GNGTS 2016 - Atti del 35° Convegno Nazionale

GNGTS 2016 S essione 2.1 281 Grünthal G., Wahlström R.; 2012: The European-Mediterranean Earthquake Catalogue (EMEC) for the last millennium . J. Seismol., 16 , 535-570. Gutscher M. -A., Roger J., Baptista M. –A., Miranda J. M., Tinti S.; 2006: Source of the 1693 Catania earthquake and tsunami (southern Italy): new evidence from tsunami modelling of a locked subduction fault plane . Geophys. Res. Lett., 33 , L08309, doi:10.1029/2005GL025442. Maramai A., Brizuela B., Graziani L.; 2014: The Euro-Mediterranean Tsunami Catalogue . Annals of Geophysics, 57(4) , S0435, doi:10.4401/ag-6437. Okada Y.; 1985: Surface deformation due to shear and tensile faults in a half-space . Bull. Seism. Soc. Am., 75 , 1135- 1154. Tinti S., Armigliato, A.; 2003: The use of scenarios to evaluate the tsunami impact in southern Italy . Mar. Geol., 199 , 221-243. Tinti S., Tonini R.; 2013: The UBO-TSUFD tsunami inundation model: validation and application to a tsunami case study focused on the city of Catania, Italy . Nat. Hazards Earth Syst. Sci., 13 , 1795-1816. A new geodetically-derived seismicity model for Italy N. D’Agostino Centro Nazionale Terremoti, Istituto Nazionale Geofisica Vulcanologia, Roma, Italy The exponential increase of the number of continuously recording GNSS stations in the last 10 years, deployed for both scientific and civilian applications, now allows an accurate mapping of the distribution of tectonic strain rate in the Italian peninsula. A compilation of the station velocities having observation intervals longer than 3.5 years has been recently submitted as input data in the framework of the planned update of the Italian probabilistic seismic hazard map (http:// https://ingvcps.wordpress.com ). This solution, aimed at representing a consensus velocity field for the Italian region, is based on the least-squares combination of three independent solutions released by the GPS analyses centers of the CNT-INGV (Centro Nazionale Terremoti, Istituto Nazionale Geofisica Vulcanologia) obtained with different processing softwares (Bernese, Gamit, Gipsy) and strategies. It is now widely recognized that the best approach to assess the uncertainties of earthquake hazard estimates, is to compare and combine independently-derived seismicity models from seismology, geology and geodesy. Measurements of crustal deformation can, under simple assumptions, be translated in estimates of seismicity rates for different magnitudes classes (Molnar, 1979; Kagan, 2002, Ward, 2007). Starting from the GPS velocity field described above, I thus propose a geodetically-derived seismicity model for the Italy and neighbouring regions to be submitted as one of the input seismicity model for the planned update of the Italian probabilistic seismic hazard map. I select 919 station horizontal velocities from the consensus velocity model, excluding stations in volcanic areas and velocities discrepant with regional velocity field. The strain rate tensor field has been calculated on a regular 0.1° � ���� ���� ����� x 0.1° grid using the VISR software (Shen et al. , 2015) taking into account the variable station spacing for the optimal smoothing parameters and finally applying a Gaussian filter of 50 km (6-sigma width) to the scalar strain rate value (maximum absolute eigenvalue of the strain rate tensor). Finally, rate of seismic moment accumulation density is converted to earthquake rate density (or earthquake potential) under the assumption (Kagan, 2002; Ward, 1998) that seismic moment distributes into earthquake sizes that follow a tapered Gutenberg-Richter (Gutenberg and Richter 1954; Kagan, 2002) distribution of given b-value and M max . Due to the limited knowledge, I made the simplest assumptions on the spatial variation of the relevant parameters entering in the conversion from strain rate to seismicity rates. In this sense we assumed a constant b-value (b=1) of the GR relationship, and constant values of the thickness of the seismogenic depth and of the maximum