of impulsive transients in earthquake seismic signals can be efficiently achieved by short-time-average to longtime-average ratio (STA/LTA) trigger algorithms (Bormann et al. 2002; Trnkoczy 2002). Moreover, some algorithms have also been proposed for the automatic classification of earthquake seismic signals (Joswig 1996; Gendron et al. 2000).

However, when working with SSVOs additional problems are encountered. From the signal analysis point of view, SSVOs can be very different from earthquake seismic signals. As already noted (e.g. by Wassermann 2002), they vary from earthquakelike transients to long-lasting and continuous tremor signals. Moreover, the signal-to-noise ratio of SSVOs is usually rather low. For these reasons, the automatic classification of SSVOs is still an open question.

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