Forecasting Strong Subsequent Earthquakes in Japan Using NESTORE Machine Learning Algorithm: preliminary results

NESTORE (Next STrOng Related Earthquake) is a machine learning algorithm for forecasting strong aftershocks during ongoing earthquake clusters. It has already been successfully applied to Italian, Greek and Californian seismicity in the past. A free version of the software in MATLAB (NESTOREv1.0) is available on GitHub. The method is trained on the region under investigation using seismicity characteristics. The obtained region-specific parameters are used to provide the probability, for the ongoing clusters, that the strongest aftershock has a magnitude greater than or equal to that of the mainshock - 1. If this probability is greater than or equal to 0.5, the cluster is labeled as type A, otherwise as type B. The current version of the code is modular and the cluster identification method is based on a window approach, where the size of the spatio-temporal window can be adjusted according to the characteristics of the analyzed region.

In this study, we applied NESTORE to the seismicity of Japan using the Japan Meteorological Agency (JMA) catalogue from 1973 to 2022. To account for the highly complex seismicity of the region, we replaced the cluster identification module with software that uses a stochastic declustering approach based on the ETAS model.

The analysis is performed in increasing time intervals after the mainshock, starting a few hours later, to simulate the evolution of knowledge over time. The analysis showed a high prevalence of clusters where there are no strong earthquakes later than 3 hours after the mainshock, leading to an imbalance between type A and type B classes.

NESTORE was trained with data from 1973 to 2004 and tested from 2005 onwards. The large imbalance in the data was mitigated by carefully analyzing the training set and developing techniques to remove outliers. The cluster type forecasting was correct in 84% of cases.

 

Funded by a grant from the Italian Ministry of Foreign Affairs and International Cooperation and Co-funded within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan - NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) and by the NEar real-tiME results of Physical and StatIstical Seismology for earthquakes observations, modeling and forecasting (NEMESIS) Project (INGV).