Seismic Data Classification using Machine Learning

被引：11

作者：

Li, Wenrui ^{[1
]}

Nakshatra ^{[2
]}

Narvekar, Nishita ^{[2
]}

Raut, Nitisha ^{[2
]}

Sirkeci, Birsen ^{[2
]}

Gao, Jerry ^{[2
]}

机构：

[1] Nanjing Xiao Zhuang Univ, Sch Informat Engn, Nanjing, Peoples R China

[2] San Jose State Univ, Dept Software Engn, San Jose, CA 95192 USA

来源：

2018 IEEE FOURTH INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (IEEE BIGDATASERVICE 2018) | 2018年

关键词：

Earthquake; Seismic waveform; S and P waves; Machine learning; Epicenter; Noise removal; obspy; SVM; Decision Tree; Random forest;

D O I：

10.1109/BigDataService.2018.00017

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Earthquakes around the world have been a cause of major destruction and loss of life and property. An early detection and prediction system using machine learning classification models can prove to be very useful for disaster management teams. The earthquake stations continuously collect data even when there is no event. From this data, we need to distinguish earthquake and non-earthquake. Machine learning techniques can be used to analyze continuous time series data to detect earthquakes effectively. Furthermore, the earthquake data can be used to predict the P-wave and S-wave arrival times.

引用

页码：56 / 63

页数：8

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