Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR
Brillouin optical time-domain reflectometry (BOTDR) is widely used for strain and temperature measurements in various fields. However, the accuracy and reliability of the measurements are often limited by the noise in the sensor signals. Dynamic measurement of BOTDR requires small averaging number a...
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IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10171342/ |
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| author | Bo Li Ningjun Jiang Xiaole Han |
| author_facet | Bo Li Ningjun Jiang Xiaole Han |
| author_sort | Bo Li |
| collection | DOAJ |
| description | Brillouin optical time-domain reflectometry (BOTDR) is widely used for strain and temperature measurements in various fields. However, the accuracy and reliability of the measurements are often limited by the noise in the sensor signals. Dynamic measurement of BOTDR requires small averaging number and fast measurement, and hence noise reduction is more significant in dynamic measurement. Small gain stimulated Brillouin scattering (SBS) can enhance the Brillouin signal power in BOTDR to realize dynamic measurement, but noise reduction is still important in system. In this work, we investigate the denoising of Brillouin gain spectrum (BGS) images using convolutional neural networks (DnCNN) to improve the accuracy of the small gain SBS STFT-BOTDR measurement of strain vibration. It is shown that the denoising of BGS images along the time axis can result in better detection of the strain vibration compared with denoising of BGS images along the fiber length. The denoising performance was evaluated using frequency uncertainties and R-squared values. The best denoising performance was achieved with a DnCNN network with 8 layers and 200 epochs, leading to a frequency uncertainty of 2.32 MHz and an R-squared value of 0.907. The frequency uncertainty is improved to about 45% of the original value. |
| format | Article |
| id | doaj-art-946c12e801ad487da3aaa0d6df427104 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-946c12e801ad487da3aaa0d6df4271042025-08-20T03:32:32ZengIEEEIEEE Photonics Journal1943-06552023-01-011541810.1109/JPHOT.2023.329146510171342Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDRBo Li0https://orcid.org/0009-0007-4820-436XNingjun Jiang1https://orcid.org/0000-0001-6070-4307Xiaole Han2https://orcid.org/0000-0002-9462-533XDepartment of Engineering, University of Cambridge, Cambridge, U.K.Institute of Geotechnical Engineering, Southeast University, Nanjing, ChinaDepartment of Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI, USABrillouin optical time-domain reflectometry (BOTDR) is widely used for strain and temperature measurements in various fields. However, the accuracy and reliability of the measurements are often limited by the noise in the sensor signals. Dynamic measurement of BOTDR requires small averaging number and fast measurement, and hence noise reduction is more significant in dynamic measurement. Small gain stimulated Brillouin scattering (SBS) can enhance the Brillouin signal power in BOTDR to realize dynamic measurement, but noise reduction is still important in system. In this work, we investigate the denoising of Brillouin gain spectrum (BGS) images using convolutional neural networks (DnCNN) to improve the accuracy of the small gain SBS STFT-BOTDR measurement of strain vibration. It is shown that the denoising of BGS images along the time axis can result in better detection of the strain vibration compared with denoising of BGS images along the fiber length. The denoising performance was evaluated using frequency uncertainties and R-squared values. The best denoising performance was achieved with a DnCNN network with 8 layers and 200 epochs, leading to a frequency uncertainty of 2.32 MHz and an R-squared value of 0.907. The frequency uncertainty is improved to about 45% of the original value.https://ieeexplore.ieee.org/document/10171342/Brillouin optical time-domain reflectometrydeep learningdistributed fiber optic sensorsimage denoising |
| spellingShingle | Bo Li Ningjun Jiang Xiaole Han Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR IEEE Photonics Journal Brillouin optical time-domain reflectometry deep learning distributed fiber optic sensors image denoising |
| title | Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR |
| title_full | Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR |
| title_fullStr | Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR |
| title_full_unstemmed | Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR |
| title_short | Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR |
| title_sort | denoising of brillouin gain spectrum images for improved dynamic measurements of botdr |
| topic | Brillouin optical time-domain reflectometry deep learning distributed fiber optic sensors image denoising |
| url | https://ieeexplore.ieee.org/document/10171342/ |
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