Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2134
Título: On-line operation of an intelligent seismic detector
Autor: Madureira, G.
Ruano, A. E.
Ruano, M. Graca
Palavras-chave: Seismic detection
Neural networks
Support vector machines
Spectrogram
Data: 2013
Editora: Springer Berlin Heidelberg
Citação: Madureira, Guilherme; Ruano, António E.; Ruano, Maria Graça. On-Line Operation of an Intelligent Seismic Detector, In Soft Computing Applications, 531-542, ISBN: 978-3-642-33940-0. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Resumo: This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA / LTA ratio- based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network’s stations of the Institute of Meteorology of Portugal (IM). Records’ spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2134
ISBN: 978-3-642-33940-0
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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