任涛^1,,
林梦楠¹,
陈宏峰^2,,,
王冉冉¹,
李松威¹,
刘晓雨²,
刘杰²
1. 东北大学软件学院, 沈阳 110819
2. 中国地震台网中心, 北京 100029

基金项目: 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


详细信息

作者简介: 任涛, 男, 博士, 教授, 主要从事人工智能相关研究.E-mail:chinarentao@163.com

通讯作者: 陈宏峰, 男, 高级工程师, 长期从事地震监测业务研究.E-mail:chf@seis.ac.cn

中图分类号: P315

收稿日期:2018-07-04
修回日期:2018-11-22
上线日期:2019-01-05

Seismic event classification based on bagging ensemble learning algorithm

REN Tao^1,,
LIN MengNan¹,
CHEN HongFeng^2,,,
WANG RanRan¹,
LI SongWei¹,
LIU XiaoYu²,
LIU Jie²
1. Software College of Northeastern University, Shenyang 110819, China
2. China Earthquake Networks Center, Beijing 100029, China


More Information

Corresponding author: CHEN HongFeng,E-mail:chf@seis.ac.cn

MSC: P315

--> Received Date: 04 July 2018
Revised Date: 22 November 2018
Available Online: 05 January 2019


摘要
摘要:地震台网在监测地震的同时记录到的非天然震动事件会对后续的科研和预报工作造成较大的影响, 因此快速准确的对天然震动事件与非天然震动事件加以区分就显得尤为重要.本文针对传统人工方法识别地震事件性质的不足之处, 采用Bagging机器学习算法对地震事件性质进行区分.首先选取震中距范围在80~200 km内的地震数据, 之后采用AIC算法自动识别P波到时, 进而用处理后的数据训练模型, 最后使用测试数据对模型进行评估, 准确率可达85%以上.因此, 本文提出的方法可以有效地对天然震动事件与非天然震动事件加以区分.
关键词: 地震事件分类/
频谱比值/
自相关系数/
Bagging算法

Abstract: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 to 85%.The method proposed in this paper can effectively distinguish between natural earthquakes and non-natural vibration events.
Key words:Seismic event classification/
Spectrum ratio/
Autocorrelation coefficient/
Bagging algorithm



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