Fast Brillouin optical time domain sensing based on two-dimension sparse sampling and deep learning recovery

Abstract

Brillouin optical time domain sensing (BOTDS) is a technique for acquiring the Brillouin scattering spectrum (BSS) of a fiber under test (FUT) by utilizing multiple frequency scans. However, the time-consuming nature of this process limits its applicability in scenarios that require rapid measurements. In order to enhance measurement efficiency and decrease storage capacity, without compromising measurement accuracy or spatial resolution, we propose a novel fast BOTDS method based on two-dimensional (2D) sparse sampling and residual channel attention network (RCAN). The proposed method employs sparse sampling to measure the BSS signal, which is then accurately restored to the BSS with normal frequency sampling intervals by using deep learning techniques. Temperature and strain information is extracted from the restored BSS, facilitating rapid and precise measurements. The effectiveness of the proposed method has been verified through simulation and experiments. In a proof-of-concept experiment, we employed a 3000 m bend-insensitive fiber as the sensing fiber and reduced the sampling frequency by a factor of 10. Our proposed method successfully recovered the Brillouin scattering spectrum signal, resulting in a temperature measurement uncertainty of 0.25 °C. Moreover, the measurement accuracy and spatial resolution almost remained unaffected, surpassing that of interpolation-based approaches.