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Postgraduate research project

CURIOSITY: Cryosphere and Underwater Remote Inspection using Optic-fibre based Sustainable noise-Interferometry

Fully funded (UK and international)
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Environmental and Life Sciences
Closing date

About the project

This project aims to combine passive acoustic noise interferometry and distributed acoustic sensing of seafloor cables embedded with machine learning. This novel, coherent combination will sustainably enable at low-cost, spatially resolved high-resolution real-time insights into physical attributes such as temperature, water-velocity or pressure of the water column and the cryosphere. 

Technological advances in ocean observation have made it possible to measure phenomena across a wide range of scales and have been instrumental in driving physical oceanography forwards. However, despite this progress, a crucial blind spot remains in our observing capabilities: no established approach exists to robustly measure the ocean interior at scales of O(10-100 m) over a significant spatio-temporal extent. Important oceanic phenomena lying within the above observational blind spot permeate almost every topical problem in physical oceanography and ocean climate change. Unlocking progress in tackling these problems thus requires a new way of observing the ocean.

This project will demonstrate a novel, low-cost, wide-area approach using seafloor cables with natural sound to characterize the ocean and cryosphere. Tomographic techniques can be used to combine many acoustic paths to obtain the spatial structure in temperature and flow. This approach has been applied to sparsely-spread sensors; however, interrogation of fibre optics within legacy seafloor cables provides many acoustic measurements along the cable length. The larger number of sensor pairs and pair-wise separations decreases the noise averaging time from the hours-to-days typically needed to achieve oceanographically relevant accuracies to ~1 minute. 

A range of data sets suitable for this approach are already available and will be supplemented with upcoming fieldwork. You will undertake both theoretical and data analysis work to apply these techniques to a range of environments, including: the coastal ocean, deep ocean, sloping shelf edge, and Antarctic ice shelves. 

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