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Application of artificial neural networks to the classification of montserrat seismic transients

The classification of different kinds of seismic transients and the analysis of their distribution in time are important information to identify the sources involved in the generation of seismic signals and define their activation in relation with eruptive activity. We chose the Artificial Neural Networks (ANN) for the classification of seismic transients as they are automatic systems, which excel in pattern classification.

We followed progressive steps for our applications, starting with the evaluation of the most suitable form of data codification. Then, we proceeded with the selection of the seismic transients to analyse, the preparation of data sets for training and test, the critical revision of the results, and the re-analysis with enlarged data sets.

Our final applications of ANN have demonstrated that this automatic tool can achieve good results for the classification of seismic transients, overcoming the drawbacks of subjectivity of human operators. ANN also allow to analyse large data sets which could hardly be handle manually.

We successfully tested ANN topologies on data recorded at Soufrière Hills Volcano by the Montserrat Volcano Observatory.

ANN may be also of great help for other volcano observatories worldwide, as they can be implemented for analyses of past and on-line data.

Reported by

Istituto nazionale di geofisica e vulcanologia, Sezione Catania
P.zza Roma, 2
95123 Catania
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