Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/16597
Authors: Oliveti, Ilaria* 
Faenza, Licia* 
Antonucci, Andrea* 
Locati, Mario* 
Rovida, Andrea* 
Michelini, Alberto* 
Title: The ShakeMap Atlas of Historical Earthquakes in Italy: Configuration and Validation
Journal: Seismological Research Letters 
Publisher: SEISMOLOGICAL SOCIETY OF AMERICA
Issue Date: 6-Sep-2023
DOI: 10.1785/0220230138
Subject Classification04.06. Seismology
Abstract: Italy has a long tradition of studies on the seismic history of the country and the neighboring areas. Several archives and databases dealing with historical earthquake data—primarily intensity data points—have been published and are constantly updated. Macroseismic fields of significant events are of foremost importance in assessing earthquake effects and for the evaluation of seismic hazards. Here, we adopt the U.S. Geological Survey (USGS)‐ShakeMap software to calculate the maps of strong ground shaking (shakemaps) of 79 historical earthquakes with magnitude ≥6 that have occurred in Italy between 1117 and 1968 C.E. We use the macroseismic data published in the Italian Macroseismic Database (DBMI15). The shakemaps have been determined using two different configurations. The first adopts the virtual intensity prediction equations approach (VIPE; i.e., a combination of ground‐motion models [GMMs] and ground‐motion intensity conversion equations [GMICEs]; Bindi, Pacor, et al., 2011; Oliveti et al., 2022b). The second exploits the intensity prediction equations (IPE; Pasolini, Albarello, et al., 2008; Lolli et al., 2019). The VIPE configuration has been found to provide more accurate results after appraisal through a cross‐validation analysis and has been applied for the generation of the ShakeMap Atlas. The resulting maps are published in the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ShakeMap (see Data and Resources; Oliveti et al., 2023), and in the Italian Archive of Historical Earthquake Data (ASMI; see Data and Resources; Rovida et al., 2017) platforms.
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