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Alternative heavy tailed models in seismology

2020-06Conference paper Open access
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datacite.subject.fosCiências Naturais::Ciências Físicas
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg12:Produção e Consumo Sustentáveis
dc.contributor.authorFelgueiras, Miguel
dc.contributor.authorMartins, João
dc.contributor.authorSantos, Rui
dc.date.accessioned2025-07-30T10:14:52Z
dc.date.available2025-07-30T10:14:52Z
dc.date.issued2020-06
dc.descriptionArticle number - 012002; Conference name - 4th International Conference on Mathematical Methods and Computational Techniques in Science and Engineering, MMCTSE 2020; Conference date - 22 February 2020 - 24 February 2020; Conference code - 161567
dc.description.abstractGreat earthquakes are commonly considered as the ones with moment magnitude (Mw ) above or equal to 8.0. Since these earthquakes can destroy entire communities located near the epicentre, the search of physical laws that explain the energy released by them is an important issue. There is a connection between the radiated energy of an earthquake, its magnitude and its seismic moment (M 0). Thence, when fitting a heavy or an extremely heavy tailed distribution to a seismic moment dataset, we are in fact adjusting a mathematical model which explains the amount of energy released by these great seisms. Therefore, the main goal of this work is to study the more appropriated Pareto based models (the most used family in this field) when explaining the seismic moment of the great earthquakes. With this purpose in mind, we selected two different catalogs that accommodate recent events and are considered more accurate than other catalogs used in previous works. We conclude that the traditional Pareto distribution remains a good choice to deal with this kind of data, but Log-Pareto lead to higher p-values and Location-scale Pareto is better fitted to the biggest events.eng
dc.description.sponsorshipFunded by FCT - Fundação para a Ciência e a Tecnologia through the project UIDB/00006/2020.
dc.identifier.citationMiguel Felgueiras et al 2020 J. Phys.: Conf. Ser. 1564 012002. DOI: https://doi.org/10.1088/1742-6596/1564/1/012002.
dc.identifier.doi10.1088/1742-6596/1564/1/012002
dc.identifier.eissn1742-6596
dc.identifier.issn1742-6588
dc.identifier.urihttp://hdl.handle.net/10400.8/13787
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIOP Publishing
dc.relationCentre of Statistics and its Applications
dc.relation.hasversionhttps://iopscience.iop.org/article/10.1088/1742-6596/1564/1/012002
dc.relation.ispartofJournal of Physics: Conference Series
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPareto principle
dc.subjectGreat earthquake
dc.subjectHeavy-tailed
dc.subjectHeavy-tailed distribution
dc.subjectMoment magnitudes
dc.subjectPareto distributions
dc.subjectPhysical laws
dc.subjectRadiated energies
dc.subjectSeismic moment
dc.titleAlternative heavy tailed models in seismologyeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT
oaire.citation.conferenceDate2020-02
oaire.citation.conferencePlaceLondon, England
oaire.citation.endPage9
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleJournal of Physics: Conference Series
oaire.citation.volume1564
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFelgueiras
person.familyNameOliveira Martins
person.familyNameSantos
person.givenNameMiguel
person.givenNameJoão Paulo
person.givenNameRui
person.identifier1051057
person.identifier.ciencia-id7610-8B27-8044
person.identifier.orcid0000-0001-5450-7374
person.identifier.orcid0000-0002-0474-1397
person.identifier.orcid0000-0002-7371-363X
person.identifier.ridC-1873-2015
person.identifier.scopus-author-id56979441000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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Great earthquakes are commonly considered as the ones with moment magnitude (Mw ) above or equal to 8.0. Since these earthquakes can destroy entire communities located near the epicentre, the search of physical laws that explain the energy released by them is an important issue. There is a connection between the radiated energy of an earthquake, its magnitude and its seismic moment (M 0). Thence, when fitting a heavy or an extremely heavy tailed distribution to a seismic moment dataset, we are in fact adjusting a mathematical model which explains the amount of energy released by these great seisms. Therefore, the main goal of this work is to study the more appropriated Pareto based models (the most used family in this field) when explaining the seismic moment of the great earthquakes. With this purpose in mind, we selected two different catalogs that accommodate recent events and are considered more accurate than other catalogs used in previous works. We conclude that the traditional Pareto distribution remains a good choice to deal with this kind of data, but Log-Pareto lead to higher p-values and Location-scale Pareto is better fitted to the biggest events.
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