The availability of the new computing techniques allows to perform
advanced analysis in near real time, improving the seismological monitoring
systems, which can extract more significant information from the raw data in a
really short time. However, the correct identification of the events remains a
critical aspect for the reliability of near real time automatic analysis. We
approach this problem by using Neural Networks (NN) for discriminating
among the seismic signals recorded in the Neapolitan volcanic area (Vesuvius,
Phlegraean Fields). The proposed neural techniques have been also applied to
other sets of seismic data recorded in Stromboli volcano. The obtained results
are very encouraging, giving 100% of correct classification for some transient
signals recorded at Vesuvius and allowing the clustering of the large dataset of
VLP events recorded at Stromboli volcano
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