This paper reports a combined technique to enhance Rayleigh scattering signal in optical fiber and deep neutral network data analytics for distributed acoustic sensing (DAS). By using ultrafast laser direct writing technique, over 45 dB of Rayleigh backscattering enhancement can be achieved in silica fibers to improve backscattering signal to improve signal to noise ratio for optical fiber DAS based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). The enhanced backscattering signal enhance detections of vibration signal to subsequent data analysis and classifications. Using deep neutral networks and through both supervised and unsupervised machine learnings, the distributed acoustic sensing system were used to detect and to identify human movements to achieve <90% identification accuracies. The applications of DAS and artificial intelligence in pipeline corrosion detection and damage classification is also discussed in this paper.
Zhaoqiang Peng, Jianan Jian, HongQiao Wen, Mohan Wang, Hu Liu, Desheng Jiang, Zhihong Mao, and Kevin P. Chen, "Fiber-optical distributed acoustic sensing signal enhancements using ultrafast laser and artificial intelligence for human movement detection and pipeline monitoring," Proc. SPIE 10937, Optical Data Science II, 109370J (Presented at SPIE OPTO: February 07, 2019; Published: 1 March 2019); https://doi.org/10.1117/12.2509875.
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