High-resolution distributed acoustic vibration demodulation method based on sweep frequency light depth fading resistance
By combining continuous linear sweep frequency optical signals and deep learning networks, the fading problem of distributed fiber optic acoustic vibration sensing systems in long-distance and high-noise environments is solved, achieving high-resolution stable demodulation and accurate positioning, which is suitable for long-distance, high-bandwidth distributed acoustic vibration monitoring.
Patent Information
- Application Number
- CN202610144012.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-02
- Publication Date
- 2026-06-09
AI Technical Summary
Existing distributed fiber optic acoustic vibration sensing systems are susceptible to interference fading under long-distance, high-resolution, and high-sensitivity conditions. Traditional methods are difficult to adaptively adjust, leading to signal distortion or detection blind spots. Furthermore, deep learning is not sufficiently applied in front-end physical demodulation.
Rayleigh scattering information is obtained by using a continuous linear sweep frequency optical signal. Combined with frequency domain demultiplexing and matched filtering compression, amplitude and phase joint modeling is performed using a deep learning network to achieve adaptive anti-fading demodulation. The Rayleigh scattering signal is spatially and temporally block-processed by the deep learning network to improve the signal-to-noise ratio and resolution.
It achieves stable demodulation at high resolution in long-distance and high-noise environments, improving the system's anti-interference and positioning accuracy, and is suitable for long-distance, high-bandwidth distributed acoustic and vibration monitoring scenarios.