Abstract

A disturbance detection method with deep learning is proposed and experimentally demonstrated for φ-OTDR. Compared with typical methods, better performance can be intensified by entirely using the multi-dimensional features of sensing data. The adaptability and anti-noise capacity are greatly advanced with the proposed method. In experiments, an ultra-high SNR of 53.98 dB can be obtained in the best case. A spatial resolution of 1.06m and SNR of 38.49 dB are achieved simultaneously when the pulse width is modulated to the narrowest. Moreover, additive noise and multiplicative noise with a level of 0.015 were applied, respectively. The SNR can reach 35.22 dB and 42.39 dB. With this approach, the stable high SNR is unprecedentedly improved. This method provides the potential for temporal-spatial disturbance detection in φ-OTDR with strong background noise and extreme conditions.

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