Detection Range Enhancement for Φ-OTDR Using Semantic Image Segmentation

Abstract

Aiming to reduce the complexity of Φ-OTDR system introduced by traditional distributed optical amplification technology in the pursuit of long-distance sensing, a novel detection range enhancement method with semantic image segmentation based on deep learning is presented. In this study, the Rayleigh backscattered curves for detecting vibration signals are used to construct the temporal-spatial image. The encoder-decoder structure and atlas spatial pyramid pool model are adopted to capture the boundary of vibration area and multi-scale context information from the temporal-spatial image. The semantic extraction of vibration position is then realized by binary classification of image pixels. In experiments, this method significantly improves the signal-to-noise ratio of the vibration location results. The signal-to-noise ratio can reach 37.84 dB, 34.28 dB, 34.09 dB, and 32.17 dB at 4 km, 10 km, 20 km, and 40 km, respectively. With this approach, the vibration detection range is enhanced to about 80 km without adding any traditional distributed optical amplification structure. Furthermore, within this enhanced detection range, the vibration signals with different frequencies and different intensities can be located and multi-point vibration can also be verified experimentally. This method provides a novel means for the detection range enhancement of Φ-OTDR system.