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dc.contributor.authorTous Liesa, Rubén
dc.contributor.authorAlvarado Bermúdez, Leonardo
dc.contributor.authorOtero Calviño, Beatriz
dc.contributor.authorCruz de la Cruz, Stalin Leonel
dc.contributor.authorRojas Ulacio, Otilio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2021-02-03T09:56:09Z
dc.date.available2021-02-03T09:56:09Z
dc.date.issued2020-10-01
dc.identifier.citationTous, R. [et al.]. Deep neural networks for earthquake detection and source region estimation in north-central Venezuela. "Bulletin of the Seismological Society of America", 1 Octubre 2020, vol. 110, núm. 5, p. 2519-2529.
dc.identifier.issn0037-1106
dc.identifier.urihttp://hdl.handle.net/2117/336778
dc.descriptionPublicat en accés obert després de sis mesos d'embargament amb el permís de l'editor / Published in open access after six months of embargo with the permission of the publisher.
dc.description.abstractReliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC‐UCV (Universitat Politecnica de Catalunya ‐ Universidad Central de Venezuela), over single‐station three‐channel signal windows for P‐wave earthquake detection and source region estimation in north‐central Venezuela. The analysis is performed on a new dataset of handpicked arrivals of P waves from local events, named CARABOBO, built and made public for reproducibility and benchmarking purposes. The CARABOBO dataset consists of three‐channel continuous data recorded by the broadband stations of the Venezuelan Foundation for Seismological Research in the region of 9.5°–11.5°N and 67.0°–69.0°W during the time period from April 2018 to April 2019. During this period, 949 earthquakes were recorded in that area, corresponding to earthquakes with magnitudes in the range from Mw 1.1 to 5.2. To estimate the epicentral source region of a detected event, the proposed network employs geographical distribution of the CARABOBO dataset into K clusters as a basis. This geographical partitioning is automatically performed by the k‐means algorithm, and the optimality of the K‐values for our dataset has been assessed using the elbow (⁠K=5⁠) and silhouette (⁠K=3⁠) methods. For target seismicity, the proposed network achieves 95.27% detection accuracy and 93.36% source region estimation accuracy, when using K=5 geographic clusters. The location accuracy slightly increases to 95.68% in the case of K=3 geographic partitions. The detection capability of this network has also been tested on the OKLAHOMA dataset, which compiles more than 2000 local earthquakes that occurred in this U.S. state. Without any modification, the proposed network yields excellent detection results when trained and evaluated on that dataset (98.21% accuracy; ConvNetQuake, fine‐tuned for this dataset, achieves a 97.32% accuracy), corresponding to a totally different geographical region.
dc.description.sponsorshipThis work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P, by the Spanish Ministry of Science and Innovation under contract PID2019-107255GB-C22, and by the SGR programmes (2017-SGR-1414 and 2017-SGR-962) of the Catalan Government. We also thank the Generalitat de Catalunya for providing additional funds under the RIS3CAT DRAC project (001-P-001723). The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the ChEESE project, grant agreement No. 823844. Additionally, this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777778 MATHROCKS.
dc.format.extent11 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshEarthquakes
dc.subject.lcshNeural networks (Computer science)
dc.titleDeep neural networks for earthquake detection and source region estimation in north-central Venezuela
dc.typeArticle
dc.subject.lemacTerratrèmols
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.contributor.groupUniversitat Politècnica de Catalunya. VIRTUOS - Virtualisation and Operating Systems
dc.identifier.doi10.1785/0120190172
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://doi.org/10.1785/0120190172
dc.rights.accessOpen Access
local.identifier.drac28685830
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/777778/EU/Multiscale Inversion of Porous Rock Physics using High-Performance Simulators: Bridging the Gap between Mathematics and Geophysics/MATHROCKS
dc.relation.projectidinfo:eu-repo/grantAgreement/GENCAT/RIS3CAT/IU16-011643 VIRTUOS P6
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/2017 SGR 1414
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
local.citation.authorTous, R.; Alvarado, L.; Otero, B.; Cruz, S.; Rojas, O.
local.citation.publicationNameBulletin of the Seismological Society of America
local.citation.volume110
local.citation.number5
local.citation.startingPage2519
local.citation.endingPage2529


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