Abstract
According to the idea now widespread that macroseismic intensity should be expressed in probabilistic terms, a beta-binomial model has been proposed in the literature to estimate the probability of the intensity at site in the Bayesian framework and a clustering procedure has been adopted to define learning sets of macroseismic fields required to assign prior distributions of the model parameters. This article presents the results concerning the learning sets obtained by exploiting the large Italian macroseismic database DBM1I11 (Locati et al. in DBMI11, the 2011 version of the Italian Macroseismic Database, 2011. http://emidius.mi.ingv.it/DBMI11/) and discusses the problems related to their use in probabilistic modelling of the attenuation in seismic regions of the European countries partners of the UPStrat-MAFA project (2012), namely South Iceland, Portugal, SE Spain and Mt Etna volcano area (Italy). Anisotropy and the presence of offshore earthquakes are some of the problems faced. All the work has been carried out in the framework of the Task B of the project.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agostinelli C, Rotondi R (2015) Analysis of macroseismic fields using statistical data depth functions: considerations leading to attenuation probabilistic modelling. Bull Earthq Eng (this volume)
Azzaro R, D’Amico S, Rotondi R, Tuvè T, Zonno G (2013) Forecasting seismic scenarios on Etna volcano (Italy) through probabilistic intensity attenuation models: a Bayesian approach. J Volcanol Geotherm Res 251:149–157
Breiman L, Friedman JH, Olshen RA, Stone CJ (1993) Classification and regression trees. Chapman Hall, New York
Kaufman L, Rousseeuw PJ (1990) Finding groups in data. Wiley, New York
Locati M, Camassi R, Stucchi M (eds) (2011) DBMI11, the 2011 version of the Italian Macroseismic Database. http://emidius.mi.ingv.it/DBMI11/. Milano, Bologna
Magri L, Mucciarelli M, Albarello D (1994) Estimates of site seismicity rates using ill-defined macroseismic data. Pure Appl Geophys 143(4):617–632
Meletti C, Patacca E, Scandone P (2000) Construction of a seismotectonic model: the case of Italy. Pageoph. 157:11–35. Zonation ZS.4 available from http://emidius.mi.ingv.it/GNDT/P511/home.html
Meroni F, Petrini V, Rotondi R, Zonno G (1991) Expected damage for alternative seismic hazard evaluations. In: Proceedings of 4th ICSZ, vol 2. Standford, California, pp 801–818
Pizzo B, Fabietti V (2013) Environmental risk prevention, post-seismic interventions and the reconstruction of the public space as a planning challenge. Italian Journal of Planning Practice 3(1):1–8
R Development Core Team (2008) R: A language and environment for statistical computing. http://www.r-project.org. Vienna, Austria
Read TRC, Cressie NAC (1988) Goodness-of-fit statistics for discrete multivariate data. Springer, New York
Rotondi R, Brambilla C, Varini E, Zonno G (2014) Task B—Probabilistic analysis of macroseismic data for forecast damage scenarios. OA Earth-prints Repository. available from http://hdl.handle.net/2122/9143
Rotondi R, Zonno G (2004) Bayesian analysis of a probability distribution for local intensity attenuation. Ann Geophys 47(5):1521–1540
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Stucchi M, Camassi R, Rovida A, Locati M, Ercolani E, Meletti C, Migliavacca P, Bernardini F, Azzaro R (2007) DBMI04, il database delle osservazioni macrosismiche dei terremoti italiani utilizzate per la compilazione del catalogo parametrico CPTI04. Quaderni di Geofisica 49:1–38. http://emidius.mi.ingv.it/DBMI04/
Tsapanos TM, Galanis O, Koravos G, Musson RMW (2002) A method for Bayesian estimation of the probability of local intensity for some cities in Japan. Ann Geophys 45(5):657–671
Urban Disaster Prevention Strategies using MAcroseismic Fields and FAult Sources (UPStrat-MAFA) - EU Project. Num. 23031/2011/613486/SUB/A5), DG ECHO Unit A5. http://upstrat-mafa.ov.ingv.it/UPStrat/, (2012)
Winkler RL (1996) Scoring rules and the evaluation of probabilities. Test 5(1):1–60
Zonno G, Rotondi R, Brambilla C (2009) Mining macroseismic fields to estimate the probability distribution of the intensity at site. Bull Seismol Soc Am 99(5):2876–2892
Acknowledgments
The authors thank the two reviewers for very useful and constructive suggestions and comments, which appreciably improved the paper. This study was co-financed by the EU - Civil Protection Financial Instrument, in the framework of the European project Urban disaster Prevention Strategies using MAcroseismic fields and FAult sources (Acronym: UPStrat-MAFA, Grant Agreement N. 23031/2011/613486/SUB/A5)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rotondi, R., Varini, E. & Brambilla, C. Probabilistic modelling of macroseismic attenuation and forecast of damage scenarios. Bull Earthquake Eng 14, 1777–1796 (2016). https://doi.org/10.1007/s10518-015-9781-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10518-015-9781-7