GNGTS 2014 - Atti del 33° Convegno Nazionale

GNGTS 2014 S essione 2.1 101 2010a, 2010b). However, the same authors point out the possible dependence of the model parameterization by local seismotectonic features (e.g,. Lombardi and Marzocchi, 2010a, 2010b). This fact casts doubt on the reliability of the results, like the probability map shown in figure 1, which has been obtained by adopting a model parameterization defined on a national scale. Another important shortcoming is the fact that the physical mechanisms of fault interaction are actually neglected. In the ETAS procedure, the possibility of an earthquake to trigger further shocks in a given site depends on its magnitude and distance from the point considered, by means of empirical relationships derived by the study of the aftershock phenomenology (Tiampo and Shcherbakov, 2012). Instead, the probabilistic algorithm does not incorporate the known processes of co-seismic and post-seismic stress redistribution (e.g., Freed, 2005). Furthermore, the ETAS procedure takes into account neither the focal mechanism of the triggering shock, nor the geometry and kinematics of the fault that would be activated. Finally, �������� ��� ��������� ������� ������ ���� ��� ��������� �� ��� �������������� Lombardi and Marzocchi (2010b) admits that the knowledge of the seismotectonic processes occurring in the study area is not considered by the forecasting procedures so far developed. The ETAS model has also been used to forecast the short-term (days) evolution of seismic swarms and, in particular, the occurrence of aftershocks following strong earthquakes (e.g., Lombardi and Marzocchi, 2010b; Marzocchi et al. , 2012). The latter use is perhaps the most appropriate, given that the methodology is in part based on the aftershock phenomenology, as discussed earlier. However, this kind of application is still relatively uncommon. The most relevant short-term prediction attempt refers to the May-June 2012 seismic sequence in the Modena-Ferrara zone (Marzocchi et al. , 2012). In this case, there is not a close relationship between the temporal pattern of estimated probability and the occurrence of themain aftershocks. Indeed, some events have took place when the probability was the highest, but other shocks (such as the June, 3 2012 one) occurred when the probability was very low (see Fig. 3 by Marzocchi et al. , 2012). Most of the shortcomings pointed out for the ETAS forecasting procedure, concerning theoretical basis, model parametrization and obtained results, can be observed in other long-term predictions performed in the framework of the CSEP experiment (e.g., Akinci, 2010; Faenza and Marzocchi, 2010; Falcone et al. , 2010; Gulia et al. , 2010). For instance, the probability maps provided by these attemps, referred to the prevision interval 2009-2014, show relatively high probability values for zones where minor or no seismic activity has actually occurred (e.g., Friuli, Gargano and eastern Sicily). On the other hand, no of the aforementioned forecasts has clearly pointed out the possibility of destructive shocks in the Po Plain (Modena-Ferrara zone) within the considered prevision interval. Problems of the probabilistic methods. The series of unpredicted, very large earthquakes that have hit various zones of the world in the last decade has raised many concerns about the reliability of the stochastic models adopted for long-term forecasting (e.g., Stein et al. , 2012). So, discussing the major points of weakness underlying the probabilistic algorithms helps understanding the reliability of this approach to the earthquake prediction. First, probabilistic procedures rely on the assumption that the analysis of the past seismicity allows us to forecast (with some uncertainty) the occurrence of future shocks. This implies the availability of accurate, complete and reasonably long seismic catalogs. However, reliable information for the Italian region is reduced to the last millennium (e.g., Rovida et al. , 2011). This time interval is much shorter than the duration of the present seismotectonic setting, presumably started in the Middle Pleistocene (e.g., Mantovani et al. , 2009). The shortness of the available seismic history may crucially affect the results of forecasting procedures (Swafford and Stein, 2007). Second, the relative rarity of strong earthquakes implies that often there are not enough data to test or discriminate among competing stochastic models. This fact poses serious problems for the validation of the probabilistic predictions (Luen and Stark, 2008).