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Abstract:
Earthquake sequences add a substantial hazard beyond the solely declustered perspective of common probabilistic seismic hazard analysis. A particularly strong driver for both social and economic losses are so‐called earthquake doublets (more generally multiplets), that is, sequences of two (or more) comparatively large events in spatial and temporal proximity. Without differentiating between foreshocks and aftershocks, we hypothesize three main influencing factors of doublet occurrence: (1) the number of direct and secondary aftershocks triggered by an earthquake; (2) the occurrence of independent clusters and seismic background events in the same time–space window; and (3) the magnitude size distribution of triggered events (in contrast to independent events). We tested synthetic catalogs simulated by a standard epidemic‐type aftershock sequence (ETAS) model for both Japan and southern California. Our findings show that the common ETAS approach significantly underestimates doublet frequencies compared with observations in historical catalogs. In combination with that the simulated catalogs show a smoother spatiotemporal clustering compared with the observed counterparts. Focusing on the impact on direct aftershock productivity and total cluster sizes, we propose two modifications of the ETAS spatial kernel to improve doublet rate predictions: (a) a restriction of the spatial function to a maximum distance of 2.5 estimated rupture lengths and (b) an anisotropic function with contour lines constructed by a box with two semicircular ends around the estimated rupture segment. These modifications shift the triggering potential from weaker to stronger events and consequently improve doublet rate predictions for larger events, despite still underestimating historic doublet occurrence rates. Besides, the results for the restricted spatial functions fulfill better the empirical Båth’s law for the maximum aftershock magnitude. The tested clustering properties of strong events are not sufficiently incorporated in typically used global catalog scale measures, such as log‐likelihood values, which would favor the conventional, unrestricted models.
