蒋一然^1,2,,
宁杰远^2,,
1. 页岩油气富集机理与有效开发国家重点实验室, 北京 100083
2. 北京大学地球与空间科学学院, 北京 100871

基金项目: 中国石油化工股份有限公司石油勘探开发研究院开放基金项目(GSYKY-B09-33)及内蒙古自治区2016年度科技重大专项"重点地区地震预测预警技术研究开发与推广示范"资助


详细信息

作者简介: 蒋一然, 男, 2017年于北京大学地球与空间科学学院获得理学学士学位, 现为北京大学地球与空间科学学院硕士生, 主要从事地震学研究.E-mail:yiranj@pku.edu.cn

通讯作者: 宁杰远, 男, 教授, 主要从事地震构造学研究.E-mail:njy@pku.edu.cn

中图分类号: P315

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

Automatic detection of seismic body-wave phases and determination of their arrival times based on support vector machine

JIANG YiRan^1,2,,
NING JieYuan^2,,
1. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
2. School of Earth and Space Sciences, Peking University, Beijing 100871, China


More Information

Corresponding author: NING JieYuan,E-mail:njy@pku.edu.cn

MSC: P315

--> Received Date: 11 July 2018
Revised Date: 01 November 2018
Available Online: 05 January 2019


摘要
摘要:面对海量地震资料，自动准确地拾取震相并确定其到时的需求非常迫切.基于支持向量机技术，本文提出了使用两个分类器SSD和SPS自动识别地震体波震相并自动拾取其到时的方法.相比于传统的自动拾取方法，本文方法能够更准确地识别震相并区分P波和S波.进一步地，我们提出了利用台阵资料辅助识别震相的方案，有效地提高了地震震相拾取的准确率.
关键词: 地震震相识别/
人工智能/
支持向量机/
地震目录

Abstract:Facing massive seismic data, it is urgent to automatically detect earthquakes and determine their arrival times accurately. Based on the support vector machine technology, we developed a method by using two classifiers SSD and SPS to automatically identify seismic body-wave phases and automatically determine their arrival times. Compared with the traditional automatic phase-picking methods, our method can more accurately identify both the seismic phases from noises, and the S phases from P phases. Moreover, we employ the array strategy to further effectively improve the accuracy of phase-detection.
Key words:Seismic phase detection/
Artificial intelligence/
Support vector machine/
Earthquake catalogue



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