Forecasting Strong Subsequent Earthquakes in Japan using an improved version of NESTORE Machine Learning Algorithm
Stefania Gentili, Giuseppe Davide Chiappetta, Giuseppe Petrillo, Piero, Brondi, Jiancang Zhuang

TL;DR
This paper enhances the NESTORE machine learning algorithm to improve forecasting of strong aftershocks in Japan, incorporating new methods to handle high seismic activity and class imbalance, achieving high accuracy in predictions.
Contribution
It introduces new algorithms and features tailored for Japan's seismic data, significantly improving aftershock forecasting accuracy over previous models.
Findings
Correctly forecasted 75% of A clusters and 96% of B clusters.
Achieved 0.75 precision and 0.94 accuracy six hours after mainshocks.
Successfully classified the 2011 Tōhoku earthquake cluster.
Abstract
The advanced machine learning algorithm NESTORE (Next STrOng Related Earthquake) was developed to forecast strong aftershocks in earthquake sequences and has been successfully tested in Italy, western Slovenia, Greece, and California. NESTORE calculates the probability of aftershocks reaching or exceeding the magnitude of the main earthquake minus one and classifies clusters as type A or B based on a 0.5 probability threshold. In this study, NESTORE was applied to Japan using data from the Japan Meteorological Agency catalog (1973-2024). Due to Japan's high seismic activity and class imbalance, new algorithms were developed to complement NESTORE. The first is a hybrid cluster identification method using ETAS-based stochastic declustering and deterministic graph-based selection. The second, REPENESE (RElevant features, class imbalance PErcentage, NEighbour detection, SElection), is…
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Taxonomy
TopicsEarthquake Detection and Analysis · Seismology and Earthquake Studies
