Leveraging the value of seismic catalogue features in building a spatiotemporal system to assess localised seismic hazard

In a previous work, we established the predictive power of seismic statistical catalogue features for whole-region temporal forecasting. We here extend this framework to a spatiotemporal approach to assess localised seismic hazard in Japan and Chile. Using ensemble learning, we predict the occurrence of M>=5 earthquakes within a 15-day horizon across varying radial distances (r=3 to 24 km) to benchmark the framework's sensitivity as a proof-of-concept prior to scaling for larger magnitude hazards.

Results indicate robust predictive power, though performance is sensitive to the prediction radius. The Japan catalogue yields an AUC of 0.76 for predictions within 24 km. However, when the prediction radius is tightened to 12 km, while the model retains predictive power (AUC 0.62), the reduced performance underscores the challenge of highly localised forecasting. Crucially, we observe a distinct shift in feature importance as the spatial scale changes: parameters that track local variations in seismicity—specifically the b-value, within our feature set—rank significantly higher in localised models compared to whole-region baselines. This suggests that machine learning models can produce forecasts that reflect underlying physical fault processes.

We further present ongoing work regarding spatiotemporally overlapping predictions, testing the hypothesis that multiple alerts intersecting in both space and time indicate a compounded hazard probability. Finally, responding to the challenges of localised prediction, we introduce a novel experimental framework that augments our current statistical features by exploring additional spatial descriptors, including both deep learning representations and hand-crafted spatial features, designed to capture aspects of fault dynamics beyond standard catalogue statistics.