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 re...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).Trained with data from 1973 to 2004(7 type A and 43 type B clusters)and tested from 2005 to 2023(4 type A and 27 type B clusters),the method correctly forecasted 75%of A clusters and 96%of B clusters,achieving a precision of 0.75 and an accuracy of 0.94 six hours after the mainshock.It accurately classified the 2011 Tōhoku event cluster.Near-real-time forecasting was applied to the sequence after the April 17,2024 M6.6 earthquake in Shikoku,correctly classifying it as a“Type B cluster”.These results highlight the potential for the forecasting of strong aftershocks in regions with high seismicity and class imbalance,as evidenced by the high recall,precision and accuracy values achieved in the test phase.展开更多
基金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.12432/8/2022,PE0000005)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)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.
文摘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).Trained with data from 1973 to 2004(7 type A and 43 type B clusters)and tested from 2005 to 2023(4 type A and 27 type B clusters),the method correctly forecasted 75%of A clusters and 96%of B clusters,achieving a precision of 0.75 and an accuracy of 0.94 six hours after the mainshock.It accurately classified the 2011 Tōhoku event cluster.Near-real-time forecasting was applied to the sequence after the April 17,2024 M6.6 earthquake in Shikoku,correctly classifying it as a“Type B cluster”.These results highlight the potential for the forecasting of strong aftershocks in regions with high seismicity and class imbalance,as evidenced by the high recall,precision and accuracy values achieved in the test phase.
