Machine Learning Forecasting of Strong Subsequent Events in New Zealand Using the NESTORE Algorithm

Highlights: What are the main findings? The machine learning algorithm NESTORE accurately forecasts the likelihood of strong aftershocks in New Zealand using seismicity recorded within hours after a mainshock. Across the 1988–2025 dataset, NESTORE correctly classified 88% of clusters, including the Canterbury–Christchurch 2010–2011 sequence. What are the implications of the main findings? NESTORE provides a promising approach under retrospective testing for near-real-time assessment of strong aftershock potential, supporting rapid response in one of the world’s most active seismic regions. The method can in future enhance operational post-earthquake forecasting and contribute to risk-mitigation strategies in New Zealand. New Zealand, located along the boundary between the Pacific and Australian plates, is among the most seismically active regions in the world. In such an area, reliable short-term forecasting of strong aftershocks is essential for seismic risk mitigation. In this study, we apply NESTORE (NExt STrOng Related Earthquake), a machine learning probabilistic forecasting algorithm, to the New Zealand earthquake catalogue to evaluate the probability that a mainshock of magnitude Mm will be followed by an event of magnitude ≥ Mm − 1 within a defined space–time window. NESTORE uses nine features describing early post-mainshock seismicity and outputs the probability that a cluster is Type A (i.e., containing a strong aftershock) or not (Type B). We assess performance using two testing strategies: chronological training–testing splits and k-fold cross-validation and refine the training set using the REPENESE outlier-detection procedure. The k-fold approach proves more robust than the chronological one, despite changes in catalogue characteristics over time. Eighteen hours after the mainshock, NESTORE correctly classified 88% of clusters (75% for Type A and 92% for Type B; Precision = 0.75). Notably, the highly destructive 2010–2011 Canterbury–Christchurch sequence was correctly identified as Type A. These findings support the applicability of NESTORE for short-term aftershock forecasting in New Zealand.