The 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 (e.g., temperature, water-velocity, pressure etc.) 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. Within this project the student 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.
The MFC CDT programme provides comprehensive training in the theory of climate science, physical sciences, scientific computing, statistics and data analysis to address pressing problems and challenges posed by climate change. The CDT programme also affords extensive opportunities for personal and professional development training, and for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. Specific training will include:
* Opportunities to present work at international conferences.
* Opportunities to participate in fieldwork on a scientific cruise in the Southern Ocean.
* Attendance at relevant summer schools.
To apply for this project please click here (https://student-selfservice.soton.ac.uk/BNNRPROD/bzsksrch.P_Search). Tick programme type - Research, tick Full-time, select Academic year – ‘2024/25', search text – ‘PhD Ocean&EarthSci (Mathematics for our Future Climate CDT)’. In Section 2 of the application form you should insert the name of the project and supervisor(s) you are interested in applying for. If you have any problems please contact acng@soton.ac.uk.
