4.6 Article

Eliminating Phase Drift for Distributed Optical Fiber Acoustic Sensing System with Empirical Mode Decomposition

期刊

SENSORS
卷 19, 期 24, 页码 -

出版社

MDPI
DOI: 10.3390/s19245392

关键词

phase-sensitive optical time domain reflectometry; distributed acoustic sensing system; phase drift elimination; empirical mode decomposition; phase recovery

资金

  1. National Natural Science Foundation of China [61975142]
  2. Key Research and Development (R&D) Projects of Shanxi Province [201803D121071]
  3. Social Development Project of Shanxi Province Key Research Plan [201703D321037]
  4. Coal-Bed Methane Joint Research Fund of Shanxi Province [2015012005, 2016012011]
  5. Natural Science Foundation of Shanxi Province [201701D221115]
  6. Shanxi Scholarship Council of China [2016-035]

向作者/读者索取更多资源

Phase-drift elimination is crucial to vibration recovery in the coherent detection phase-sensitive optical time domain reflectometry system. The phase drift drives the whole phase signal fluctuation as a baseline, and its negative effect is obvious when the detection time is long. In this paper, empirical mode decomposition (EMD) is presented to extract and eliminate the phase drift adaptively. It decomposes the signal by utilizing the characteristic time scale of the data, and the baseline is eventually obtained. It is validated by theory and experiment that the phase drift deteriorates seriously when the length of the vibration region increases. In an experiment, the phase drift was eliminated under the conditions of different vibration frequencies of 1 Hz, 5 Hz, and 10 Hz. The phase drift was also eliminated with different vibration intensities. Furthermore, the linear relationship between phase and vibration intensity is demonstrated with a correlation coefficient of 99.99%. The vibrations at 0.5 Hz and 0.3 Hz were detected with signal-to-noise ratios (SNRs) of 55.58 dB and 64.44 dB. With this method, when the vibration frequency is at the level of Hz or sub-Hz, the phase drift can be eliminated. This contributes to the detection and recovery of low-frequency perturbation events in practical applications.

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