The optical fiber network is the backbone of modern telecommunications infrastructure. There is currently an estimated 5 billion km of deployed fibers worldwide, crisscross the world’s oceans, and forming dense networks around major metropolitan areas. In addition to their intended purpose of facilitating telecommunications, optical fibers can also be used as sensors to monitor the ambient environment. Distributed fiber optic sensing (DFOS) exploit the scattering mechanisms in glass – Rayleigh, Brilluoin and Raman scattering – that are sensitive to strain and/or temperature. Furthermore, any environmental parameter that can be transduced to strain or temperature can also be sensed by DFOS. DFOS can help facilitate public safety and smarter cities, and can support research in areas such as ocean bottom seismography. DFOS has been around since the 1970s. Recently, this field has been revolutionised by technologies originally developed for telecommunications, including coherent detection, digital signal processing, coding, and spatial/frequency diversity, which can improve performance in terms of measurand resolution, reach, spatial resolution and bandwidth. Combining DFOS with machine learning methods, it is possible to realize complete sensing systems that are compact, low-cost, and can operate in harsh environments. In this talk, I will review Rayleigh-based DFOS methods, and provide insight into recent research results in this field, and examples of DFOS applications over deployed fibers.
