Fading noise reduction in distributed acoustic sensing using an optimal weighted average algorithm.

Area of Science:

• Photonics and Optical Sensing
• Signal Processing
• Fiber Optic Technology

Background:

• Distributed Acoustic Sensing (DAS) is susceptible to fading noise, which degrades signal quality.
• Conventional DAS systems struggle to mitigate this noise effectively, limiting their application range.
• Rayleigh backscattered signals in optical fibers are fundamental to DAS but prone to interference.

Purpose of the Study:

• To introduce a novel approach for reducing fading noise in distributed acoustic sensing (DAS).
• To improve the signal-to-noise ratio and clarity of acoustic measurements obtained via DAS.
• To demonstrate the effectiveness of the proposed method in a practical fiber optic sensing scenario.

Main Methods:

• Utilizing a phase-sensitive optical time-domain reflectometry (OTDR) system with diversity detection.
• Collecting Rayleigh signals across multiple optical modes.
• Developing and applying an optimal weighted average algorithm to synthesize phase-demodulated signals.

Main Results:

• The proposed approach significantly reduces fading noise in DAS.
• An aggregate phase is synthesized, presenting acoustic signals with enhanced clarity.
• Experimental validation on a 12.8 km sensing fiber confirmed the noise reduction efficacy compared to conventional DAS.

Conclusions:

• The novel weighted average algorithm effectively reduces fading noise in DAS.
• The phase-sensitive OTDR system with diversity detection provides a robust platform for this noise reduction technique.
• This method offers a significant improvement for applications requiring high-fidelity acoustic sensing over long fiber optic cables.