Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions

被引：1

作者：

Mus, Sergi ^{[1
]}

Gutierrez, Norma ^{[1
]}

Tous, Ruben ^{[1
]}

Otero, Beatriz ^{[1
]}

Cruz, Leonel ^{[1
]}

Llacer, David ^{[1
]}

Alvarado, Leonardo ^{[2
]}

Rojas, Otilio ^{[3
,4
]}

机构：

[1] Univ Politecn Catalunya UPC, Barcelona, Spain

[2] Fdn Venezolana Invest Sismol, Caracas 1070, Venezuela

[3] Univ Cent Venezuela, Fac Ciencias, Caracas, Venezuela

[4] Barcelona Supercomp Ctr BSC, Barcelona, Spain

来源：

MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE | 2019年 / 11943卷

关键词：

Earthquake detection; Neural networks; Deep learning; LSTM;

D O I：

10.1007/978-3-030-37599-7_62

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Bocono and La Victoria - San Sebastian faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.

引用

页码：751 / 754

页数：4

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