Low frequency vibration detection for phase-sensitive OTDR based on SSA+VMD

To enhance the detection precision of Φ-OTDR, noise suppression algorithms are widely used due to their low cost and convenience. Among these algorithms, variational mode decomposition (VMD) has been proven to be an efficient method for Φ-OTDR signal denoising. The selection of VMD parameters is crucial and usually requires experimental optimization to achieve the best denoising performance. Addressing this issue, we propose an improved VMD algorithm combined with the sparrow search algorithm (SSA) for parameter optimization. Specifically, SSA is utilized to optimize the penalty factor and the number of modal decompositions in VMD. By using the minimum envelope entropy as the fitness function, SSA can automatically generate and optimize the key parameters in VMD, ensuring the best possible denoising results. Experimental results show that under disturbances at 100Hz and 1kHz, the noise suppression effect on the Φ-OTDR signal is significantly improved. The signal-to-noise ratio (SNR) is notably increased, with a maximum increase of 45.7dB. Moreover, the combination of SSA and VMD also exhibits excellent performance in low-frequency signal denoising, effectively suppressing phase drift. It successfully recovers signal waveforms at 0.1Hz.