High-sensitivity acoustic impedance sensing based on forward Brillouin scattering in a highly nonlinear fiber.

By using radial acoustic modes induced forward Brillouin scattering (FBS) in a highly nonlinear fiber (HNLF), to the best of our knowledge we have demonstrated acoustic impedance sensing with the sensitivity reaching beyond 3MHz for the first time. Benefiting from the high acousto-optical coupling efficiency, both radial acoustic modes (R0,m) and torsional-radial acoustic modes (TR2,m) induced FBS in HNLF have larger gain coefficient and scattering efficiency than those in standard single-mode fiber (SSMF). This provides better signal-to-noise ratio (SNR) and hence larger measurement sensitivity. By using R0,20 mode in HNLF, we have achieved a higher sensitivity of 3.83 MHz/[kg/(s · mm2)], in contrast to that of 2.70 MHz/[kg/(s · mm2)] when measured using R0,9 mode (with almost the largest gain coefficient) in SSMF. Meanwhile, with the use of the TR2,5 mode in HNLF, the sensitivity is measured to be 0.24 MHz/[kg/(s · mm2)], which is still 1.5 times larger than that reported when using the same mode in SSMF. The improved sensitivity would make the detection of the external environment by FBS based sensors more accurate.

Simultaneous sensing of temperature and strain with enhanced performance using forward Brillouin scattering in highly nonlinear fiber.

Simultaneous temperature and strain sensing has been demonstrated for the first time to our knowledge by using forward Brillouin scattering (FBS) in a highly nonlinear fiber (HNLF). It is based on different responses of radial acoustic modes R0,m and torsional-radial acoustic modes TR2,m to the temperature and strain. High-order acoustic modes with large FBS gain in an HNLF are chosen to improve the sensitivity. To reduce the measurement error, a method to select the best mode combination with the lowest measurement errors is proposed and demonstrated by both simulation and experiment. Three mode combinations have been used for both temperature and strain sensing, and by using the mode combination (R0,18, TR2,29), the lowest temperature and strain errors of 0.12°C/39 µÉ› have been achieved. Compared with sensors using backward Brillouin scattering (BBS), the proposed scheme only requires frequency measurement around 1 GHz, which is cost-effective without the need for a ∼10-GHz microwave source. Moreover, the accuracy is enhanced since the FBS resonance frequency and spectrum linewidth are much smaller than those of BBS.

Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system.

We have proposed and demonstrated a denoising and extraction convolutional neural network (DECNN) composed of 1D denoising convolutional autoencoder (DCAE) and 1D residual attention network (RANet) modules to extract temperature and strain simultaneously in a Brillouin optical time-domain analysis (BOTDA) system. With DCAE for high-fidelity denoising and RANet for accurate and robust information extraction, integrated denoising and extraction of both temperature and strain have been realized for the first time under a single CNN framework. Both simulation and experiment have been conducted to statistically analyze the performance of the proposed scheme and compare it with the conventional equation solving method (CESM), which show that DECNN has large noise tolerance and robustness over a wide range of temperature/strain and signal-to-noise ratio (SNR) conditions. The mean standard deviation (SD) and root mean square error (RMSE) of the temperature/strain extracted by DECNN over a wide range of SNRs are only 0.2°C/9.7µÉ› and 2°C/32.3µÉ› at the end of 19.38 km long sensing fiber, respectively. At a relatively low SNR of 8.8 dB, DECNN shows 196 times better temperature/strain uncertainty and 146 times faster processing speed when compared with CESM.