GNGTS 2017 - 36° Convegno Nazionale

304 GNGTS 2017 S essione 2.1 the coastline is estimated by applying empirical amplification factors (Glimsdal et al. , 2017). All the calculations are performed through the hazard calculation platform developed at INGV in collaboration with GFZ. The results of the application consist of: i) two sensitivity analyses (on some alternative SPTHA formulations, and on the separation between BS and PS); ii) SPTHA results along selected coastlines for an exposure time of 50 yr, along with disaggregation analyses (Fig. 1), and relative uncertainty. This methodology was initially designed during the ASTARTE project and has been later applied for the regional-scale SPTHA in the TSUMAPS-NEAM project. Acknowledgements We acknowledge financial support by the EC FP7 ASTARTE (Grant agreement 603839). The TSUMAPS-NEAM Project (http://www.tsumaps-neam.eu/) is co-financed by the European Union Civil Protection Mechanism, Agreement Number: ECHO/SUB/2015/718568/PREV26. References Glimsdal S, Lovholt F, Harbitz C, Orefice S, Romano F, Brizuela B, Lorito S, Hoechner A, Babeyko A (2017) Development of Local Amplification Factors in the NEAM Region for Production of Regional Tsunami Hazard Maps, in Geophysical Research Abstracts, Vol. 19, EGU2017-7657-1.  Mací��� ��� ������� ����� ������� ��� ��������� ��� ������������� �� �� ������ ����������� ������������ �� �������� as, J., Castro, M.J., Ortega, S., Escalante C., González-Vida J. M. (2017) Performance Benchmarking of Tsunami- HySEA Model for NTHMP�� ���������� ������� ����������� ���� ����� ��������� ���� ������������������� ’� ���������� ������� ����������� ���� ����� ��������� ���� ������������������� s Inundation Mapping Activities. Pure Appl. Geophys., doi: 10.1007/s00024-017- 1583-1. Molinari I, Tonini R, Lorito S, Piatanesi A, Romano F, Melini D, Hoechner A, Gonzàlez Vida JM, Maciás J, Castro MJ, de la Asunción M (2016). Fast evaluation of tsunami scenarios: uncertainty assessment for a Mediterranean Sea database, Nat. Hazards Earth Syst. Sci., 16, 2593-2602, doi:10.5194/nhess16-2593-2016. Selva J., Tonini R., Molinari I., Tiberti M.M., Romano F., Grezio A., Melini D., Piatanesi A., Basili R., Lorito S. (2016). Quantification of source uncertainties in Seismic Probabilistic Tsunami Hazard Analysis (SPTHA). Geophys. J. Int., 205, 1780-1803, doi:10.1093/gji/ggw107. New seismicity models for updating the national Italian seismic hazard model F. Visini 1 , B. Pace 2 , C. Meletti 1 , W. Marzocchi 1 and MPS16 Working Group 1 INGV - Istituto Nazionale di Geofisica e Vulcanologia 2 DiSPUTer - Università degli Studi “G. d’Annunzio”, Chieti-Pescara, Italy In this work we present the results of the activities of a task (task 3: “seismicity models”) of the MPS16 project that aims at producing the new national probabilistic seismic hazard model for Italy. In particular, the task 3 activities have been focused on the definition of a set of seismicity models and on the analysis of their uncertainty. More than 30 researchers subdivided in 12 working groups have produced 11 seismicity models covering the entire Italian territory and 1 has been built ad hoc for the volcanic Etna area. The 11 national models have been built using different types of sources, methods, and input data. In the following we describe the basic features of each single model. Area Source Models. Model A1 : The seismotectonic zonation (SZ) of A1 has been defined according to the regional seismotectonic settings and past seismicity. The availability of new data and geological studies in most of the country allows the definition of a new SZ model with respect to the one used in the past seismic hazard model for Italy (ZS9 model; Meletti et al. , 2008). Individual seismicity rates have been computed for each zone, using the parametric catalogue of Italian earthquakes (CPTI15); for each zone, the seismicity rate has been assessed using two approaches: i) zones have been grouped into some macro areas to evaluate the b- value, and then it has been used to calculated seismicity rates into the zones according to a GR distribution; ii) using the observed seismicity rates for each zone.