학술논문

Using Global Existing Fiber Networks for Environmental Sensing
Document Type
Periodical
Source
Proceedings of the IEEE Proc. IEEE Proceedings of the IEEE. 110(11):1853-1888 Nov, 2022
Subject
General Topics for Engineers
Engineering Profession
Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Nuclear Engineering
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Optical fiber sensors
Optical scattering
Optical fibers
Fiber nonlinear optics
Sensors
Optical interferometry
Optical pulses
Brillouin scattering
Raman scattering
Rayleigh scattering
coherent detection
distributed acoustic sensing (DAS)
distributed strain sensing (DSS)
distributed temperature sensing (DTS)
distributed vibration sensing (DVS)
optical communications
optical fiber sensing
Language
ISSN
0018-9219
1558-2256
Abstract
We review recent advances in distributed fiber optic sensing (DFOS) and their applications. The scattering mechanisms in glass, which are exploited for reflectometry-based DFOS, are Rayleigh, Brillouin, and Raman scatterings. These are sensitive to either strain and/or temperature, allowing optical fiber cables to monitor their ambient environment in addition to their conventional role as a medium for telecommunications. Recently, DFOS leveraged technologies developed for telecommunications, such as coherent detection, digital signal processing, coding, and spatial/frequency diversity, to achieve improved performance in terms of measurand resolution, reach, spatial resolution, and bandwidth. We review the theory and architecture of commonly used DFOS methods. We provide recent experimental and field trial results where DFOS was used in wide-ranging applications, such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring. Events of interest often have unique signatures either in the spatial, temporal, frequency, or wavenumber domains. Based on the temperature and strain raw data obtained from DFOS, downstream postprocessing allows the detection, classification, and localization of events. Combining DFOS with machine learning methods, it is possible to realize complete sensor systems that are compact, low cost, and can operate in harsh environments and difficult-to-access locations, facilitating increased public safety and smarter cities.