Performance evaluation of a standalone PCA-based denoising method for Distributed Acoustic Sensing (DAS) data
DOI:
https://doi.org/10.24036/teknomekanik.v9i2.44372Keywords:
denoising algorithm, distributed acoustic sensing, PCA performance, principal component analysisAbstract
This paper experimentally evaluates the effectiveness of Principal Component Analysis (PCA) for denoising distributed acoustic sensing (DAS) data. Experiments were conducted by applying different vibration strengths using a piezo-electric transducer (PZT) at various sensing locations along the sensing fiber. Unlike existing hybrid PCA-based DAS denoising approaches, this work explicitly investigates PCA as a standalone denoising framework, addressing the lack of systematic evaluation of its effectiveness and practical applicability. Results show that PCA improves the signal-to-noise ratio (SNR) by at least 4.7 dB across a range of strain levels. The SNR also shows improvements exceeding 5 dB for sensing fiber lengths up to ~5.2 km. For ~10.2 km vibration location, PCA still achieved around 2.45 dB of SNR improvement. The PCA algorithm was then compared with traditional denoising algorithms, i.e., Moving Average, Low-Pass Filtering, and Wavelet Denoising, at a fixed sensing fiber length of 3.2 km and 2 Vpp applied to the PZT. PCA outperformed these approaches in noise reduction while maintaining moderate computational cost. Overall, PCA effectively suppresses background noise while preserving the integrity of the vibration signal. These results indicate that standalone PCA is a practical denoising option for DAS applications that require improved SNR at a moderate processing cost.
References
F. Muñoz and M. A. Soto, “Enhancing fibre-optic distributed acoustic sensing capabilities with blind near-field array signal processing,” Nat. Commun., vol. 13, no. 1, p. 4019, Jul. 2022, https://doi.org/10.1038/s41467-022-31681-x
[A. Lellouch and B. L. Biondi, “Seismic Applications of Downhole DAS,” Sensors, vol. 21, no. 9, 2021, https://doi.org/10.3390/s21092897
T. Okamoto, D. Iida, and Y. Koshikiya, “Distributed Acoustic Sensing of Seismic Wave Using Optical Frequency Domain Reflectometry,” Journal of Lightwave Technology, vol. 41, no. 22, pp. 7036–7044, 2023, https://doi.org/10.1109/JLT.2023.3297608
B. G. Gorshkov, A. E. Alekseev, M. A. Taranov, D. E. Simikin, V. T. Potapov, and D. A. Ilinskiy, “Low noise distributed acoustic sensor for seismology applications,” Appl. Opt., vol. 61, no. 28, p. 8308, 2022, https://doi.org/10.1364/ao.468804
S. Dou et al., “Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study,” Sci. Rep., vol. 7, no. 1, p. 11620, 2017, https://doi.org/10.1038/s41598-017-11986-4
F. Walter et al., “Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain,” Nat. Commun., vol. 11, no. 1, p. 2436, 2020, https://doi.org/10.1038/s41467-020-15824-6
H. Wijaya, P. Rajeev, and E. Gad, “Distributed optical fibre sensor for infrastructure monitoring: Field applications,” Optical Fiber Technology, vol. 64, p. 102577, 2021, https://doi.org/https://doi.org/10.1016/j.yofte.2021.102577
M. Jia et al., “Pipeline Leaks Early Warning Based on Distributed Optical Fiber Sensing Technology,” IEEE Photonics Technology Letters, vol. 36, no. 6, pp. 397–400, 2024, https://doi.org/10.1109/LPT.2024.3362913
C. Huang, F. Peng, and K. Liu, “Pipeline Inspection Gauge Positioning System Based on Optical Fiber Distributed Acoustic Sensing,” IEEE Sens. J., vol. 21, no. 22, pp. 25716–25722, 2021, https://doi.org/10.1109/JSEN.2021.3116973
J. Zuo et al., “Pipeline Leak Detection Technology Based on Distributed Optical Fiber Acoustic Sensing System,” IEEE Access, vol. 8, pp. 30789–30796, 2020, https://doi.org/10.1109/ACCESS.2020.2973229
X. Liu, Y. Wang, B. Jin, Q. Ying, D. Wang, and Y. Wang, “Real-time distributed oil/gas pipeline security pre warning system based on Φ-OTDR,” Progress in Electromagnetics Research Symposium, vol. 2017-Novem, pp. 1717–1719, 2017, https://doi.org/10.1109/PIERS-FALL.2017.8293412
L. Ren, T. Jiang, Z. guang Jia, D. sheng Li, C. lin Yuan, and H. nan Li, “Pipeline corrosion and leakage monitoring based on the distributed optical fiber sensing technology,” Measurement (Lond)., 2018, https://doi.org/10.1016/j.measurement.2018.03.018
S. C. Huang, W. W. Lin, M. T. Tsai, and M. H. Chen, “Fiber optic in-line distributed sensor for detection and localization of the pipeline leaks,” Sens. Actuators A Phys., vol. 135, no. 2, pp. 570–579, 2007, https://doi.org/10.1016/j.sna.2006.10.010
Y. Pan et al., “Development of a distributed polarization-OTDR to measure two vibrations with the same frequency,” 2015 International Conference on Optical Instruments and Technology: Optical Sensors and Applications, vol. 9620, p. 962002, 2015, https://doi.org/10.1117/12.2193409
H. Liu, J. Ma, T. Xu, W. Yan, L. Ma, and X. Zhang, “Vehicle Detection and Classification Using Distributed Fiber Optic Acoustic Sensing,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1363–1374, 2020, https://doi.org/10.1109/TVT.2019.2962334
D. Tu, S. Xie, Z. Jiang, and M. Zhang, “Ultra long distance distributed fiber-optic system for intrusion detection,” Advanced Sensor Systems and Applications V, vol. 8561, p. 85611W, 2012, https://doi.org/10.1117/12.2001292
Y. Zhan et al., “A distributed optical fiber sensor system for intrusion detection and location based on the phase-sensitive OTDR with remote pump EDFA,” Optik (Stuttg)., vol. 225, no. May 2020, p. 165020, 2021, https://doi.org/10.1016/j.ijleo.2020.165020
H. F. Taylor and C. E. Lee, “Method For Fiber Optic Intrusion Sensing. US5194847A,” no. 19, 1993. https://patents.google.com/patent/US5194847A/en
X. Yu, D. Zhou, B. Lu, S. Liu, and M. Pan, “Phase-sensitive optical time domain reflectometer for distributed fence-perimeter intrusion detection,” AOPC 2015: Optical Fiber Sensors and Applications, vol. 9679, no. 50, p. 96790S, 2015, https://doi.org/10.1117/12.2199685
J. C. Juarez and H. F. Taylor, “Distributed fiber optic intrusion sensor system,” in Optics InfoBase Conference Papers, 2005, pp. 2081–2087. https://doi.org/10.1109/JLT.2005.849924
X. Lu and K. Krebber, “Characterizing detection noise in phase-sensitive optical time domain reflectometry,” Opt. Express, vol. 29, pp. 18791–18806, 2021, https://doi.org/10.1364/OE.424410
K. Blotekjaer, “Fundamental noise sources that limit the ultimate resolution of fiber optic sensors,” in Optical and Fiber Optic Sensor Systems, S. Huang, K. D. Bennett, and D. A. Jackson, Eds., SPIE, 1998, pp. 1–12. https://doi.org/10.1117/12.318192
L. Zhao, X. Zhang, and Z. Xu, “A high-fidelity numerical model of coherent Φ-OTDR,” Measurement: Journal of the International Measurement Confederation, vol. 230. 2024. https://doi.org/10.1016/j.measurement.2024.114526
R. Rathod, R. Pechstedt, D. J. Webb, and D. A. Jackson, “Rayleigh backscattering in optical fibers: a noise limitation and a sensing mechanism,” in Tenth International Conference on Optical Fibre Sensors, B. Culshaw and J. D. C. Jones, Eds., SPIE, 1994, pp. 498–501. https://doi.org/10.1117/12.184972
T. Huang et al., “Multiple noise reduction for distributed acoustic sensing data processing through densely connected residual convolutional networks,” J. Appl. Geophy., vol. 228, p. 105464, 2024, https://doi.org/10.1016/j.jappgeo.2024.105464
M. Zabihi and K. Krebber, “Laser source frequency drift compensation in Φ-OTDR systems using multiple probe frequencies,” Optics Express, vol. 30, no. 11. p. 19990, 2022. https://doi.org/10.1364/oe.460302
A. Masoudi, T. Lee, M. Beresna, and G. Brambilla, “10-cm spatial resolution distributed acoustic sensor based on an ultra low-loss enhanced backscattering fiber,” Optics Continuum, vol. 1, no. 9, pp. 2002–2010, 2022, https://doi.org/10.1364/OPTCON.468673
Y. I. X. Iao et al., “Fading suppression and noise reduction of a DAS system integrated multi-core fiber,” vol. 32, no. 15, pp. 26793–26807, 2024, https://doi.org/10.1364/OE.528514
X. Zhu, S. Zhao, X. Li, R. Zhang, and M. Kong, “Optimization of the moving averaging–moving differential algorithm for Φ-OTDR,” Appl. Opt., vol. 61, no. 19, p. 5633, 2022, https://doi.org/10.1364/ao.461922
I. Ashry et al., “Normalized differential method for improving the signal-to-noise ratio of a distributed acoustic sensor,” Appl. Opt., vol. 58, no. 18, p. 4933, 2019, https://doi.org/10.1364/ao.58.004933
H. Wang, Y. Chen, R. Min, and Y. Chen, “Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring,” Sensors, vol. 22, no. 24, 2022, https://doi.org/10.3390/s22249976
Z. Qin, L. Chen, and X. Bao, “Wavelet Denoising Method for Improving Detection Performance of Distributed Vibration Sensor,” IEEE Photonics Technology Letters, vol. 24, no. 7, pp. 542–544, 2012, https://doi.org/10.1109/LPT.2011.2182643
T. Zhu, X. Xiao, Q. He, and D. Diao, “Enhancement of SNR and Spatial Resolution in $varphi$-OTDR System by Using Two-Dimensional Edge Detection Method,” Journal of Lightwave Technology, vol. 31, no. 17, pp. 2851–2856, 2013, https://doi.org/10.1109/JLT.2013.2273553
M. P. Isken, H. Vasyura-Bathke, T. Dahm, and S. Heimann, “De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter,” Geophys. J. Int., vol. 231, no. 2, pp. 944–949, 2022, https://doi.org/10.1093/gji/ggac229
Y. Tian, J. Sui, Y. Li, N. Wu, and D. Shao, “A Novel Iterative PA-MRNet: Multiple Noise Suppression and Weak Signals Recovery for Downhole DAS Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022, https://doi.org/10.1109/TGRS.2022.3170635
H. Zhao, T. Bai, and Y. Chen, “Background Noise Suppression for DAS-VSP Records Using GC-AB-Unet,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, https://doi.org/10.1109/LGRS.2022.3211399
M. Wang, L. Deng, Y. Zhong, J. Zhang, and F. Peng, “Rapid Response DAS Denoising Method Based on Deep Learning,” Journal of Lightwave Technology, vol. 39, no. 8, pp. 2583–2593, 2021, https://doi.org/10.1109/JLT.2021.3052651
H. Ma, S. Zhou, Y. Wang, N. Wu, Y. Li, and Y. Tian, “Improving Distributed Acoustic Sensing Data Quality With Self-Supervised Learning,” IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1–5, 2024, https://doi.org/10.1109/LGRS.2024.3400836
A. D. Atia Ibrahim, S. Lin, J. Xiong, J. Jiang, Y. Fu, and Z. Wang, “The application of PCA on Փ-OTDR sensing system for vibration detection,” in 2019 18th International Conference on Optical Communications and Networks (ICOCN), IEEE, Aug. 2019, pp. 1–3. https://doi.org/10.1109/ICOCN.2019.8934672
A. D. A. Ibrahim, S. Lin, J. Xiong, J. Jiang, Y. Fu, and Z. Wang, “Integrated principal component analysis denoising technique for phase-sensitive optical time domain reflectometry vibration detection,” Appl. Opt., vol. 59, no. 3, p. 669, 2020, https://doi.org/10.1364/ao.59.000669
H. Wu et al., “The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor,” IEEE Sens. J., vol. 23, no. 19, pp. 22944–22951, 2023, https://doi.org/10.1109/JSEN.2023.3305532
Y. Lu, T. Zhu, L. Chen, and X. Bao, “Distributed vibration sensor based on coherent detection of phase-OTDR,” Journal of Lightwave Technology, vol. 28, no. 22, pp. 3243–3249, 2010, https://doi.org/10.1109/JLT.2010.2078798
H. Gabai and A. Eyal, “On the sensitivity of distributed acoustic sensing,” Opt. Lett., vol. 41, no. 24, pp. 5648–5651, Dec. 2016, https://doi.org/10.1364/OL.41.005648
A. H. Hartog, An Introduction to Distributed Optical Fibre Sensors, 1st ed. CRC Press, 2017. https://doi.org/10.1201/9781315119014
K. E. Haavik, “On the Use of Low-Frequency Distributed Acoustic Sensing Data for In-Well Monitoring and Well Integrity: Qualitative Interpretation,” SPE Journal, vol. 28, no. 03, pp. 1517–1532, 2023, https://doi.org/10.2118/212868-PA
M. S. Yusri et al., “Characterization of DWT as Denoising Method for φ-OTDR Signal,” International Journal of Nanoelectronics and Materials, vol. 14, no. Special Issue InCAPE. pp. 333–340, 2021. https://ijneam.unimap.edu.my/images/PDF/InCAPE2021/Vol_14_SI_Dec_2021_325-332.pdf
A. T. Turov, F. L. Barkov, Y. A. Konstantinov, D. A. Korobko, C. A. Lopez-Mercado, and A. A. Fotiadi, “Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors,” Algorithms, vol. 16, no. 9, 2023, https://doi.org/10.3390/a16090440
A. T. Turov et al., “Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application,” Algorithms, vol. 16, no. 5, p. 217, Apr. 2023, https://doi.org/10.3390/a16050217
H. He et al., “SNR Enhancement in Phase-Sensitive OTDR with Adaptive 2-D Bilateral Filtering Algorithm,” IEEE Photonics J., vol. 9, no. 3, pp. 1–10, 2017, https://doi.org/10.1109/JPHOT.2017.2700894
K. Naeem, B. H. Kim, D. J. Yoon, and I. B. Kwon, “Enhancing detection performance of the phase‐sensitive otdr based distributed vibration sensor using weighted singular value decomposition,” Applied Sciences (Switzerland), vol. 11, no. 4, pp. 1–12, 2021, https://doi.org/10.3390/app11041928
G. Xiang, A. Sun, Y. Liu, and L. Gao, “Optical Fiber Technology An improved denoising method for -OTDR signal based on the combination of temporal local GMCC and ICEEMDAN-WT,” Optical Fiber Technology, vol. 87, no. September, p. 103949, 2024, https://doi.org/10.1016/j.yofte.2024.103949
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