Retrospective Operational Aftershock Forecasting for 2016 Amatrice-Norcia Seismic Sequence in Central Italy

Abstract

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 fullyprobabilistic procedure succeeds in providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval (in the order of hours or days). The procedure aims at exploiting the information provided by the ongoing seismic sequence in quasireal time. The versatility of the Bayesian inference is exploited to adaptively update the forecasts 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 procedure 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.