Abstract
Distributed acoustic sensing (DAS) is a fiber sensing technology based on Rayleigh scattering, which transforms optical fiber into a series of sensing units. It has become an indispensable part in the field of seismic monitoring, vehicle tracking, and pipeline monitoring. Fiber Rayleigh scattering responses lay at the core of DAS. However, there are few in-depth studies on the purpose of acquiring fiber Rayleigh scattering responses. In this paper, we establish a deep learning framework based on the bidirectional gated recurrent unit, which is the first time to predict the fiber Rayleigh scattering responses, to the best of our knowledge. The deep learning framework is trained with a numerical simulation dataset only, but it can process experimental data successfully. Moreover, since the responses could have a wider effective bandwidth than the experimental probing pulses, a finer spatial resolution could be obtained after demodulation. This work indicates that the deep learning framework can capture the characteristics of the fiber Rayleigh scattering responses effectively, which paves the way for intelligent DAS.
References
Fernández-Ruiz M R, Soto M A, Williams E F, et al. Distributed acoustic sensing for seismic activity monitoring. APL Photonics, 2020, 5: 030901
Liu H Y, Ma J H, Xu T W, et al. Vehicle detection and classification using distributed fiber optic acoustic sensing. IEEE Trans Veh Technol, 2020, 69: 1363–1374
Li Z Q, Zhang J W, Wang M N, et al. Fiber distributed acoustic sensing using convolutional long short-term memory network: a field test on high-speed railway intrusion detection. Opt Express, 2020, 28: 2925
Li G, Zeng K H, Zhou B, et al. Vibration monitoring for the West-East Gas Pipeline Project of China by phase optical time domain reflectometry (phase-OTDR). Instrum Sci Tech, 2021, 49: 65–80
Bao X Y, Wang Y. Recent advancements in Rayleigh scattering-based distributed fiber sensors. Adv Dev Instrum, 2021, 2021: 8696571
Cranch G A, Nash P J. High-responsivity fiber-optic flexural disk accelerometers. J Lightwave Technol, 2000, 18: 1233–1243
Liang Y X, Wang Z N, Lin S T, et al. Optical-pulse-coding phase-sensitive OTDR with mismatched filtering. Sci China Inf Sci, 2022, 65: 192303
Wang Z N, Zhang L, Wang S, et al. Coherent Φ-OTDR based on I/Q demodulation and homodyne detection. Opt Express, 2016, 24: 853
Jiang J L, Wang Z N, Wang Z T, et al. Coherent Kramers-Kronig receiver for Φ-OTDR. J Lightwave Technol, 2019, 37: 4799–4807
Park J, Lee W, Taylor H F. Fiber optic intrusion sensor with the configuration of an optical time-domain reflectometer using coherent interference of Rayleigh backscattering. In: Proceedings of Photonics China’98, Beijing, 1998. 49–56
Healey P. Fading in heterodyne OTDR. Electron Lett, 1984, 20: 30
Zhang X Z, Sun H N, Jiang J F, et al. Optical time-series signals classification based on data augmentation for small sample. Sci China Inf Sci, 2022, 65: 229303
Shiloh L, Eyal A, Giryes R. Efficient processing of distributed acoustic sensing data using a deep learning approach. J Lightwave Technol, 2019, 37: 4755–4762
Huang M F, Ji P, Wang T, et al. First field trial of distributed fiber optical sensing and high-speed communication over an operational telecom network. J Lightwave Technol, 2020, 38: 75–81
Wang M N, Deng L, Zhong Y Z, et al. Rapid response DAS denoising method based on deep learning. J Lightwave Technol, 2021, 39: 2583–2593
Jiang F, Zhang Z H, Lu Z X, et al. High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning. Opt Express, 2021, 29: 33467
Liu T, Li H, He T, et al. Ultra-high resolution strain sensor network assisted with an LS-SVM based hysteresis model. Opto-Electron Adv, 2021, 4: 200037
Liehr S, Borchardt C, Münzenberger S. Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers. Opt Express, 2020, 28: 39311
Liehr S, Jäger L A, Karapanagiotis C, et al. Real-time dynamic strain sensing in optical fibers using artificial neural networks. Opt Express, 2019, 27: 7405
Wang Y F, Liu Q W, Li B Z, et al. Boosting the data processing speed by artificial neural network in distributed fiber-optic sensor. In: Proceedings of Optical Fiber Sensors Conference 2020 Special Edition, Washington, 2021
Li H, Fan C Z, Liu T, et al. Time-slot multiplexing based bandwidth enhancement for fiber distributed acoustic sensing. Sci China Inf Sci, 2021, 65: 119303
Liang Y X, Lin S T, Wang Z N, et al. Impulse response restoration of fiber Rayleigh scattering channel with double complementary pulses and deep learning. In: Proceedings of Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC), Beijing, 2020. 1–3
Liang Y X, Wang Z N, Lin S T, et al. Experimental demonstration of phase-sensitive OTDR with adaptive probe-pulse modulation. In: Proceedings of Optical Fiber Communication Conference (OFC), Washington, 2021
Karanov B, Chagnon M, Thouin F, et al. End-to-end deep learning of optical fiber communications. J Lightwave Technol, 2018, 36: 4843–4855
Wang F, Bian Y M, Wang H C, et al. Phase imaging with an untrained neural network. Light Sci Appl, 2020, 9: 77
Ravuri S, Lenc K, Willson M, et al. Skilful precipitation nowcasting using deep generative models of radar. Nature, 2021, 597: 672–677
Cho K, van Merrienboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, 2014. 1724–1734
Qian H, Luo B, He H J, et al. Phase demodulation based on DCM algorithm in Φ-OTDR with self-interference balance detection. IEEE Photon Technol Lett, 2020, 32: 473–476
Chen D, Liu Q W, He Z Y. Phase-detection distributed fiber-optic vibration sensor without fading-noise based on time-gated digital OFDR. Opt Express, 2017, 25: 8315
Wu Y, Wang Z N, Xiong J, et al. Interference fading elimination with single rectangular pulse in Φ-OTDR. J Lightwave Technol, 2019, 37: 3381–3387
Guerrier S, Dorize C, Awwad E, et al. Introducing coherent MIMO sensing, a fading-resilient, polarization-independent approach to Φ-OTDR. Opt Express, 2020, 28: 21081
Pastor-Graells J, Martins H F, Garcia-Ruiz A, et al. Single-shot distributed temperature and strain tracking using direct detection phase-sensitive OTDR with chirped pulses. Opt Express, 2016, 24: 13121
Chen D, Liu Q W, Wang Y F, et al. Fiber-optic distributed acoustic sensor based on a chirped pulse and a non-matched filter. Opt Express, 2019, 27: 29415
Xiong J, Wang Z N, Wu Y, et al. Single-shot COTDR using sub-chirped-pulse extraction algorithm for distributed strain sensing. J Lightwave Technol, 2020, 38: 2028–2036
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 62075030), National Ten-Thousand Talent Program (Grant No. W030211001001), and Sichuan Provincial Project for Outstanding Young Scholars in Science and Technology (Grant No. 2020JDJQ0024).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liang, Y., Sun, J., Zhang, J. et al. Prediction of fiber Rayleigh scattering responses based on deep learning. Sci. China Inf. Sci. 66, 222301 (2023). https://doi.org/10.1007/s11432-022-3734-0
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-022-3734-0