Show simple item record

dc.contributor.authorRojas, Otilio
dc.contributor.authorOtero Calviño, Beatriz
dc.contributor.authorAlvarado, Leonardo
dc.contributor.authorMus, Sergi
dc.contributor.authorTous Liesa, Rubén
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.identifier.citationRojas, O. [et al.]. Artificial neural networks as emerging tools for earthquake detection. "Computación y sistemas", 2019, vol. 23, núm. 2, p. 335-350.
dc.description.abstractAs seismic networks continue to spread and monitoring sensors become more ef¿cient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identi¿cation, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recentadvancesarerelatedtotheapplicationofArti¿cial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.
dc.format.extent16 p.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Enginyeria civil::Geologia::Riscos geològics
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshSeismic waves
dc.subject.otherP and S seismic waves
dc.subject.otherEarthquake hypocenters
dc.subject.otherUnsupervised and semisupervised
dc.subject.otherDeep and convolutional neural networks
dc.subject.otherTraining and testing data sets.
dc.titleArtificial neural networks as emerging tools for earthquake detection
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacOnes sísmiques
dc.contributor.groupUniversitat Politècnica de Catalunya. VIRTUOS - Virtualisation and Operating Systems
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
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/AGAUR/PRI2010-2013/2014 SGR 1051
local.citation.authorRojas, O.; Otero, B.; Alvarado, L.; Mus, S.; Tous, R.
local.citation.publicationNameComputación y sistemas

Files in this item


This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain