4.7 Article

Using Global Existing Fiber Networks for Environmental Sensing

期刊

PROCEEDINGS OF THE IEEE
卷 110, 期 11, 页码 1853-1888

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2022.3199742

关键词

Optical fiber sensors; Optical scattering; Optical fibers; Fiber nonlinear optics; Sensors; Optical interferometry; Optical pulses; Brillouin scattering; coherent detection; distributed acoustic sensing (DAS); distributed strain sensing (DSS); distributed temperature sensing (DTS); distributed vibration sensing (DVS); optical communications; optical fiber sensing; Raman scattering; Rayleigh scattering

资金

  1. Comunidad de Madrid [SINFOTON2-CM: P2018/NMT-4326]
  2. FEDER [SINFOTON2-CM: P2018/NMT-4326]
  3. MCIN/AEI
  4. European Union NextGenerationEU/PRTR Program under Project PSI [PLEC2021-007875]
  5. University of Alcala [CCG20/IA-028]
  6. European Research Council [OCEAN-DAS: ERC-2019-POC-875302]
  7. Spanish Government [RTI2018-097957-B-C31, RTI2018-097957-B-C33]
  8. Spanish MICINN [IJC2018-035684-I]

向作者/读者索取更多资源

This article reviews recent advances in distributed fiber optic sensing (DFOS) and its applications. DFOS utilizes the scattering mechanisms in glass to measure strain and/or temperature, enabling the monitoring of the ambient environment. By leveraging technologies developed for telecommunications, DFOS has achieved improved performance in terms of measurement resolution, reach, spatial resolution, and bandwidth. The article discusses the theory and architecture of commonly used DFOS methods and provides examples of their applications in various fields such as geohazard monitoring, seismic monitoring, traffic monitoring, and infrastructure health monitoring.
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.

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