Advancing the ETAS Model to Improve Forecasts of Earthquake Sequences and Doublets

Earthquake sequences typically show distinct spatiotemporal patterns, characterized by a power-law decay
of aftershock times and elongated aftershock distributions around the (extended) rupture. A prominent approach to
model seismic clustering in space and time is the Epidemic Type Aftershock Sequence (ETAS) model that differentiates
an independent background seismicity process from a branching tree process for triggered events. The conventional
ETAS approach shows three substantial biases: (1) The assumption of isotropic spatial distributions of aftershock
locations stands in contrast to observations and geophysical models for large mainshocks. (2) The unlimited spatial
distribution allows small events to trigger aftershocks at unrealistically large distances. (3) Short-term incomplete
event records after large mainshock events suggest supposedly smaller aftershock productivity and cluster sizes. The
above biases can lead to an underestimation of the aftershock productivity of strong events, and in consequence to
underpredicted cluster sizes, and to a miss-specification of the spatial aftershock distribution in the case of clearly ex-
tended ruptures. Here, we combine an ETAS-Incomplete model, accounting for short-term aftershock incompleteness,
with an ETAS approach applying anisotropic, spatially restricted distributions of aftershock locations. We evaluate
the benefits of these models by running forecast experiments for the 2019 Ridgecrest sequence and analyzing the oc-
currence frequencies of so-called Earthquake Doublets, i.e., sequences of two or more similarly strong earthquakes
within a small time-space window. The new model provides more realistic sequence forecasts and doublet predictions
and might be of particular interest for (short term) risk assessment units.