Deep learning enhanced long-range fast BOTDA for vibration measurement

Hua Zheng, Yaxi Yan, Yuyao Wang, Xingliang Sben, Chao Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In this paper, we propose and experimentally demonstrate a scheme of deep learning enhanced long-range fast Brillouin optical time-domain analysis (BOTDA). The volumetric data from fast BOTDA is denoised and demodulated by using a deep video denoising network and a deep neural network, respectively. Benefitting from the advanced deep learning algorithms, the sensing range of fast BOTDA is extended to 10 km successfully. In experiment, vibration signal is measured with a sampling rate of 23 Hz, 2 m spatial resolution, and 1.19 MHz accuracy over 10 km single-mode fiber with only 4 averages. Due to the low computational complexity and GPU acceleration, the network takes less than 0.04 s to process 100x21800 data, which is much faster than the conventional algorithms. This method provides the potential for real-time vibration measurement in fast BOTDA with long sensing range.

Original languageEnglish
JournalJournal of Lightwave Technology
Publication statusAccepted/In press - 2021


  • Brillouin optical time-domain analysis (BOTDA)
  • deep learning
  • Frequency measurement
  • Noise reduction
  • Optical fiber networks
  • Optical fiber sensors
  • Optical fibers
  • Signal to noise ratio
  • Training
  • ultra-fast measurement

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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