Testing the forecasting skills of aftershock models using a Bayesian framework

The Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that used for operational earthquake/aftershock forecasting. Previous studies have investigated relative performance of specific case studies. However, a rigorous comparative evaluation forecasting basic several different earthquake sequences has not been done before. In this study, forecasts five prominent from multiple catalogs computed using Bayesian predictive distribution, which fully accounts uncertainties in parameters. This is by Markov Chain Monte Carlo (MCMC) sampling parameters forward simulation ETAS or MOL to compute forecasts. results evaluated statistical tests, including comparison tests. skill tests indicate tends perform consistently well on first three fails same certain time intervals. it definite whether better performing model. work demonstrates use forecast testing catalogs, also applicable with higher magnitude completeness.