Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers
| Autor: | Sascha Liehr, Christopher Borchardt, Sven Münzenberger |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Optical fiber
Backscatter Computer science Noise reduction Acoustics 02 engineering and technology 01 natural sciences Convolutional neural network law.invention 010309 optics symbols.namesake Optics Fiber Bragg grating law 0103 physical sciences Time domain Rayleigh scattering Reflectometry Signal processing business.industry 021001 nanoscience & nanotechnology Atomic and Molecular Physics and Optics Fiber optic sensor Computer Science::Computer Vision and Pattern Recognition symbols 0210 nano-technology Raman spectroscopy business |
| Zdroj: | Optics Express. 28:39311 |
| ISSN: | 1094-4087 |
| Popis: | A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|