GNGTS 2014 - Atti del 33° Convegno Nazionale

100 GNGTS 2014 S essione 2.1 2012, Frosinone February 16, 2013 and Caserta December 29, 2013. The shocks that occurred in northwestern Tuscany (Garfagnana January 25, 2013 and Lunigiana June 21, 2013) and eastern Marche (Ancona July 21, 2013) do not seem to be well correlated with the areas of highest probability (Fig. 1). The same also holds for the May-June 2012 sequence (8 shocks with M≥4.5), which has took place in the Modena-Ferrara zone near the boundary between the orange and yellow in Fig. 1. Finally, shocks with M≥4.5 have occurred in zones associated with very low probability values (Fig. 1): southern Tyrrhenian Sea August 16, 2010; Rovigo July 17, 2011 and Romagna coastal zone June 6, 2012. In summary, the location of the main shocks actually occurred is poorly correlated with the spatial pattern of probability values. The estimation of the probability is performed for a regular grid of small-area cells (0.1°x0.1°). This results in a smoothed map of the expected seismicity (Fig. 1), which poses some problems to the practical use of such product. For instance, the possible epicentral areas of major shocks are poorly constrained. Moreover, the computed probabilities ��� ���������� ​are everywhere very small (10 -3 to 10 -6 ), in spite of the sharp colour contrast adopted in Fig. 1. It is not clear which would be the threshold value that implies special attention by the users of the map. Also, the fact that even minor shocks (4.5≤ M <5.5) are considered, does not make the forecast more effective. Predicting that the whole Apennine belt will be hit within a few years by a series of small earthquakes is a plausible statement, but does not add much to what is already known about the seismicity pattern of the Italian region. To plan appropriate prevention activities, the prediction should focus to identify in advance the zones prone to destructive events (M≥5.5). In addition, several objections concerns the ETAS methodology. First, the results obtained strictly depend on the many parameters of the stochastic model. The numerical value of these parameters is usually obtained by the statistical analysis of the seismic catalog, often using the first half of the catalog to retrospectively predict the second part (e.g,. Lombardi and Marzocchi, Fig. 1 – Probability that at least one shallow (h≤30 km) earthquake with magnitude M L ≥4.5 will occur in the time interval August 1, 2009 - July, 31 2014 (from Lombardi and Marzocchi, 2010a). As suggested by CSEP (Schorlemmer et al. , 2010), probability values have been computed for the small-area (0.1°x0.1°) cells of a regular grid superimposed to the study area. The green circle identifies the cell to which the maximum probability value is assigned. The white dots indicate the epicenters of the earthquakes with M L ≥4.5 that have took place in the above prediction interval (see text).