Fading suppression method based on redundant data within the spatial resolution and deep learning for a Φ-OTDR system

Weak light intensity positions induced by interference fading adversely affect the sensing performance of phase-sensitive optical time-domain reflectometry (Φ-OTDR). Most effective fading suppression methods rely on frequency or phase modulation of the light source, which requires complex hardware modifications. To solve the above issue, this paper proposes a novel multi-channel data synthesizing method based on deep neural network (MDS-DNN) to reduce the impact of interference fading on the signal-to-noise ratio (SNR) of Φ-OTDR. The proposed algorithm can work efficiently without any modification of the conventional Φ-OTDR setup. The spatial sampling rate of the Φ-OTDR systems is typically much higher than the spatial resolution. This means that neighboring sampling points carry the same external vibration signal, providing redundant information. Therefore, it is possible to perform comprehensive analysis on these multi-channel data to improve the suppression capability of interference fading noise. This work designs a long short-term memory (LSTM) network-based framework and an end-to-end training strategy to automatically learn the correlation between these multi-channel data and the ideal sensing signal. Simulation and experimental results show that the MDS-DNN algorithm can effectively suppress phase noise and improve the SNR at fading positions. Experiments using the data collected from the actual Φ-OTDR system demonstrate that the output SNR can reach 49.88 dB, which is 19.65 dB higher than the average level of the input channels. Moreover, the MDS-DNN method reduces the false alarm rate caused by interference fading by one order.