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
收藏
页码：751 / 754
页数：4
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