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

Area of Science:

• Geophysics
• Optical Sensing Technologies
• Signal Processing

Background:

• Distributed acoustic sensing (DAS) faces challenges in seismic exploration due to laser source frequency drift (LSFD) and phase noise.
• These issues degrade the true vibration phase curve recovery, limiting DAS applications in geophysics.

Purpose of the Study:

• To propose and validate a novel method for suppressing phase noise and drift in DAS.
• To enhance the signal-to-noise ratio (SNR) and improve the accuracy of vibration phase recovery for seismic exploration.

Main Methods:

• A new algorithm combining symmetric extreme value expansion (SEE), symplectic geometry mode decomposition (SGMD), and Pearson correlation coefficient (PCC) was developed (SEE-SGMD-PCC).
• The method's mathematical principles and processing flow were detailed.
• Experiments were conducted using a DAS system with digital heterodyne coherent detection.

Main Results:

• Simulation experiments confirmed the effectiveness of the SEE-SGMD-PCC method.
• Detailed analysis identified phase drift and noise sources in the DAS system.
• Significant improvements in phase signal SNR and effective restoration of phase information were achieved in single and multi-frequency vibration tests compared to other methods.

Conclusions:

• The SEE-SGMD-PCC method effectively suppresses phase noise and drift in DAS, significantly improving phase signal quality.
• The method demonstrates feasibility through simulations and on-site experiments, promoting DAS application in complex seismic environments.