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Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods.

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Summary
This summary is machine-generated.

This study introduces a new method to assess non-uniform responses in optical fiber systems. Machine learning significantly improves accuracy in measuring phase differences, enhancing reflectometric system performance.

Keywords:
chirped-OTDRdistributed fiber optic sensingmachine learningphase demodulation

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Area of Science:

  • Metrology
  • Optical Fiber Sensing
  • Machine Learning Applications

Background:

  • Phase-sensitive reflectometric systems face challenges with non-uniform responses along optical fibers.
  • The stochastic nature of Rayleigh reflectors complicates accurate measurements in fiber optic sensing.

Purpose of the Study:

  • To investigate the metrological properties of phase-sensitive reflectometric systems.
  • To develop and evaluate a methodology for assessing response non-uniformity in optical fibers.
  • To compare the efficacy of cross-correlation and machine learning algorithms for this assessment.

Main Methods:

  • Utilized chirped-reflectometry as an example system for analysis.
  • Simulated deformation impacts by altering light source wavelength.
  • Employed cross-correlation algorithms and neural network-based machine learning approaches.
  • Experimentally estimated response non-uniformity.

Main Results:

  • Neural network algorithms demonstrated over 34% improvement in phase difference measurement accuracy compared to cross-correlation.
  • The proposed methodology effectively evaluates response non-uniformity across different fiber sections.
  • Chirped-reflectometry was used to quantify these non-uniformities.

Conclusions:

  • The developed methodology provides an effective means to evaluate response non-uniformity in optical fibers.
  • Machine learning offers a significant advantage in improving the accuracy of phase-sensitive reflectometric measurements.
  • This research contributes to enhanced metrological properties of fiber optic sensing systems.