The classification of seismo-volcanic signals using Hidden Markov Models as applied to the Stromboli and Etna volcanoes

Abstract: The aim of this work is to apply the Hidden Markov Model (HMM) method to recognise seismic signals belonging to different active volcanoes. We use data obtained from two field surveys carried out in 1997 and 1999 at Stromboli and Etna, respectively. For Stromboli we used two types of seismic signals for recognition purposes: Strombolian explosions and background seismic noise whilst for Etna we used volcanic tremors and tremor bursts. We initially proceeded to visually identify the signals, and to segment the data to obtain a model for each event class. We then applied these models separately to each volcano dataset, finally combining both datasets as a test of the portability of the system. The method analyses the seismograms and compares the characteristics of the data to a number of pre-defined event classes. If a signal is present, the method detects its occurrence and produces a classification. We observed that, to obtain reliable results, a careful and adequate segmentation process is crucial and that each signal type must be represented by its own specific model. Once we had built this model, the success level of the system was high. The success rates obtained indicated that the method was fully able to detect, isolate, and identify signals from raw seismic data. These results imply that, once an adequate training process has been used, this method is particularly appropriate for work in real time, as well as concurrently with the data acquisition system. [Copyright &y& Elsevier]