Phase drift and noise suppression method based on SEE-SGMD-PCC in a distributed acoustic sensor

Abstract

The problems of laser source frequency drift (LSFD) and phase noise in distributed acoustic sensor (DAS) make it difficult to recover the true vibration phase curve, which limits its application scope in the field of seismic exploration. In order to suppress the influence of phase noise and phase drift on the phase signal and improve the signal-to-noise ratio (SNR) of the phase curve, this paper proposes a method based on symmetric extreme value expansion, symplectic geometry mode decomposition and Pearson correlation coefficient (SEE-SGMD-PCC). Firstly, the mathematical principles and processing flow of the SEE-SGMD-PCC algorithm were introduced, and the effectiveness of this method was verified through multiple sets of simulation experiments. Secondly, The DAS system implemented using digital heterodyne coherent detection technology was used as the experimental platform, and the phase drift and phase noise sources of the phase signal were analyzed in detail. Next, in single frequency and multi frequency vibration signal experiments, compared with other methods, the SNR of the phase signal is significantly improved, and the phase information is effectively restored. Finally, the feasibility of the proposed method was demonstrated through two on-site experiments. The proposal of this method further promotes the application process of DAS in complex seismic exploration environments.