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Seismic detection using support vector machines

2014Journal article Restricted access
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dc.contributor.authorRuano, Antonio
dc.contributor.authorMadureira, G.
dc.contributor.authorBarros, O.
dc.contributor.authorKhosravani, Hamid Reza
dc.contributor.authorRuano, M. Graça
dc.contributor.authorFerreira, P. M.
dc.date.accessioned2014-07-17T12:40:14Z
dc.date.available2014-07-17T12:40:14Z
dc.date.issued2014
dc.date.updated2014-07-16T14:29:42Z
dc.description.abstractThis study describes research to design a seismic detection system to act at the level of a seismic station, providing a similar role to that of STA/LTA ratio-based detection algorithms. In a first step, Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), trained in supervised mode, were tested. The sample data consisted 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 input ANN 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. The proposed system best results, in terms of sensitivity and selectivity in the whole data ranged between 98% and 100%. These results compare very favourably with the ones obtained by the existing detection system, 50%, and with other approaches found in the literature. Subsequently, the system was tested 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 classifier presented 88.4% and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM. Due to the input features used, the average time taken for detection with this approach is in the order of 100 s. This is too long to be used in an early-warning system. In order to decrease this time, an alternative set of input features was tested. A similar performance was obtained, with a significant reduction in the average detection time (around 1.3 s). Additionally, it was experimentally proved that, whether off-line or in continuous operation, the best results are obtained when the SVM detector is trained with data originated from the respective seismic station.por
dc.identifier.citationRuano, A.E.; Madureira, G.; Barros, O.; Khosravani, H.R.; Ruano, M.G.; Ferreira, P.M.Seismic detection using support vector machines, Neurocomputing, 135, 5, 273-283, 2014.por
dc.identifier.doihttp://dx.doi.org/ 10.1016/j.neucom.2013.12.020
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2013.12.020
dc.identifier.issn0925-2312
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/4781
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0925231214000332por
dc.subjectSeismic detectionpor
dc.subjectNeural networkspor
dc.subjectSupport vector machinespor
dc.subjectEarly warning systemspor
dc.titleSeismic detection using support vector machinespor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage283por
oaire.citation.startPage273por
oaire.citation.titleNeurocomputingpor
oaire.citation.volume135por
person.familyNameRuano
person.familyNameKhosravani
person.familyNameRuano
person.givenNameAntonio
person.givenNameHamid Reza
person.givenNameMaria
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-6308-8666
person.identifier.orcid0000-0001-7273-5979
person.identifier.orcid0000-0002-0014-9257
person.identifier.ridB-4135-2008
person.identifier.ridA-8321-2011
person.identifier.scopus-author-id7004284159
person.identifier.scopus-author-id7004483805
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublicationdd2ad4e5-427f-468c-a272-688fae19ce52
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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