Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment
Better earthquake forecasts by mapping how shaking clusters differ across regions
Standard earthquake forecasting assumes seismic activity follows the same random pattern everywhere, but analysis of Central Asian earthquakes from 2010–2024 overwhelmingly rejects this assumption. A new neural network model called EarthquakeNet estimates how clustering patterns vary location-by-location, improving weekly forecasts by 8.6 percent overall and 12.5 percent for high-magnitude weeks when accurate predictions matter most.
Earthquake early-warning systems guide emergency response and evacuation decisions. Better forecasts of which regions will experience intense clustering in a given week could help authorities pre-position resources and issue more reliable alerts. The model's strongest gains come in predicting extreme weeks (5+ earthquakes), exactly when forecasts are hardest to make and most consequential for public safety.