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

• Published in: Journal of lightwave technology Vol. 40; no. 8; pp. 2639 - 2650
• Main Authors: Hernandez, Pablo D., Ramirez, Jaime A., Soto, Marcelo A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic measurement; Acoustic measurements; Acoustics; Artificial neural networks; Broadband; Computer architecture; Deep learning; Distributed acoustic sensing; Early warning systems; earthquake detection; Earthquakes; Feature extraction; Fiber optics; machine learning; Neural networks; Optical fiber networks; Optical fiber sensors; Optical fibers; Recurrent neural networks; Seismic measurements; Seismic waves; Waveforms
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2021.3138724

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.