Bayesian Earthquake Forecasting approach based on the Epidemic Type Aftershock Sequence model

The Epidemic Type Aftershock Sequence (ETAS) model is used as a baseline model both for earthquake clustering and earthquake prediction. In most forecasting experiments, the ETAS parameters are estimated based on a short and local catalog, therefore the model parameter optimization carried out by means of a Maximum Likelihood Estimation may be not as robust as expected. We use Bayesian forecast techniques to solve this problem, where a non-informative flat prior distributions of the parameters is adopted. A Metropolis-Hasting algorithm is employed to sample the model parameters and earthquake events. We also show, through a forecasting experiment, how the Bayesian inference of the parameters allows to obtain a less confident prediction.