Denoising of burst vibrations by adaptive polynomial fitting in φ-OTDR

Aiming to improve the sensing performance of phase-sensitive optical time-domain reflectometer in burst vibration events, a denoising method based on adaptive polynomial fitting is demonstrated to separate submerged vibrations from background noises. The randomly distributed low-frequency noises arising from the sensing system or environments can degrade the signal-to-noise ratio in detecting burst signals with low occurrence rate. Considering the temporal characteristic of target signals, the polynomial fitting operation is applied to the Rayleigh backscattering from each sensing point to extract the vibration profile from background noises. The fitting factor determined by the coefficient of determination R2 adaptively optimize the fitting model to obtain a sufficient noise suppression. In experiments, the burst vibrations located at the far-end of the sensing fiber with repetition rates of 100 Hz, 10 Hz, and 1 Hz are detected, and location SNR of 5.5 dB of 1 Hz burst vibration is achieved.