Identification of Infrasound Regimes at Mount Etna using Pattern Recognition Techniques

Mount Etna, Europe’s largest and most active volcano is situated close to the Metropolitan area of Catania with about 1 Million inhabitants. Continuous monitoring has therefore been carried out for decades. Among the various disciplines infrasound recordings play an important role in this context. Explosive activity near or above ground as well as shallow tremor processes are easier to identify with airborne sound waves than with seismic waves that are significantly scattered and refracted in the volcanic edifice. However, infrasound signals are often affected by noise, especially by wind noise in the summit area.

At Mount Etna five summit craters are currently known with fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns interfered by changing noise levels. Manual distinction of noisy data from real volcanogenic signals brings along a considerable effort and requires expert knowledge. We therefore apply unsupervised pattern recognition techniques for this task. Extracting features from the amplitude spectrum we are able to distinguish different infrasound regimes with Self-Organizing maps (SOMs). SOMs allow to color-code the results for an intuitive interpretation and evidence the presence of transitional activity regimes. We define a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible to train the SOM. This enables a straight forward interpretation of new data.