GNGTS 2017 - 36° Convegno Nazionale

GNGTS 2017 S essione 2.1 285 4. �� �������� ��� ������� ������� ��������� ����� �� ����� �� ������ � ������� ����������� we excluded the Italian records preceding 2009, in order to obtain a dataset independent by that used to calibrate the model by Bindi et al. (2011), named After 2009. Fig. 2 shows the trend of the LLH with period for the model selected for ACRs. The ranking for the datasets All and After 2009 are very similar, because the data used to calibrate the GMPEs is less the 15% with respect to the total dataset. LLH values for EC8-A are generally largest, while the ranking of MR exhibits the lowest values. We assigned 16 points to best performing GMPEs and 1 point to the worst for each ranking scheme and for each dataset; the points are summed up in order to obtain a final rank. The total score for each dataset is reported in Tab. 1. We finally select four GMPEs (Bindi et al., 2011; Cauzzi et al., 2015; Akkar et al., 2014; Boore et al ., 2014) over the candidates for ACRs, considering the results of the reference site condition subset, although is not uniquely defined in the different ground motion models. We disregarded ASB14 in epicentral distance, as we preferred models which account for the finite fault geometry. Between the two pan-european models (ASB14 and BND14) calibrated for hypocentral distance, we preferred the former, as the functional form is different from ITA10, which is the best performing model. In the final selection, we included two worldwide model, which are CZ15 and the best performing model among NGA-West2 (BSSA14), which is also prefer-able to the other NGA-West2 models for the simpler functional form. References Akkar, S., M. A. Sandıkkaya, and J. J. Bommer (2014), Empirical ground-motion models for point- and extended- source crustal earthquake scenarios in Europe and the Middle East, Bull Earthquake Eng, 12(1), 359–387, doi:10.1007/s10518-013-9461-4. Bindi, D., F. Pacor, L. Luzi, R. Puglia, M. Massa, G. Ameri, and R. Paolucci (2011), Ground motion prediction equations derived from the Italian strong motion database, Bull Earthquake Eng, 9(6), 1899–1920, doi:10.1007/ s10518-011-9313-z. Bommer, J. J., J. Douglas, F. Scherbaum, F. Cotton, H. Bungum, and D. Fah (2010), On the Selection of Ground- Motion Prediction Equations for Seismic Hazard Analysis, Seismological Re-search Letters, 81(5), 783–793, doi:10.1785/gssrl.81.5.783. Boore, D. M., J. P. Stewart, E. Seyhan, and G. M. Atkinson (2014), NGA-West2 Equations for Predicting PGA, PGV, and 5% Damped PSA for Shallow Crustal Earthquakes, Earthq Spectra, 30(3), 1057–1085, doi:10.1193/ 070113EQS184M. Cauzzi, C., E. Faccioli, M. Vanini, and A. Bianchini (2015), Updated predictive equations for broad-band (0.01–10 s) horizontal response spectra and peak ground motions, based on a global dataset of digital acceleration records, Bull Earthquake Eng, doi:10.1007/s10518-014-9685-y. CEN (2003). Eurocode 8: Design of structures for earthquake resistance—Part 1: General rules, seismic actions and rules for buildings. Bruxelles: European Committee for Standardization. Cotton, F., F. Scherbaum, J. J. Bommer, and H. Bungum (2006), Criteria for Selecting and Adjusting Ground-Motion Models for Specific Target Regions: Application to Central Europe and Rock Sites, J Seismol, 10(2), 137–156, doi:10.1007/s10950-005-9006-7. Delavaud, E. et al. (2011), Toward a ground-motion logic tree for probabilistic seismic hazard assessment in Europe, J Seismol, 16(3), 451–473, doi:10.1007/s10950-012-9281-z. Douglas, J. (2015), Ground motion prediction equations 1964-2015, http://www.gmpe.org.uk . Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of theAmerican Statistical Association, 102, 359-378. Scherbaum, F., E. Delavaud, and C. Riggelsen (2009). Model selection in seismic hazard analysis: An information– theoretic perspective, Bull. Seismol. Soc. Am. 99, no. 6, 3234–3247. Stucchi, M., C. Meletti, V. Montaldo, H. Crowley, G. M. Calvi, and E. Boschi (2011), Seismic Hazard Assessment (2003-2009) for the Italian Building Code, Bull Seismol Soc Am, 101(4), 1885–1911, doi:10.1785/0120100130. Zechar, J. D., and Zhuang, J. (2014) A parimutuel gambling perspective to compare probabilistic seismicity forecasts. Geophysical Journal International, 199, 60-68. doi:10.1093/gji/ggu137.