Brillouin optical time-domain analysis (BOTDA) using distributed Brillouin amplification (DBA) only requires a milliwatt-level pump to achieve a sensing range beyond 100 km, which provides a powerful tool for temperature/strain sensing. However, similar to the majority of other long-range BOTDAs, the state-of-the-art reports require > 1000 times average, severely restricting the sensing speed. The blind area over tens of kilometers caused by the nonuniform Brillouin response and parasitic amplitude modulation (AM) are crucial factors affecting the signal-to-noise ratio (SNR). Here, a comprehensive performance optimization and substantial enhancement for BOTDA sensors was presented by the direct demodulation of an injection-locked dual-bandwidth probe wave. Injection locking (IL) can completely eliminate the impact of AM noise; dual-bandwidth probe enables self-adaptive pulse loss compensation, thereby intensifying the SNR flatness along the ultralong fiber, and direct probe demodulation can overcome nonlocal effects and allows ∼19.7 dB enhancement of probe input power. Therefore, using only 100 times average, ∼148.3 km sensing, and ∼5 m spatial resolution were achieved with < ∼0.8 MHz standard deviation of Brillouin frequency shift (BFS) over a broad range (∼131.7 km). The reduction in averages was more than 10 times that of the reported majority of long-range BOTDAs. Such performances were achieved without using time-consuming or post-processing techniques, such as optical pulse coding and image denoising. Because this approach is compatible with optical chirp chain technique without frequency sweeping, fast acquisition (0.3 s) was also realized, which has the potential for fast sensing at 3.3 Hz along a ∼150 km fiber.

We numerically explored the enhanced performance and physical mechanism of semiconductor laser (SL) based reservoir computation (RC) with double optoelectronic feedback (DOEF). One-step and multistep Santa Fe time series predictions were used as standard test benchmarks in this work. We found that in the optimized parameter region the normalized mean square error (NMSE) of an SL-based RC under DOEF is smaller than an SL-based RC with single optoelectronic feedback (SOEF). In addition, the performance improvement is more obvious for multistep prediction, which is particularly suitable for more complex tasks that requires a higher memory capability (MC). The enriched node states (optical intensity of the virtual nodes for each sample) and the enhanced MC of the proposed DOEF were verified by a comparison to SOEF under the optimized feedback strength. The influence of the feedback strength and the delay difference on the NMSE and the MC was also investigated. Our study should be helpful in the design of a high-performance optoelectronic RC based on an SL.