Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application
Abstract
:1. Introduction
2. Approach
2.1. Averaging and Moving Differential
2.2. Dynamic Spectrum Averaging
- A choice of scanning window with the length of w’, which allows fitting the most significant event on an OFDR trace consisting of N samples into it (Figure 4a);
- The process of spatial domain scanning using this window. During the scanning process, for all points from w’/2 to N − w’/2, the standard deviation σ of the signal values P at the points included in this window is calculated;
- Normalization of the obtained σ values to 1 (σ = σnorm);
- A filtered trace (Figure 4b) calculation according to
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Event Frequency, kHz | Event Type | Most Efficient Technique | SNR Increase, dB |
---|---|---|---|
10 | Pointwise | MD+FDDA | 5.2 |
3 | Pointwise with harmonics | FDDA | 6.6 |
2 | Distributed | SA+FDDA | 9.7 |
6.5 | Distributed | SA+FDDA | 13.1 |
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Turov, A.T.; Konstantinov, Y.A.; Barkov, F.L.; Korobko, D.A.; Zolotovskii, I.O.; Lopez-Mercado, C.A.; Fotiadi, A.A. Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application. Algorithms 2023, 16, 217. https://doi.org/10.3390/a16050217
Turov AT, Konstantinov YA, Barkov FL, Korobko DA, Zolotovskii IO, Lopez-Mercado CA, Fotiadi AA. Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application. Algorithms. 2023; 16(5):217. https://doi.org/10.3390/a16050217
Chicago/Turabian StyleTurov, Artem T., Yuri A. Konstantinov, Fedor L. Barkov, Dmitry A. Korobko, Igor O. Zolotovskii, Cesar A. Lopez-Mercado, and Andrei A. Fotiadi. 2023. "Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application" Algorithms 16, no. 5: 217. https://doi.org/10.3390/a16050217