Jiang Fei, Zhang Zhenhai, Lu Zixiao, Li Honglang, Tian Yahui, Zhang Yixin, Zhang Xuping
Opt Express. 2021 Oct 11;29(21):33467-33480. doi: 10.1364/OE.439646.
Phase-measuring phase-sensitive optical time-domain reflectometry (OTDR) has been widely used for the distributed acoustic sensing. However, the demodulated phase signals are generally noisy due to the laser frequency drift, laser phase noise, and interference fading. These issues are usually addressed individually. In this paper, we propose to address them simultaneously using supervised learning. We first use numerical simulations to generate the corresponding noisy differential phase signals for the given acoustic signals. Then we use the generated acoustic signals and noises together with some real noise data to train an end-to-end convolutional neutral network (CNN) for the acoustic signal enhancement. Three experiments are conduct to evaluate the performance of the proposed signal enhancement method. After enhancement, the average signal-to-noise ratio (SNR) of the recovered PZT vibration signals is improved from 13.4 dB to 42.8 dB, while the average scale-invariant signal-to-distortion ratio (SI-SDR) of the recovered speech signals is improved by 7.7 dB. The results show that, the proposed method can well suppress the noise and signal distortion caused by the laser frequency drift, laser phase noise, and interference fading, while recover the acoustic signals with high fidelity.
相位测量相敏光时域反射仪（OTDR）已被广泛用于分布式声学传感。然而，由于激光频率漂移、激光相位噪声和干扰衰落，解调后的相位信号通常有噪声。这些问题通常是分别处理的。在本文中，我们建议使用监督学习同时解决这些问题。我们首先使用数值模拟为给定的声学信号生成相应的有噪声的差分相位信号。然后，我们将生成的声学信号和噪声与一些真实噪声数据一起用于训练一个用于声学信号增强的端到端卷积神经网络（CNN）。进行了三个实验来评估所提出的信号增强方法的性能。增强后，恢复的PZT振动信号的平均信噪比（SNR）从13.4 dB提高到42.8 dB，而恢复的语音信号的平均尺度不变信号失真比（SI-SDR）提高了7.7 dB。结果表明，所提出的方法能够很好地抑制由激光频率漂移、激光相位噪声和干扰衰落引起的噪声和信号失真，同时以高保真度恢复声学信号。
