Repository logo
 
Publication

On-line operation of an intelligent seismic detector

2013-01-30Conference object Restricted access
Simple item page
dc.contributor.authorMadureira, G.
dc.contributor.authorRuano, Antonio
dc.contributor.authorRuano, M. Graça
dc.date.accessioned2015-04-13T14:53:37Z
dc.date.available2015-04-13T14:53:37Z
dc.date.issued2013-01-30
dc.description.abstractThis 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. © 2013 Springer-Verlag Berlin Heidelberg.
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-642-33941-7-47
dc.identifier.issn2194-5357
dc.identifier.otherAUT: ARU00698; MRU00118;
dc.identifier.urihttp://hdl.handle.net/10400.1/5885
dc.language.isoeng
dc.peerreviewedyes
dc.peerreviewedyes
dc.subjectNeural networks
dc.subjectSeismic detection
dc.subjectSpectrogram
dc.subjectSupport vector machines
dc.titleOn-line operation of an intelligent seismic detector
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage542
oaire.citation.startPage531
oaire.citation.titleAdvances in Intelligent Systems and Computing
oaire.citation.volume195 AISC
person.familyNameRuano
person.familyNameRuano
person.givenNameAntonio
person.givenNameMaria
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-6308-8666
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.rightsrestrictedAccess
rcaap.typeconferenceObject
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2013_Ballas et al_Madureira.pdf
Size:
634.62 KB
Format:
Adobe Portable Document Format
Download

Collections

FCT2-Artigos (em revistas ou actas indexadas)