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A support vector machine seismic detector for early-warning applications

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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.accessioned2014-07-17T12:28:08Z
dc.date.available2014-07-17T12:28:08Z
dc.date.issued2013
dc.date.updated2014-07-16T14:55:35Z
dc.description.abstractThis paper extends a Support Vector Machine (SVM) approach for the detection of seismic events, at the level of a seismic station. In previous works, it was shown that this approach produced excellent results, in terms of the Recall and Specificity measures, whether applied off-line or in a continuous scheme. The drawback was the time taken for achieving the detection, too large to be applied in a Early-Warning System (EWS). This paper shows that, by using alternative input features, a similar performance can be obtained, with a significant reduction in detection time. Additionally, it is 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.A Support Vector Machine Seismic Detector for Early-Warning Applications, Trabalho apresentado em Intelligent Control and Automation Science, In 3rd IFAC International Conference on Intelligent Control and Automation Science (2013), Chengdu, 2013.por
dc.identifier.doihttp://dx.doi.org/ 10.3182/20130902-3-CN-3020.00082
dc.identifier.issn9783902823458
dc.identifier.otherAUT: ARU00698; MRU00118;
dc.identifier.urihttp://hdl.handle.net/10400.1/4780
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevier, IFACpor
dc.relation.publisherversionhttp://www.ifac-papersonline.net/Detailed/63491.htmlpor
dc.subjectSmart systemspor
dc.subjectActuators and distributed systemspor
dc.subjectComputational intelligence methods in modelingpor
dc.subjectSystems identification and controlpor
dc.titleA support vector machine seismic detector for early-warning applicationspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceChengdupor
oaire.citation.endPage410por
oaire.citation.issue1por
oaire.citation.startPage405por
oaire.citation.titleIntelligent Control and Automation Sciencepor
oaire.citation.volume3por
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.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublicationdd2ad4e5-427f-468c-a272-688fae19ce52
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscoverydd2ad4e5-427f-468c-a272-688fae19ce52

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