Elliptic Triggering Kernels and Adaptive Productivity in European OEF

Operational Earthquake Forecasting (OEF) predominantly relies on Epidemic-Type Aftershock Sequence (ETAS) models for short-term seismicity forecasts. We first develop and calibrate a baseline ETAS model for the European region, systematically exploring parameterisations that include alternative productivity laws and spatially variable background rates informed by the European Seismic Hazard Model (ESHM20). These extensions provide a consistent reference framework for regional-scale OEF. Building on this baseline, we improve the spatial triggering component by replacing isotropic kernels with event-specific elliptic kernels that incorporate directional information inferred from aftershock distributions. In near-real-time forecasting, the estimation of kernel orientations introduces a latency, as directional information becomes available only after sufficient aftershocks have occurred. However, our model leads to improved performance in pseudo-prospective forecasts, highlighting the relevance of spatial anisotropy in triggered seismicity. We also find reduced bias in ETAS parameters, primarily the productivity law. 

We further investigate mismatches between expected and observed aftershock productivity by proposing simple productivity updates based on residuals between predicted and observed aftershock counts, yielding modest positive information gain on average. A sequence-by-sequence analysis reveals, however, that some sequences transition from early underestimation to later overestimation, or vice versa, limiting the effectiveness of uniform adaptive schemes. We therefore explore whether early sequence behaviour and covariates such as tectonic regime, location, and geophysical features can help anticipate subsequent productivity evolution. Finally, we assess the practical value of increasing model complexity for OEF, questioning whether statistically significant performance gains translate into meaningful improvements over simpler forecasting approaches.