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dc.contributor.authorLlácer Giner, David
dc.contributor.authorOtero Calviño, Beatriz
dc.contributor.authorTous Liesa, Rubén
dc.contributor.authorMonterrubio Velasco, Marisol
dc.contributor.authorCarrasco Jiménez, José
dc.contributor.authorRojas Ulacio, Otilio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.identifier.citationLlácer, D. [et al.]. Random forest parameterization for earthquake catalog generation. A: International Conference on Machine Learning, Optimization, and Data Science. "Machine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020: Siena, Italy, July 19-23, 2020: revised selected papers, part I". Berlín: Springer, 2020, p. 233-243. ISBN 978-3-030-64583-0. DOI 10.1007/978-3-030-64583-0_22.
dc.description.abstractAn earthquake is the vibration pattern of the Earth’s crust induced by the sliding of geological faults. They are usually recorded for later studies. However, strong earthquakes are rare, small-magnitude events may pass unnoticed and monitoring networks are limited in number and efficiency. Thus, earthquake catalog are incomplete and scarce, and researchers have developed simulators of such catalogs. In this work, we start from synthetic catalogs generated with the TREMOL-3D software. TREMOL-3D is a stochastic-based method to produce earthquake catalogs with different statistical patterns, depending on certain input parameters that mimics physical parameters. When an appropriate set of parameters are used, TREMOL-3D could generate synthetic catalogs with similar statistical properties observed in real catalogs. However, because of the size of the parameter space, a manual searching becomes unbearable. Therefore, aiming at increasing the efficiency of the parameter search, we here implement a Machine Learning approach based on Random Forest classification, for an automatic parameter screening. It has been implemented using the machine learning Python’s library SciKit Learn.
dc.description.sponsorshipThis work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P and by the Catalan Government through the programmes 2017-SGR-1414, 2017-SGR-962 and the RIS3CAT DRAC project 001-P-001723. Moreover, 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). 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.
dc.format.extent11 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.otherSynthetic catalogs
dc.subject.otherRandom forest
dc.titleRandom forest parameterization for earthquake catalog generation
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
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 (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/AGAUR/2017 SGR 1414
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/823844/EU/Centre of Excellence for Exascale in Solid Earth/ChEESE
local.citation.authorLlácer, D.; Otero, B.; Tous, R.; Monterrubio, M.; Carrasco, J.; Rojas, O.
local.citation.contributorInternational Conference on Machine Learning, Optimization, and Data Science
local.citation.publicationNameMachine Learning, Optimization, and Data Science, 6th International Conference, LOD 2020: Siena, Italy, July 19-23, 2020: revised selected papers, part I

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