Neural Negative Binomial Regression for Weekly Seismicity Forecasting: Per-Cell Dispersion Estimation and Tail Risk Assessment
Researchers have developed a new neural network architecture called EarthquakeNet to improve the forecasting of weekly earthquake occurrences. This model addresses limitations in standard approaches by estimating an endogenous per-cell overdispersion parameter, capturing spatial heterogeneity in seismic clustering. Evaluations show EarthquakeNet reduces prediction errors by 8.6% compared to existing methods, with a 12.5% improvement in forecasting extreme events. AI
IMPACT Introduces a novel neural network architecture for seismic forecasting, potentially improving accuracy and risk assessment for extreme events.