Abstract

With the rapid advancement of deep learning, its applications in ffber optic sensing are expanding signiffcantly, particularly in recognizing diverse scenarios where substantial progress has been made. However, the crucial task of obtaining demodulation results from raw data remains relatively underexplored. Accurate demodulation is essential for a deeper understanding of the physical processes in ffber optic sensing systems, enhancing measurement accuracy, and optimizing system performance. In this paper, a phase demodulation algorithm for distributed acoustic sensor (DAS) data processing was developed using deep learning framework. The proposed approach aims to replace the computationally intensive processes of traditional demodulation algorithms, such as the Hilbert transform, in speciffc scenarios. The proposed method enables faster acquisition of detailed demodulated data and provides an intuitive, real-time graphical display of the results. Experimental results demonstrate that the proposed method signiffcantly enhances processing speed while maintaining accuracy comparable to traditional demodulation algorithms. Speciffcally, for a dataset comprising 2000 rows and 4500 columns (approximately 9 million data points), the response time is reduced from 2.62 to 0.1 s when processing data at a pulse frequency of 20 kHz over a sensing distance of 1.8 km.

Biography

Mengmeng Chen is a professor in the School of Electronic Engineering at Nanjing Xiaozhuang University. She received her B.S. degree from Nanjing University of Science and Technology in 2007 and her Ph.D. degree from Nanjing University in 2013. From 2018 to 2019, she was an academic visitor at the University of Southampton, UK. Since 2020, she has been a postdoctoral fellow at Nanjing University. Her research interests include fiber-optic communications and distributed optical fiber sensing.