Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8579
AuthorsSelva, J.* 
Sandri, L.* 
TitleProbabilistic Seismic Hazard Assessment: Combining Cornell-like approaches and data at sites through Bayesian inference
Issue Date2013
Series/Report no.3/103 (2013)
DOI10.1785/0120120091
URIhttp://hdl.handle.net/2122/8579
KeywordsCornell-McGuire approach
site intensity
Bayesian inference
Subject Classification04. Solid Earth::04.06. Seismology::04.06.11. Seismic risk 
AbstractThe societal importance and implications of seismic hazard assessment forces the scientific community to pay an increasing attention to the evaluation of uncertainty, to provide accurate assessments. Probabilistic Seismic Hazard Assessment (PSHA) formally accounts for the natural variability of the involved phenomena, from seismic sources to wave propagation. Recently, an increasing attention is paid to the consequences that alternative modeling procedures have on hazard results. This uncertainty, essentially of epistemic nature, has been shown to have major impacts on PSHA results, leading to extensive applications of techniques like the Logic Tree. Here, we develop a formal Bayesian inference scheme for PSHA that allows, on one side, to explicitly account for all uncertainties and, on the other side, to consider a larger set of sources of information, from heterogeneous models to past data. This process decreases the chance of undesirable biases, and leads to a controlled increase of the precision of the probabilistic assessment. In addition, the proposed Bayesian scheme allows (i) the assignment of a ’subjective’ reliability to single models, without requirement of completeness or homogeneity, and (ii) a transparent and uniform evaluation of the ’strength’ of each piece of information used on the final results. The applicability of the method is demonstrated through the assessment of seismic hazard in the Emilia-Romagna region (Northern Italy), in which the results of a traditional Cornell-McGuire hazard model based on a Logic Tree are locally updated with the historical macroseismic records, to provide a unified assessment that accounts for both sources of information.
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