摘要
简要回顾了从基于传统机器学习的普通感知型分布式光纤声波传感器(DAS)到基于深度学习的全智能化DAS的转变历程,深入分析了基于多维信息提取的DAS监督学习及半监督、无监督和跨场景迁移等深度学习方法的研究现状,概括了不同识别模型的构建思路、特点,及其识别性能、处理时间等评价指标,也论述了DAS从单源检测到多源混叠检测、从单任务到多任务处理等智能感知能力提升面临的新挑战,最后对全智能化DAS的信号处理发展方向及新趋势进行了展望。
Significance Optical fiber sensors play an increasingly important role in safety monitoring areas in the smart Internet of Things(IoT).Particularly,a fiber-optic distributed acoustic sensor(fiber-optic DAS)based on the phase-sensitive optical time-domain reflectometry(Φ-OTDR)technology provides a highly dense,cost-effective,and continuous environment measurement way over a wide range.All kinds of vibration sources can be sensed and located with high sensitivity and precision utilizing the widely laid ordinary telecommunication cables,and thus fiber-optic DAS has been applied in various ground listening applications,such as natural disaster prediction of ocean-floor seismic activity,volcanic events,and earthquake,energy exploration in oil and gas industry,and civil infrastructure monitoring in the pipelines,railways,and perimeters.It leads to a new generation of large-scale fiber-optic IoT for ground and underwater listening technology.From the current research status in China and abroad,DAS is becoming mature in its hardware performance,such as the demodulation fidelity,sensing distance,detection bandwidth,and sensitivity,which are all approaching their perfection.However,with the rapid advance of DAS applications,the complicated and ever-changing environments for large-scale monitoring have brought about challenges of high false alarm rates due to its advantages of high sensitivity.It is difficult to achieve high-precision detection,recognition,and positioning of perceived vibration and acoustic targets,which has become the biggest technical bottleneck restricting the large-scale application of DAS technology.In recent years,driven by the development of advanced signal processing and artificial intelligence(AI)technology,the signal processing methods of fully intelligent DAS with high accuracy and real-time performance in practical complex environments have become a research hotspot and focus in the field of fiber-optic sensing.The signal processing method in DAS plays a crucial and decisive role in improving t
作者
吴慧娟
王新蕾
廖海贝
矫玺本
刘一羽
舒新建
王璟伦
饶云江
Wu Huijuan;Wang Xinlei;Liao Haibei;Jiao Xiben;Liu Yiyu;Shu Xinjian;Wang Jinglun;Rao Yunjiang(Key Laboratory of Fiber Optic Sensing and Communication,Ministry of Education,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China;Fiber Optic Sensing Research Center,Zhijiang Laboratory,Hangzhou 310000,Zhejiang,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2024年第1期180-200,共21页
Acta Optica Sinica
基金
国家自然科学基金(U21A20453,41527805,61301275,61290312)
教育部长江学者和创新团队发展计划(IRT1218)
高等学校学科创新引智计划(B14039)
四川省自然科学基金面上项目(2023NSFSC0382)。
关键词
光纤物联网
相敏光时域反射
分布式光纤声波传感器
智能感知
信号处理
fiber-optic Internet of Things
phase-sensitive optical time domain reflectometry
fiber-optic distributed acoustic sensor
smart sensing
signal processing
