ETAS-Approach Accounting for Short-Term Incompleteness of Earthquake Catalogs

ABSTRACT The epidemic-type aftershock sequence (ETAS) model is a powerful statistical to explain and forecast the spatiotemporal evolution of seismicity. However, its parameter estimation can be strongly biased by catalog deficiencies, particularly short-term incompleteness related missing events in phases high-seismic activity. Recent studies have shown that these fluctuations completeness magnitude explained blindness detection algorithms after earthquakes, preventing with smaller magnitude. Based on this assumption, I derive direct relation between true detectable seismicity rate distributions, respectively. These relations only include one additional parameter, so-called blind time Tb, lead closed-form maximum-likelihood formulation estimate ETAS parameters directly accounting for varying completeness. Tests using synthetic simulations show resolved from incomplete catalogs. Finally, apply new California’s most prominent mainshock–aftershock sequences last decades. results leads superior fits Tb decreasing time, indicating improved algorithms. estimated significantly differ standard approach, higher b-values larger trigger potentials than previously thought.