Laser & Optoelectronics Progress, Volume. 62, Issue 3, 0300002(2025)

Advances in Machine-Learning Techniques for Distributed Fiber-Optic Sensing Performance Enhancement

Dingyi Ma1...2,*, Xinyu Liu1,2, Yongzheng Li2,3, Linfeng Guo1,2,4, and Xiaomin Xu45 |Show fewer author(s)
Author Affiliations
  • 1School of Physics and Optoelectronic Engineering, Nanjing University of Information Science & Technology, Nanjing , 210044, Jiangsu , China
  • 2Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing , 210044, Jiangsu , China
  • 3China Railway No.3 Group East China Construction Co., Ltd., Nanjing 211153, Jiangsu , China
  • 4Jiangsu International Joint Laboratory on Meterological Photonics and Optoelectronic Detection, Nanjing 210044, Jiangsu , China
  • 5Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
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    Distributed fiber-optic sensing technology can detect system parameters such as temperature, strain, and vibration by demodulating the change in scattered light in optical fiber to achieve distributed measurement. The weak scattered-light signal results in low signal-to-noise ratios for a system and limited sensing performance. In recent years, researchers have focused on enhancing the performance of distributed fiber-optic sensing systems using machine learning. Herein, from the perspectives of data extraction, noise removal, and resolution enhancement, the progress of machine-learning technology in the research and development of distributed fiber-optic sensing in the past decade is described comprehensively, and an analysis and comparison with technology outlook is performed. Different types of machine-learning techniques offer different degrees of improvement to the processing speed. Among them, denoising neural networks are more effective in improving the signal-to-noise ratio, as shown by their superior noise suppression and ability to further improve the signal quality. Additionally, convolutional neural networks are outstanding in terms of improving spatial resolutions and can capture and resolve complex spatial features more accurately. These results are expected to further promote the subsequent in-depth application of machine-learning techniques to various distributed fiber-optic sensing technologies.

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    Dingyi Ma, Xinyu Liu, Yongzheng Li, Linfeng Guo, Xiaomin Xu. Advances in Machine-Learning Techniques for Distributed Fiber-Optic Sensing Performance Enhancement[J]. Laser & Optoelectronics Progress, 2025, 62(3): 0300002

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    Paper Information

    Category: Reviews

    Received: Apr. 29, 2024

    Accepted: Jun. 17, 2024

    Published Online: Feb. 21, 2025

    The Author Email:

    DOI:10.3788/LOP241191

    CSTR:32186.14.LOP241191

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