RETROSPECTIVE OPERATIONAL AFTERSHOCK FORECASTING FOR 2016 AMATRICE-NORCIA SEISMIC SEQUENCE IN CENTRAL ITALY

The first days elapsed after the occurrence of an earthquake and its triggered aftershocks are crucial in terms of emergency decision-making. To this end, the adopted novel and fully-probabilistic procedure succeeds in providing spatio-temporal predictions of aftershock oc-currence in a prescribed forecasting time interval (in the order of hours or days). The proce-dure aims at exploiting the information provided by the ongoing seismic sequence in quasi-real time. The versatility of the Bayesian inference is exploited to adaptively update the fore-casts based on the incoming information as it becomes available. The aftershock clustering in space and time is modelled based on an Epidemic Type Aftershock Sequence (ETAS) model. One of the main novelties of the proposed procedure is that it considers the uncertainties in the aftershock occurrence model and its model parameters. This is done by pairing up the Bayesian robust reliability framework and the suitable simulation schemes (Markov Chain Monte Carlo Simulation) provides the possibility of performing the whole forecasting proce-dure with minimum (or no) need of human interference. This procedure is demonstrated through a retrospective spatio-temporal early forecasting of seismicity associated with the 2016 Amatrice-Norcia seismic sequence in central Italy. Seismicity forecasts are issued with various time intervals in the first few days after the main events within the sequence.