Testing Aftershock Forecasts Using Bayesian Methods
Abstract
The presence of strong aftershocks can increase the seismic hazard following a large earthquake and should be considered for operational earthquake forecasting and risk management. Aftershock forecasts are generated from seismicity models during the evolution of the aftershock sequence. This work compares quantitative test results of the forecasting abilities for three competing aftershock rate models - the modified Omori law, the Epidemic Type Aftershock Sequence model, and the compound Omori law - to identify the best performing model for forecasting the largest aftershock during the early aftershock sequence. Forecasts of large aftershock probabilities are generated by either the Extreme Value distribution or the Bayesian Predictive distribution for the forecasting time interval. Testing is conducted retrospectively on five sequences for a fixed forecasting time interval of seven days during the early aftershock sequence. None of the models and forecasting methods consistently outperforms the others regardless of the training time interval.