Mining Macroseismic Fields to Estimate the Probability Distribution of the Intensity at Site (Articolo in rivista)

Type
Label
  • Mining Macroseismic Fields to Estimate the Probability Distribution of the Intensity at Site (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1785/0120090042 (literal)
Alternative label
  • Zonno G.; Rotondi R.; Brambilla C. (2009)
    Mining Macroseismic Fields to Estimate the Probability Distribution of the Intensity at Site
    in Bulletin of the Seismological Society of America; Seismological Society of America, El Cerrito (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Zonno G.; Rotondi R.; Brambilla C. (literal)
Pagina inizio
  • 2876 (literal)
Pagina fine
  • 2892 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 99 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 17 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 5 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • INGV, Milano; IMATI-CNR, Milano; IMATI-CNR, Milano (literal)
Titolo
  • Mining Macroseismic Fields to Estimate the Probability Distribution of the Intensity at Site (literal)
Abstract
  • The analysis of the seismic attenuation is a prominent and problematic component of hazard assessment. Over the last decade it has become increasingly clear that the intrinsic uncertainty of the decay process must be expressed in probabilistic terms. This implies estimating the probability distribution of the intensity at a site as the combination of the distribution of the intensity decay and of the distribution of the epicentral macroseismic intensity found for the area surrounding that site. We focus here on the estimation of the distribution of the intensity decay. Previous studies presented in the literature show that the intensity decay in Italian territory varies greatly from one region to another and depends on many factors, some of them not easily measurable. Assuming that the decay shows a similar behavior in function of the epicenter-site distance when the same geophysical conditions and building vulnerability characterize different macroseismic fields, we have classified some macroseismic fields drawn from the Italian felt report database by applying a clustering algorithm. Earthquakes in the same class constitute the input of a two-step procedure for the Bayesian estimation of the probability distribution of the intensity decay at any distance from the epicenter, conditioned on the epicentral macroseismic intensity, where the intensity decay is considered an integer, random variable, following a binomial distribution. The scenario generated by a future earthquake is forecast either by the predictive distribution in each distance bin or by a binomial distribution whose parameter is a continuous function of the distance. The estimated distributions have been applied to forecast the scenario actually produced by the Colfiorito earthquake on 26 September 1997; for both options the expected and observed intensities have been compared on the basis of some validation criteria. The same procedure has been repeated using the probability distribution of the intensity decay estimated on the basis of each class of macroseismic fields identified by the clustering algorithm. (literal)
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