Automatic classification of segmented seismic recordings at the Nevado del Ruiz volcano, Columbia

The Nevado del Ruiz volcano is an active and dangerous volcano in the Andean volcanic belt. Measuring seismic activity is one of the most reliable and widely used techniques to monitor and predict renewed volcanic activity. Seismic activity can be caused by several different underlying physical processes. It is of interest to the earth-science observatories monitoring potentially dangerous volcanoes to determine the underlying cause of the registered earthquakes. Typically segmented seismic recordings are classified by hand often based upon their frequency contents. An automated system capable of discriminating reliably between several different seismic recording classes can potentially release the human expert from the labor intensive classification task. An Interesting question concerning the frequency representation of the segmented seismic recordings is: if it is better to use only frequency information in the form of a single spectrum or to use a time frequency representation such as a spectrogram. Furthermore it is of interest to see if the ordering of the spectral frames inside the resulting spectrograms is of importance. In this study a justified spectrogram representation is developed for the segmented recordings from the Nevado del Ruiz volcano. Using this spectrogram representation we also look at five different classification strategies in combination with a large number of different classifiers. Often seismic events such as volcanic tectonic earthquakes, tectonic earthquakes, rockfall etc... are registered by several seismic stations. It is of interest to see if the recordings of multiple stations can be combined to improve classification results. Furthermore it is of interest to see how well the untrained and trained classifier systems generalize to the recordings of other stations.

Subject

pattern recognition
seismic recordings

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master thesis

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