Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2178
Título: A neural network seismic detector
Autor: Madureira, G.
Ruano, A. E.
Palavras-chave: Seismic detector
Neural networks
Support vector machines
Data: 2009
Citação: Ruano, Antonio Eduardo de Barros; Guilherme, Madureira,. A Neural Network Seismic Detector, Acta Technica Jaurinensis, 2, 2, 159-170, 2009.
Resumo: This experimental study focuses on a detection system at the seismic station level that should have a similar role to the detection algorithms based on the ratio STA/LTA. We tested two types of neural network: Multi-Layer Perceptrons and Support Vector Machines, trained in supervised mode. The universe of data consisted of 2903 patterns extracted from records of the PVAQ station, of the seismography network of the Institute of Meteorology of Portugal. The spectral characteristics of the records and its variation in time were reflected in the input patterns, consisting in a set of values of power spectral density in selected frequencies, extracted from a spectro gram calculated over a segment of record of pre-determined duration. The universe of data was divided, with about 60% for the training and the remainder reserved for testing and validation. To ensure that all patterns in the universe of 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. The best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favorably with the ones obtained by the existing detection system, 50%.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2178
ISSN: 1789-6932
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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