Artificial intelligence-driven distributed acoustic sensing technology and engineering application
Abstract Distributed acoustic sensing (DAS) technology is a fiber-optic based distributed sensing technology. It achieves real-time monitoring of acoustic signals by detecting weak disturbances along the fiber. It has advantages such as long measurement distance, high spatial resolution and large dynamic range. Artificial intelligence (AI) has great application potential in DAS technology, including data augmentation, preprocessing and classification and recognition of acoustic events. By introducing AI algorithms, DAS system can process massive data more automatically and intelligently. Through data analysis and prediction, AI-enabled DAS technology has wide applications in fields such as transportation, energy and security due to its accuracy of monitoring data and reliability of intelligent decision-making. In the future, the continuous advancement of AI technology will bring greater breakthroughs and innovations for the engineering application of DAS technology, play a more important role in various fields, and promote the innovation and development of the industry.
Authors
- Zixing Huang (ORCID: https://orcid.org/0000-0001-7967-5948)
- Xiaobing Shi (ORCID: https://orcid.org/0000-0002-7605-8829)
- Liu Chuan-qing (ORCID: https://orcid.org/0009-0009-4942-5492)
- Jingming Zhang (ORCID: https://orcid.org/0000-0002-0961-934X)
- Jiayao Sun
- Liyang Shao (ORCID: https://orcid.org/0000-0003-1272-1959)
- Yunbin Ma
- Huaxin Gu (ORCID: https://orcid.org/0009-0000-4045-4750)
- Haifeng Zhang (ORCID: https://orcid.org/0000-0002-7094-669X)
- Changyuan Yu (ORCID: https://orcid.org/0000-0002-3185-0441)
- Lu Han (ORCID: https://orcid.org/0000-0002-1573-2912)
- Xiongji Yang
- Feihong Yu (ORCID: https://orcid.org/0000-0003-3299-5594)
- Shuaiqi Liu (ORCID: https://orcid.org/0000-0002-6754-1587)
- Deyu Xu (ORCID: https://orcid.org/0009-0002-4945-1489)
- Qi Mu (ORCID: https://orcid.org/0009-0005-7534-5999)
- Xingwei Chen (ORCID: https://orcid.org/0009-0001-3094-207X)
Institutions
- North China Institute of Science and Technology (CN)
- Hong Kong Polytechnic University (HK)
- Southern University of Science and Technology (CN)
- Shenzhen Metro (China) (CN)
- Pearl River Hydraulic Research Institute (CN)
Publication Details
- Journal
- PhotoniX
- Published
- 2025-02-24
- DOI
- https://doi.org/10.1186/s43074-025-00160-z
- Citations
- 45
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 45.28
Funders
- Natural Science Foundation of Shenzhen City
- Science, Technology and Innovation Commission of Shenzhen Municipality