基于支持向量机的地震体波震相自动识别及到时自动拾取

Yiran Jiang; Jieyuan Ning

doi:10.6038/cjg2019M0442

基于支持向量机的地震体波震相自动识别及到时自动拾取

Translated title of the contribution: Automatic detection of seismic body-wave phases and determination of their arrival times based on support vector machine

Yiran Jiang
, Jieyuan Ning^*

^*Corresponding author for this work

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development
Peking University

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

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.

Translated title of the contribution	Automatic detection of seismic body-wave phases and determination of their arrival times based on support vector machine
Original language	Chinese (Traditional)
Pages (from-to)	361-373
Number of pages	13
Journal	Acta Geophysica Sinica
Volume	62
Issue number	1
DOIs	https://doi.org/10.6038/cjg2019M0442
State	Published - 1 Jan 2019
Externally published	Yes

Access to Document

10.6038/cjg2019M0442

Cite this

@article{56742368fd424f33a9cf5540e52388e9,

title = "基于支持向量机的地震体波震相自动识别及到时自动拾取",

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.",

keywords = "Artificial intelligence, Earthquake catalogue, Seismic phase detection, Support vector machine",

author = "Yiran Jiang and Jieyuan Ning",

year = "2019",

month = jan,

day = "1",

doi = "10.6038/cjg2019M0442",

language = "繁体中文",

volume = "62",

pages = "361--373",

journal = "Acta Geophysica Sinica",

issn = "0001-5733",

publisher = "Science Press ",

number = "1",

}

TY - JOUR

T1 - 基于支持向量机的地震体波震相自动识别及到时自动拾取

AU - Jiang, Yiran

AU - Ning, Jieyuan

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Earthquake catalogue

KW - Seismic phase detection

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/85063033973

U2 - 10.6038/cjg2019M0442

DO - 10.6038/cjg2019M0442

M3 - 文章

AN - SCOPUS:85063033973

SN - 0001-5733

VL - 62

SP - 361

EP - 373

JO - Acta Geophysica Sinica

JF - Acta Geophysica Sinica

IS - 1

ER -

基于支持向量机的地震体波震相自动识别及到时自动拾取

Abstract

Access to Document

Other files and links

Fingerprint

Cite this