Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing

被引:49
作者
Hernandez, Pablo D. [1 ]
Ramirez, Jaime A. [2 ]
Soto, Marcelo A. [1 ]
机构
[1] Univ Tecn Federico Santa Maria, Dept Elect Engn, Valparaiso 2390123, Chile
[2] Novelcode SpA, Vina Del Mar 2580216, Chile
关键词
Optical fiber sensors; Optical fibers; Seismic measurements; Optical fiber networks; Earthquakes; Acoustic measurements; Deep learning; Distributed acoustic sensing; earthquake detection; optical fiber sensors; machine learning; NETWORK;
D O I
10.1109/JLT.2021.3138724
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, deep learning models trained with real seismic data are proposed and proven to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The proposed neural network architectures cover the three classical deep learning paradigms: fully connected artificial neural networks (FC-ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Results demonstrate that training these networks with seismic waveforms measured by traditional broadband seismometers can extract and learn relevant features of earthquakes, enabling the reliable detection of seismic waves in DAS measurements. The intrinsic differences between DAS and seismograph waveforms, and eventual errors in the labelling of the DAS data, slightly reduce the performance of the models when tested with the distributed acoustic measurements. Despites of that, trained models can still reach up to 96.94% accuracy in the case of CNN and 93.86% in the case of CNN+RNN. The method and results here reported could represent an important contribution to the development of an early warning earthquake system based on DAS technology.
引用
收藏
页码:2639 / 2650
页数:12
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