We demonstrate a novel distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Both the phase and the amplitude of the Rayleigh scattering (RS) light can be demodulated in real-time. The technique is based on I/Q demodulation and homodyne detection using a 90° optical hybrid. The theoretical analysis is given, and as a proof of the concept, the dynamic strain sensing is experimentally demonstrated, with a sensing range of 12.566 km and a spatial resolution of 10 m.
In the distributed optical fiber sensing (DOFS) domain, simultaneous measurement of vibration and temperature/strain based on Rayleigh scattering and Brillouin scattering in fiber could have wide applications. However, there are certain challenges for the case of ultra-long sensing range, including the interplay of different scattering mechanisms, the interaction of two types of sensing signals, and the competition of pump power. In this paper, a hybrid DOFS system, which can simultaneously measure temperature/strain and vibration over 150 km, is elaborately designed via integrating the Brillouin optical time-domain analyzer (BOTDA) and phase-sensitive optical time-domain reflectometry (Ф-OTDR). Distributed Raman and Brillouin amplifications, frequency division multiplexing (FDM), wavelength division multiplexing (WDM), and time division multiplexing (TDM) are delicately fused to accommodate ultra-long-distance BOTDA and Ф-OTDR. Consequently, the sensing range of the hybrid system is 150.62 km, and the spatial resolution of BOTDA and Ф-OTDR are 9 m and 30 m, respectively. The measurement uncertainty of the BOTDA is ± 0.82 MHz. To the best of our knowledge, this is the first time that such hybrid DOFS is realized with a hundred-kilometer length scale.
Due to the similarity of Brillouin optical time domain analyzer (BOTDA) signals, image denoising could be utilized to remove the noise. However, the performance can be much degraded due to inaccurate noise level estimation. By numerical and experimental study, we compare the noise level estimation of three different methods for BOTDA: calculating the standard deviation (STD) of the measurements, a filter-based estimation algorithm, and a patch-based estimation algorithm proposed in this paper, which selects weak textured patches of BOTDA signal and then estimates noise level using principal component analysis (W-PCA). The results show that W-PCA and the mean of STD can accurately estimate the noise level, while the filter-based method overestimates the noise level. Nevertheless, for BOTDA with distributed amplification, the STD has huge fluctuation along the length, while the W-PCA is relatively robust for its global consideration. Experimental results of an ultra-long-distance BOTDA prove that the non-local means denoising processing based on W-PCA effectively removes the noise of a sensing system without signal distortion.
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