Operational earthquake loss forecasting: a retrospective analysis of some recent Italian seismic sequences
Articolo
Data di Pubblicazione:
2016
Citazione:
Operational earthquake loss forecasting: a retrospective analysis of some recent Italian seismic sequences / Chioccarelli, E., Iervolino, I.. - In: BULLETIN OF EARTHQUAKE ENGINEERING. - ISSN 1570-761X. - 14:9(2016), pp. 2607-2626. [10.1007/s10518-015-9837-8]
Abstract:
Operational earthquake forecasting (OEF) relies on real-time monitoring of seismic activity in an area of interest to provide constant (e.g., daily) updates of the expected number of events exceeding a certain magnitude threshold in a given time window (e.g., 1 week). It has been demonstrated that the rates from OEF can be used to estimate expected values of the seismic losses in the same time interval OEF refers to. This is a procedure recently defined as operationalearthquakelossforecasting (OELF), which may be the basis for rational short-term seismic risk assessment and management. In Italy, an experimental OELF system, named MANTIS-K, is currently under testing. It is based on weekly rates of earthquakes exceeding magnitude (M) 4, which are updated once a day or right after the occurrence in the country of an M 3.5+ earthquake. It also relies on large-scale structural vulnerability and exposure data, which serve to the system to provide continuously the weekly expected number of: (1) collapsed buildings, (2) displaced residents, and (3) casualties. While the probabilistic basis of MANTIS-K was described in previous work, in this study OELF is critically discussed with respect to three recent Italian seismic sequences. The aim is threefold: (1) illustrating all the features of the OELF system in place; (2) providing insights to evaluate whether if it would have been a useful additional tool for short-term management; (3) recognizing common features, if any, among the losses computed for different sequences.
Tipologia CRIS:
1.1 Articolo in rivista
Elenco autori:
Chioccarelli, Eugenio; Iervolino, Iunio
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