Open AccessLong-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers
Author(s) -
Sascha Liehr,
Christopher Borchardt,
Sven Münzenberger
Publication year - 2020
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.402789
Subject(s) - reflectometry , distributed acoustic sensing , computer science , backscatter (email) , optics , convolutional neural network , optical fiber , rayleigh scattering , noise reduction , brillouin scattering , attenuation , fiber optic sensor , artificial intelligence , time domain , telecommunications , physics , computer vision , wireless
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.