Forecasting strong subsequent earthquakes in Japan using an improved version of NESTORE machine learning algorithm

Abstract

In this study, the advanced machine learning algorithm NESTORE (Next STrOng Related Earthquake) was applied to the Japan Meteorological Agency catalog (1973–2024). It calculates the probability that the aftershocks will reach or exceed a magnitude equal to the magnitude of the mainshock minus one and classifies the clusters as type A or type B, depending on whether this condition is met or not. It has been shown useful in the tests in Italy, western Slovenia, Greece, and California. Due to Japan’s high and complex seismic activity, new algorithms were developed to complement NESTORE: a hybrid cluster identification method, which uses both ETAS-based stochastic declustering and deterministic graph-based selection, and REPENESE (RElevant features, class imbalance PErcentage, NEighbour detection, SElection), an algorithm for detecting outliers in skewed class distributions, which takes in account if one class has a larger number of samples with respect to the other (class imbalance).

Keywords

Machine learning / Cluster identification / ETAS / Strong aftershock / Japan / Outliers detection

CRediT authorship contribution statement

S. Gentili: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. G.D. Chiappetta: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Investigation, Formal analysis, Data curation. G. Petrillo: Writing – review & editing, Investigation, Data curation. P. Brondi: Writing – review & editing, Visualization, Software, Investigation. J. Zhuang: Writing – review & editing, Supervision, Software, Resources, Project administration, Methodology, Funding acquisition, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Developed within the Bilateral project Italy–Japan 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 grant “Progetto INGV Pianeta Dinamico: Near real-time results of Physical and Statistical Seismology for earthquakes observations, modelling and forecasting (NEMESIS)” - code CUP D53J19000170001 - funded by Italian Ministry MIUR (“Fondo Finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese”, legge 145/2018). G.P. would like to acknowledge the Earth Observatory of Singapore (EOS), and the Singapore Ministry of Education Tier 3b project “Investigating Volcano and Earthquake Science and Technology (InVEST)”. G.P. and J.Z. are also partially supported by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) project for seismology Toward Research innovation with data of earthquakes (STAR-E), Grant Number JPJ010217. Map figures have been realized using QGIS (http://www.qgis.org), while other analyses have been performed using ZMAP (http://www.seismo.ethz.ch/en/research-and-teaching/products-software/software/ZMAP). For Fig. 9 we used the free online application Plotdigitizer (https://plotdigitizer.com/app) for data extraction.

The NESTOREv1.0 toolbox is available for free download from GitHub at the address https://github.com/StefaniaGentili/NESTORE and its reproducibility package is available on Zenodo https://zenodo.org/account/settings/github/repository/StefaniaGentili/NESTORE.

We wish to thank Rita Di Giovambattista for her useful suggestions on NESTORE and Ritsuko Matsu'ura for the useful information on M_JMA and M₀ scales. We thank Robert Shcherbakov and two anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.

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