Noise reduction by Brillouin spectrum reassembly in Brillouin optical time domain sensors

Zhang, Yuyang; Lu, Yuangang; Zhang, Zelin; Wang, Jiming; He, Chongjun; Wu, Tong

doi:10.1016/j.optlaseng.2019.105865

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…The AM n change caused by each NLM parameter is 9.55%, 0.05%, and −0.04%, respectively, which indicates that the decrease in AM is mainly caused by the GSP. It is worth noting that if all the NLM parameters are optimal, the FWHM of the denoised trace only expands by only 2.56% compared with that of the clean trace, which is much less than the previous results [13,14,19,20,22]. The total signal amplitude is reduced by 9.25% compared with the clean trace.…”

Section: Discussionmentioning

confidence: 62%

“…Therefore, various methods, such as wavelet denoising, Bilateral filter, Block Matching and 3D filtering, and video-Block Matching and 3D filtering, have been attempted to improve the SR besides the measurement accuracy (MA) [5,[8][9][10]. Recent studies have shown that the SR for ideal data and original noisy data is 0.4 m, and it retains 0.4 m by Brillouin spectrum-reassembly filter, but the SR deteriorates to 1.5 m by NLM filter [19]. The combination of NLM and Gaussian filter can effectively improve the performance of the distributed Brillouin fiber sensor compared to the results obtained with NLM, enabling a 22% improvement in MA in the non-transition section and an 84% improvement in SR in the transition section [20].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Chen

Zhu

et al. 2023

Photonics

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Non-local mean (NLM) can significantly improve the signal-to-noise ratio (SNR), but it inevitably reduces the spatial resolution of distributed optical fiber sensors (DFOS), which hinders its practical application and the improvement of DFOS performance. In this paper, the quantitative relationship between the signal broadening of a phase-sensitive optical time-domain reflectometer (φ-OTDR) and the NLM parameters is analyzed to identify the cause and extent of the spatial resolution degradation. The denoising results for the mimic periodic and φ-OTDR vibration signals indicate that the signal broadening is mainly due to the similarity window size of NLM, and the signal amplitude reduction is caused by the Gaussian smoothing parameter. Compared with the reference signals, the signal broadening of the mimic and φ-OTDR signals after denoising are 2.56% and 2.74%, respectively, which is much less than the previous results. The signal amplitude is reduced by 9.25% and 13.62%, respectively. This work promotes the application of NLM and improves the performance of DFOS.

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Section: Discussionmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Chen

Zhu

et al. 2023

Photonics

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“…As can be seen from formula (1), the response frequency of BOTDR mainly has five constraints: Factor one is the key to the distributed positioning of the system, which cannot be improved due to material limitation; Factor 2: Many researchers have proposed schemes such as pulse coding for the signal-to-noise ratio of the system to reduce the cumulative number of noise reduction [10] . Factor three exists in the sweeping frequency method, which is difficult to improve due to industrial conditions, but it can be ignored for time-frequency analysis, slope assistance and other technical solutions that do not require sweeping frequency.…”

Section: Principle and Theoretical Analysismentioning

confidence: 99%

Research on Brillouin optical time domain reflection technology based on slope assistance

Zheng,

Yang,

et al. 2023

AOPC 2023: Optic Fiber Gyro

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Traditional Brillouin optical time domain reflectometer (BOTDR) generally uses frequency scanning to obtain the Brillouin gain spectrum. And the measurement speed of the sweeping frequency (SF) method is slow, usually on the order of minutes. In this paper, the mechanism of rapid BOTDR measurement based on sloped-assisted (SA) technology is analyzed, the measurement frequency of SA-BOTDR is theoretically calculated, and the system scheme is proposed and designed. Through experimental research, the temperature measurement range of the system is 25°C~70°C at the end of the 203m sensing fiber. The temperature measurement accuracy is 1.38°C, the spatial resolution is 1.21 m, and the measurement frequency is 11.49 Hz.

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“…At present, researchers of the Brillouin Spectrum denoising algorithm take advantage of the characteristics of the BOTDA system's BGS data points as pixels in the image, and use image-processing algorithms for denoising to improve the SNR of the BOTDA system and enhance the precision of the BFS extraction, so as to obtain more accurate distributed temperature measurement values [20][21][22]. The image-processing algorithm, Non-Local Means (NLM), is a widely used denoising method, which uses the non-local self-similarity of an image to collect the most similar image blocks and perform a weighted average to obtain the current pixel value, so that the denoising effect is greatly improved.…”

Section: Related Workmentioning

confidence: 99%

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

Lou,

Wang,

Sima

et al. 2023

Energies

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The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.

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Noise reduction by Brillouin spectrum reassembly in Brillouin optical time domain sensors

Cited by 11 publications

References 31 publications

Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Quantitative Analysis of φ-OTDR Spatial Resolution Influenced by NLM Parameters

Research on Brillouin optical time domain reflection technology based on slope assistance

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

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