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Authors: Akinci, Aybige* 
Moschetti, Morgan* 
Taroni, Matteo* 
Title: Ensemble Smoothed Seismicity Models for the New Italian Probabilistic Seismic Hazard Map
Issue Date: Jun-2018
Series/Report no.: /89 (2018)
DOI: 10.1785/0220180040
Abstract: We develop a long-term (a few decades or longer) earthquake rate forecast for Italy based on smoothed seismicity for incor- poration in the 2017–2018 Italian Probabilistic Seismic Haz- ard Maps (IPSHM). Because the earthquake rate models from previous IPSHM were computed using source zones that were drawn around seismicity and tectonic provinces, the present model will be the first introduction of the smoothed seismic- ity method into the IPSHM. Smoothed seismicity models are constructed from both historical CPTI15 (Catalogo Parame- trico dei Terremoti Italiani, 1000–2014) and instrumental (1981–2016) earthquake catalogs and use both fixed and adaptive smoothing methods. We compute spatial likelihood values comparing the spatial distribution of observed earth- quakes with a suite of trial earthquake rate models to optimize smoothing parameters and catalogs. Then we produce an en- semble model using two different smoothing models (adap- tive and fixed) and two earthquake catalogs (historical and instrumental), which are weighted equally through a logic- tree approach to improve the forecast capability. We also compare our optimized smoothed seismicity models with the best two models of the Italian Collaboratory for the Study of Earthquake Predictability (CSEP) experiment and retro- spectively test them with the CSEP methodology. We ob- served that the ensemble model performs slightly better than the optimized fixed and the adaptive smoothing seismic- ity models obtained in this study and the best time-indepen- dent model of the CSEP Italian experiment. The preferred ensemble model forecasts an annual rate of 1.47 M ≥ 5:0 earthquakes, with higher rates mainly concentrating along the Apennines chain, eastern Alps, Calabria, and northeast Sicily. Finally, six ensemble models are created from the differ- ent smoothing methods using different weights through a logic-tree approach to estimate the uncertainty associated with the model.
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