Abstract
To address the challenge of weak vibration signal detection, we propose a chaotic fiber laser-based distributed sensing system integrated with machine learning-assisted signal extraction. The system combines a chaotic fiber laser with a linear non-balanced Sagnac interferometer, enabling high sensitivity to external perturbations while effectively suppressing reciprocal effects of traditional ring interferometer systems. A convolutional neural network (CNN) is employed to directly learn and extract discriminative vibration features from the chaotic sensing signals, facilitated by phase space reconstruction (PSR), which preserves the system’s intrinsic dynamics under extreme noise. By jointly exploiting the broadband, noise-like characteristics of chaotic laser sensing, and the nonlinear feature extraction capability of CNNs, the proposed system enables reliable detection of weak vibration signals under ultra-low signal-to-noise ratio (SNR) conditions, down to −22 dB. Experimental results demonstrate a weak frequency detection ranging from 0.1 Hz to 10 kHz, with significantly enhanced sensitivity and bandwidth compared with conventional signal processing-based methods.