Application of Artificial Neural Networks for the Classification of the Seismic Transients at Soufrière Hills Volcano, Montserrat
Document Type
Article
Publication Date
11-2003
Digital Object Identifier (DOI)
https://doi.org/10.1029/2003GL018082
Abstract
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.
Rights Information
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Geophysical Research Letters, v. 30, issue 21, art. 2090
Scholar Commons Citation
Langer, H.; Falsaperla, S.; and Thompson, Glenn, "Application of Artificial Neural Networks for the Classification of the Seismic Transients at Soufrière Hills Volcano, Montserrat" (2003). School of Geosciences Faculty and Staff Publications. 905.
https://digitalcommons.usf.edu/geo_facpub/905
