GNGTS 2017 - 36° Convegno Nazionale

272 GNGTS 2017 S essione 2.1 a good agreement (7.4 expected events vs. 8). Note that the equivalent rate must not be confused with the actual Poisson rate obtained through catalog declustering. Removing dependent events significantly underestimates the observed rate (3 expected events vs. 8), clearly affecting the seismic hazard. As an example, Fig. 1 shows a very first application of our forecasting model within the framework of a probabilistic ground-motion hazard assessment in central Italy. To this end, the prediction equation for PGA of Bindi et al. (2011) was applied. Results will be compared with those obtained via conventional approaches assuming that seismicity follows a Poisson process. As with all new methods, our forecasting model clearly deserves further testing but appears promising, at the least for long- term (tens of years) probabilistic seismic hazard assessment. Its applicability is not only limited to earthquake forecasting. In fact, it can be easily extended to other disciplines dealing with self-similar processes, particularly to other geohazards, such as rainstorms, floods, landslides, avalanches, and tsunamis. For further details on this research, interested readers may refer to Barani et al. (2017). References Akinci A., Galadini F., Pantosti D., Petersen M., Malagnini L., Perkins D.; 2009: Effect of time dependence on probabilistic seismic-hazard maps and deaggregation for the Central Apennines, Italy . Bull. Seismol. Soc. Am., 99 , 585-610. Barani S., Mascandola C., Riccomagno E., Spallarossa D., Albarello D., Ferretti G., Scafidi D., Augliera P., Massa M.; 2017: Long-range dependence in earthquake-moment release and implications for earthquake occurrence probability . Submitted to Scientific Reports. Bindi D., Pacor F., Luzi L., Puglia R., Massa M., Ameri G. and Paolucci R.; 2011: Ground motion prediction equations derived from the Italian strong motion database . Bulletin of Earthquake Engineering, 9 , 1899-1920. Frankel A.; 1991: High-frequency spectral falloff of earthquakes, fractal dimension of complex rupture, b value, and the scaling of strength on faults . J. Geophys. Res., 96 , 6291-6302. Gutenberg B., Richter, C.R.; 1944: Frequency of earthquakes in California . Bull. Seismol. Soc. Am., 34 , 185-188. Hurst H. E.; 1951: Long-term storage capacity of reservoirs . Am. Soc. Civil Eng. Trans., 2447 , 770-808. Kagan Y. Y. Jackson, D. D.; 1991: Long-term earthquake clustering . Geophys. J. Int., 104 , 117-133. Kagan Y. Y., Knopoff, L.; 1980: Spatial distribution of earthquakes: the two-point correlation function . Geophys. J. Roy. Astron. Soc., 62 , 303-320. King G.; 1983: The Accommodation of large strains in the upper lithosphere of the earth and other solids by self- similar fault systems: the geometrical origin of b-value . Pageoph., 121 , 761-815. Lomnitz C.; 1994: Fundamentals of Earthquake Prediction . John Wiley & Sons Inc., 326 pp. Mandelbrot B.; 1982: The Fractal Geometry of Nature . W. H. Freeman and Co., New York. Petersen M. D., Cao T., Campbell K. W., Frankel A. D.; 2007: Time-independent and time-dependent seismic hazard assessment for the state of California: uniform California earthquake rupture forecast model 1.0 . Seismol. Res. Lett., 78 , 99-109. Scholz C. H.; 2002: The Mechanics of Earthquakes and Faulting . Cambridge University Press. Stein R. S.; 1999: The role of stress transfer in earthquake occurrence . Nature, 402 , 605-609. Fig. 1 - Ground motion hazard map for PGA corresponding to a mean return period of 475 years.