Artificial intelligence-driven distributed acoustic sensing technology and engineering application

The Distributed Acoustic Sensing (DAS) system based on Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) uses narrow-linewidth lasers as high-coherence light sources, utilizing Rayleigh scattering signals in the fiber for real-time monitoring. During the detection of pulse propagation, the scattered light interferes, and the detected signal is the coherent superposition of Rayleigh scattering signals within the pulse width. This technology offers advantages such as long measurement distances, high spatial resolution, and a large dynamic range, efficiently capturing acoustic wave propagation information in the environment.

 AI in DAS mainly includes three stages: data acquisition, preprocessing, and machine learning model construction. Data is the foundation of artificial intelligence, facing challenges such as difficulties in data acquisition and large data volumes. The establishment of public DAS datasets and the development of data augmentation algorithms help promote further advancements in the AI+DAS field. Data preprocessing involves two steps: denoising and feature extraction. Denoising algorithms effectively reduce the impact of Gaussian noise, phase noise, and fading on the signal. Feature extraction improves the accuracy of classification models by selecting appropriate features. In model construction, traditional machine learning methods such as Support Vector Machines (SVM) and Hidden Markov Models (HMM) are still widely used. Deep learning models like Convolutional Neural Networks (CNN) are becoming mainstream algorithms for DAS pattern recognition. Advanced learning paradigms, including semi-supervised learning, unsupervised learning, and transfer learning, are also gradually being applied to DAS event recognition, aiming to enhance recognition accuracy and model robustness.

 AI-driven DAS technology demonstrates broad application potential across multiple industries. In the transportation sector, DAS technology can be used for infrastructure monitoring and intelligent transportation systems. In the energy sector, DAS technology is applied to oil and gas pipeline monitoring and power system monitoring. In the security field, DAS technology provides early warning and protection for critical facilities by monitoring surrounding vibrations and acoustic signals.

Introduction of the main author

Shao Liyang, Vice Dean/Professor (Doctoral Supervisor) of the School of Innovation and Entrepreneurship at Southern University of Science and Technology, Corresponding member of the National Academy of Artificial Intelligence in the United States, National Distinguished Young Expert, Double Hired Researcher at Pengcheng Laboratory/Southern Ocean Laboratory, Director of the Aerospace Ocean Integrated Intelligent Network Research Center, Executive Director of Guangdong Integrated Optoelectronic Sensing Laboratory, Expert in Major Projects/National Award Review of the Ministry of Science and Technology/National Science Foundation, Senior Member of IEEE/OSA/SPIE, Senior Member of the Chinese Optical Society, Member of the Optical Testing Special Committee/Fiber and Integrated Optics Special Committee, Member of the Fiber Sensing Technology Special Committee of the Chinese Optical Society, and Editorial Board Member of PhotonIX/OEA and other journals. The main research directions are intelligent distributed fiber optic sensing technology and engineering applications, key technologies and application research for integrated intelligent networking of heaven, earth and sea, covering intelligent monitoring in oil and gas pipeline networks, wind power grids, rail transit, bridges and tunnels, perimeter security, communication optical cables (operators), aerospace and other fields, and deploying applications in marine wind power, marine oil and gas exploration, marine geophysics, and military defense. At present, more than 200 academic papers have been published in major international journals and conferences, including 183 SCI papers (Nat. Comm., Light Sci. Appl., Opto Electronic Advances, Laser Photon. Rev., PhotonIX, etc.), with 7835 citations and an H-factor of 48. I have delivered two keynote speeches and over 20 invited presentations at important international conferences such as IEEE ICCT, CLEO-PR, and APOS. I have also served as a TCP or organizing committee member for multiple international conferences, including ACP 2018/21/22, OECC 2021, OGC 2020-24, APOS 2018-24, and CLEO-PR 2018/20, and participated in organizing over 20 international conferences. Authorized over 20 invention patents. In recent years, I have led and undertaken more than 20 scientific research projects, including the International Cooperation Special Project of the Ministry of Science and Technology, the National Natural Science Foundation of China, the Sichuan Provincial High level Talent Introduction Fund, major projects of the Provincial Department of Science and Technology, international cooperation projects of the Guangdong Provincial Department of Science and Technology, projects of the Guangdong Provincial Department of Natural Resources, research start-up projects of the Shenzhen Municipal Government, and enterprise horizontal projects. The developed sensing system has been widely applied in various engineering fields (such as Shenzhen Gas, Hong Kong Metro, China Telecom, Southern Power Grid, and demonstration projects in the Qiongzhou Strait 500kV transmission cable), and its achievements have been repeatedly reported by mainstream media such as People's Daily, Xinhua News Agency, China Science and Technology Industry, Shenzhen Business Daily, China Radio Greater Bay Area Voice, Voice of the Internet of Things, and Southern Innovation Center. Has won the Australian Government's "Endeavor Scholar Award", "Zhan Tianyou Railway Technology Award Youth Award", Sichuan Province's "Outstanding Contribution Expert", been selected for the Shenzhen Overseas High level Talent Introduction Plan (Class B), "Shenzhen Industrial Development and Innovation Talent Award", China Industry University Research Cooperation Promotion Award (Individual), and the Second Prize of China Optical Engineering Society Technology Invention Award, etc. In addition, it has been selected for five consecutive years in the "Top 2% of Global Scientists" list released by Stanford University in the United States.

Published on: PhotoniX Paper link: https://photonix.springeropen.com/articles/10.1186/s43074-025-00160-z 

Literature search: PhotoniX 6, 4 (2025) https://doi.org/10.1186/s43074-025-00160-z