Journal of Radio Electronics. eISSN 1684-1719. 2023. ¹12
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DOI: https://doi.org/10.30898/1684-1719.2023.12.16

 

APPLICATION OF MACHINE LEARNING

FOR PHASE DETECTION IN COHERENT OPTICAL REFLECTOMETER

 

 

V.A. Yatseev, O.V. Butov

 

 

Kotelnikov IRE RAS

125009, Russia, Moscow, Mokhovaya str., 11, b.7

 

The paper was received November 29, 2023.

 

Abstract. The work is aimed at solving the problem of phase detection in a phase-sensitive reflectometer when working with stochastic Rayleigh reflectors in the fiber. An improvement of measurement methods in coherent reflectometric systems, based on the use of machine learning algorithms, is proposed using a chirp reflectometer as an example. A chirp reflectometer with a tunable wavelength laser source was used to collect data necessary for training neural networks. The study shows that scanning by wavelength allows simulating various external effects, such as deformation or temperature changes over extended areas, thus ensuring efficient data collection for training. The use of even simple neural network algorithms leads to a significant increase in phase measurement accuracy by 43%, demonstrating the potential of this method for detecting phase of complex interferometric signals.

Key words: phase-sensitive reflectometry, OTDR (Optical Time-Domain Reflectometry), machine learning, phase detection, Rayleigh scattering.

Financing: The work was performed within the framework of the state assignment of the IRE named after V.A.Kotelnikov of the Russian Academy of Sciences.

Corresponding author: Yatseev Vasily Arturovich, yatseev@gmail.com

References

1. Juškaitis R., Mamedov A. M., Potapov V. T., Shatalin S. V. Interferometry with Rayleigh backscattering in a single-mode optical fiber // Optics Letters. 1994. – Vol. 19, ¹ 3. – P. 225. – DOI: https://doi.org/10.1364/ol.19.000225.

2. Liu S., Yu F., Hong R., Xu W., Shao L., Wang F. Advances in phase-sensitive optical time-domain reflectometry // Opto-Electronic Advances. 2022. – Vol. 5, ¹ 3. – P. 200078–200078. – DOI: https://doi.org/10.29026/oea.2022.200078.

3. Pnev A. B., Zhirnov A. A., Stepanov K. V., Nesterov E. T., Shelestov D. A., Karasik V. E. Mathematical analysis of marine pipeline leakage monitoring system based on coherent OTDR with improved sensor length and sampling frequency // Journal of Physics: Conference Series. 2015. – ¹ 584. – P. 012016. – DOI: https://doi.org/10.1088/1742-6596/584/1/012016.

4. Lindsey N. J. Geophysical Applications of ϕ-OTDR/DAS // Optical Fiber Communication Conference (OFC). 2023. – URL: http://dx.doi.org/10.1364/ofc.2023.w1j.1.

5. He M., Feng L., Fan J. A method for real-time monitoring of running trains using Ô-OTDR and the improved Canny // Optik. 2019. – Vol. 184. – P. 356–363. – DOI: https://doi.org/10.1016/j.ijleo.2019.04.112.

6. Cai Y., Ma J., Yan W., Zhang W., An Y. Aircraft detection using phase-sensitive optical-fiber OTDR // Sensors. 2021. – Vol. 21, ¹ 15. – P. 5094. – DOI: https://doi.org/10.3390/s21155094.

7. Yang N., Zhao Y., Chen J., Wang F. Real-time classification for Φ-OTDR vibration events in the case of small sample size datasets // Optical Fiber Technology. 2023. – Vol. 76. – P. 103217. – DOI: https://doi.org/10.1016/j.yofte.2022.103217.

8. He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. – DOI: http://doi.org/10.1109/CVPR.2016.90.

9. Chubchev E. D., Tomyshev K., Nechepurenko I., Dorofeenko A., Butov O. V. Machine learning approach to data processing of TFBG-assisted SPR sensors // Journal of Lightwave Technology. 2022. – Vol. 40, ¹ 9. – P. 3046–3054. – DOI: https://doi.org/10.1109/jlt.2022.3148533.

10. Shi Y., Wang Y., Zhao L., Fan Z. An event recognition method for Φ-OTDR sensing system based on deep learning // Sensors. 2019. – Vol. 19, ¹ 15. – P. 3421. – DOI: https://doi.org/10.3390/s19153421.

11. Peng F., Wu H., Jia X.-H., Rao Y.-J., Wang Z.-N., Peng Z.-P. Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines // Optics Express. 2014. – Vol. 22, ¹ 11. – P. 13804. – DOI: https://doi.org/10.1364/oe.22.013804.

12. Chen D., Liu Q., He Z. Distributed fiber-optic acoustic sensor with sub-nano strain resolution based on time-gated digital OFDR // Asia Communications and Photonics Conference. 2017. – DOI: https://doi.org/10.1364/ACPC.2017.S4A.2.

13. Yatseev V. A., Zotov A. M., Butov O. V. Combined frequency and phase domain time-gated reflectometry based on a fiber with reflection points for absolute measurements // Results in Physics. 2020. – Vol. 19. – DOI: https://doi.org/10.1016/j.rinp.2020.103485.

14. Zinsou R., Liu X., Wang Y., Zhang J., Wang Y., Jin B. Recent progress in the performance enhancement of phase-sensitive OTDR vibration sensing systems // Sensors. 2019. – Vol. 19, ¹ 7. – P. 1709. – DOI: https://doi.org/10.3390/s19071709.

15. Fernández-Ruiz M. R., Costa L., Martins H. F. Distributed acoustic sensing using chirped-pulse phase-sensitive OTDR technology // Sensors. 2019. – Vol. 19, ¹ 20. – P. 4368. – DOI: https://doi.org/10.3390/s19204368.

16. He H., Yan L., Qian H., Zhou Y., Zhang X., Luo B., Pan W., Fan X., He Z. Suppression of the interference fading in phase-sensitive OTDR with phase-shift transform // Journal of Lightwave Technology. 2021. – Vol. 39, ¹ 1. – P. 295–302. – DOI: https://doi.org/10.1109/jlt.2020.3023699.

17. Wang D., Zou J., Wang Y., Jin B., Bai Q., Liu X., Liu Y. Distributed optical fiber low-frequency vibration detecting using cross-correlation spectrum analysis // Journal of Lightwave Technology. 2020. – Vol. 38, ¹ 23. – P. 6664–6670. – DOI: https://doi.org/10.1109/jlt.2020.3016117.

18. Bhatta H. D., Costa L., Garcia-Ruiz A., Fernandez-Ruiz M. R., Martins H. F., Tur M., Gonzalez-Herraez M. Extending the measurement of true dynamic strain via chirped-pulse phase-sensitive optical time domain reflectometry to 100’s of microstrains // 26th International Conference on Optical Fiber Sensors. 2018. – URL: http://doi.org/10.1364/ofs.2018.wf14.

19. Goodfellow I., Bengio Y., Courville A. Deep Learning (Adaptive Computation and Machine Learning series) // The MIT Press 2016.

 

For citation:

Yatseev V.A., Butov O.V. Application of machine learning for phase detection in coherent optical reflectometer. // Journal of Radio Electronics. – 2023. – ¹. 12. https://doi.org/10.30898/1684-1719.2023.12.16 (In Russian)