Application of Artificial Neural Networks for the classification of the seismic transients at Soufrière Hills volcano, Montserrat

Details Application of Artificial Neural Networks for the classification of the seismic transients at Soufrière Hills volcano, Montserrat Application of Artificial Neural Networks for the classification of the seismic transients at Soufrière Hills volcano, Montserrat

Nov 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003georl..30usde5l&link_type=abstract

Geophysical Research Letters, Volume 30, Issue 21, pp. SDE 5-1, CiteID 2090, DOI 10.1029/2003GL018082

Computer Science

Performance

5

Seismology: Volcano Seismology (8419), Volcanology: Eruption Monitoring (7280), Volcanology: General Or Miscellaneous

Scientific paper

Seismic activity at Soufrière Hills volcano is characterized by a variety of transients, such as tectonic earthquakes, long-period events, hybrid events, and rockfalls. The huge quantity of seismic data daily recorded on the volcano makes the application of automatic processing highly recommendable. We propose a method of supervised classification of the transients based on Artificial Neural Networks (ANN), which may be useful for processing the large data sets piled up in the past. Particularly, data sets recorded before the climactic eruptions from 1995 to 2002 may allow us to reconstruct the distribution of the different classes of seismic transients in time. We believe that this analysis may give useful insights into impending eruptive scenarios. The good performance of the ANN with 70% of transients correctly classified in a test set of 156 data, along with the opportunity to revise the misfits, make ANN a powerful tool for data processing.

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