GNGTS 2017 - 36° Convegno Nazionale

294 GNGTS 2017 S essione 2.1 The outcomes of these analyses can explain some of the variability of models developed for the same tectonic region but – if the goal is to go in the direction of having more homogenous models – we must improve the way we describe the methods adopted for the construction of the models and – ultimately – the assessment of seismic hazard. Fig. 1 - Current status of the GEM global mosaic. Investigating features of earthquake clustering: a comparative analysis for selected sequences in Italy A. Peresan 1 , R. Rotondi 2 , E. Varini 2 , S. Gentili 1 1 National Institute of Oceanography and Experimental Geophysics. CRS-OGS, Udine, Italy 2 Istituto di Matematica Applicata e Tecnologie Informatiche, CNR. Milano, Italy A number of studies aimed at seismic hazard assessment, as well as at the space-time analysis of earthquakes occurrence (e.g. Gentili et al. , 2017), require preliminary declustering of the earthquake catalog. Moreover, the identification and statistical characterization of seismic clusters provide useful insights about the features of seismic energy release and their relation to physical properties of the crust within a given region. Earthquake clustering, in fact, is an fundamental aspect of seismicity, with typical features in space, time, and energy domains that provide key information about earthquake dynamics. In spite of the overall agreement about the existence of different types of clusters (sequences, swarms, bursts, etc.), there is no agreed formal definition of seismic clusters, nor a unique method to identify them. Most of the declustering algorithms available in literature are based on a deterministic space-time-window scheme or on a stochastic branching model (e.g. ETAS model by Ogata, 1998), which are generally suitable for large earthquakes, characterized by evident aftershock series clearly emerging from the background seismicity. Since various methods, relying on different physical/statistical assumptions, may lead to diverse classifications of earthquakes into main events and related events, we investigate the classification differences among different declustering techniques. Various techniques, including classical space-time windows methods