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《Chinese Journal of Geophysics》 2019-01
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Seismic event classification based on bagging ensemble learning algorithm

REN Tao;LIN MengNan;CHEN HongFeng;WANG RanRan;LI SongWei;LIU XiaoYu;LIU Jie;Software College of Northeastern University;China Earthquake Networks Center;  
The non-natural vibration events recorded by the Seismic Network while monitoring the earthquake will have a greater impact on the subsequent research and forecasting work.Therefore,it is particularly important to distinguish between natural earthquakes and non-natural vibration events quickly and accurately.In this paper,the Bagging machine learning algorithm is used to distinguish the nature of earthquake events,to improve the inadequacies of traditional artificial methods to identify the nature of earthquake events.Firstly,the seismic data with the epicenter distance in the range of 80~200 km is selected.Then,the AIC algorithm is utilized to automatically identify the arrival time of the P wave.After that,the processed data is used to train the model.Finally,the model is evaluated using the test data,and the accuracy rate is up to85%.The method proposed in this paper can effectively distinguish between natural earthquakes and non-natural vibration events.
【Fund】: 国家自然科学基金资助项目(61473073 61104074);; 中央高校基本科研业务费(N161702001);; 辽宁省高校优秀人才基金(LJQ2014028)资助
【CateGory Index】: P315.7
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【Co-citations】
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