Machine learning on optical fiber sensor data

Optical fiber is the backbone of the internet’s communication, e.g. in the form of submarine fiber cables. It can also be employed as a sensor device, by means of combined opto-acoustic methods such as Distributed acoustic sensing (DAS) or State of Polarisation (SoP) sensing. Fiber is cabapble of sensing all kinds of vibrational/acoustic events, from animal sounds over seismic activity to submarine and ship movements. Obviously, strategic interest in this field is significant, especially in the context of new ambitious fiber projects, such as the Nordunet “Vison 2023 - Resilient Submarine Cable System through the Arctic equipped with Sensing”.

In collaboration with Nordunet we would like to explore existing SoP datasets, and see to what extent we can use ML techniques to identify, cluster and classify events.