Abstract
In this paper, we propose and experimentally demonstrate a scheme of deep learning enhanced long-range fast Brillouin optical time-domain analysis (BOTDA). The volumetric data from fast BOTDA is denoised and demodulated by using a deep video denoising network and a deep neural network, respectively. Benefitting from the advanced deep learning algorithms, the sensing range of fast BOTDA is extended to 10 km successfully. In experiment, vibration signal is measured with a sampling rate of 23 Hz, 2 m spatial resolution, and 1.19 MHz accuracy over 10 km single-mode fiber with only 4 averages. Due to the low computational complexity and GPU acceleration, the network takes less than 0.04 s to process 100 × 21800 data, which is much faster than the conventional algorithms. This method provides the potential for real-time vibration measurement in fast BOTDA with long sensing range.
PDF Article
More Like This
Towards fast sensing along ultralong BOTDA: flatness enhancement by utilizing injection-locked dual-bandwidth probe wave
Yulian Yang, Liming Liu, Qingxue Deng, Xinhong Jia, Han Wu, Wenyan Liang, Li Jiang, Weijie Song, Huiliang Ma, Jiabing Lin, and Shirong Xu
Opt. Express 30(12) 20501-20514 (2022)
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription