The choice of wavelet decomposition layer (DL) not only affects the denoising quality of wavelet denoising (WD), but also limits the denoising efficiency, especially when dealing with real phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) signals with complex signal characteristics and different noise distributions. In this paper, a straightforward adaptive DL selection method is introduced, which dose not require known noise and clean signals, but relies on the similarity between the probability density function (PDF) of method noise (MN) and the PDF of Gaussian white noise. Validation is carried out using hypothetical noise signals and measured $\phi$-OTDR vibration signals by comparison with conventional metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The proposed wavelet DL selection method contributes to the fast processing of distributed fiber optic sensing signals and further improves the system performance.

Non-local mean (NLM) can significantly improve the signal-to-noise ratio (SNR), but it inevitably reduces the spatial resolution of distributed optical fiber sensors (DFOS), which hinders its practical application and the improvement of DFOS performance. In this paper, the quantitative relationship between the signal broadening of a phase-sensitive optical time-domain reflectometer ($\phi$-OTDR) and the NLM parameters is analyzed to identify the cause and extent of the spatial resolution degradation. The denoising results for the mimic periodic and $\phi$-OTDR vibration signals indicate that the signal broadening is mainly due to the similarity window size of NLM, and the signal amplitude reduction is caused by the Gaussian smoothing parameter. Compared with the reference signals, the signal broadening of the mimic and $\phi$-OTDR signals after denoising are 2.56% and 2.74%, respectively, which is much less than the previous results. The signal amplitude is reduced by 9.25% and 13.62%, respectively. This work promotes the application of NLM and improves the performance of DFOS.

Optical fiber distributed acoustic sensing (DAS) represents a revolutionary acoustic wave detection technology with a large scale and high sensitivity and is widely used in industrial fields. In conventional single-frequency DAS systems that are based on acousto-optic modulators (AOMs), frequency and phase drift induced by frequency-shift modulation, the randomness of the injected pulse phase, and random fading of the Rayleigh backscattering signal (RBS) are bottlenecks that limit further performance improvements. Herein, we propose a novel DAS architecture that features phase-locked multifrequency (PLMF) pulse injection that is based on zero-frequency-shift modulation, which overcomes the performance bottlenecks of conventional DAS. Specifically, zero-frequency-shift modulation combines a semiconductor optical amplifier (SOA) with forward-stimulated Brillouin scattering (FSBS) to suppress modulation-induced chirp to generate seed pulses with high phase and frequency stability and low chirp noise, thereby greatly improving the low-frequency detection ability of the system. Furthermore, we establish a PLMF pulse train generation mechanism by matching the pulse repetition frequency to the frequency shift, which enables either minimum residual superposition of RBSs at different frequencies in the raw intensity domain to achieve high strain resolution or flexible multifrequency combinations to achieve high spatial resolution. The PLMF-DAS system achieves world-class performance with a strain resolution of 300 fε/√Hz @1 kHz, an ultralow-frequency response down to 0.07 mHz, a sensing range of 172 km and a spatial resolution of 0.25 m. This system eliminates the need for expensive and complex optoelectronic components and provides an effective pathway for realizing next-generation high-performance and engineered DAS systems.