PhD Studentship: Fibre Optics and Machine Learning for the Future of Volcano Monitoring

Primary supervisor - Dr Jess Johnson 

Scientific background  

Volcano monitoring has come a long way in the last century (1), but recent volcanic tragedies at Mt. Merapi (Indonesia) in 2023, Mt. Semeru (Indonesia) in 2021, Taal (Philippines) in 2020, and Whakaari (New Zealand) in 2019 have illustrated, in devastating fashion, the need for better monitoring capabilities and understanding of volcanic processes around the world. 

New methodologies in photonic sensing (2) and machine learning (ML) (3,4) are in their infancy in volcanology, yet the potential for revolutionising the discipline is immense. Distributed Sensing (DS) is a brand-new technology for environmental research that does not rely upon individual sensors but utilises optical fibre (5). Traditional methods of subsurface monitoring are restricted in either time or space. Spot measurements record continuously but lack spatial resolution. Campaign measurements capture high spatial resolution data at a single point in time. However, DS can measure at 25 cm intervals at rates up to 100 kHz. 

Research methodology  

This project will use emerging ML methods of processing DS data on new recordings from Mount Ruapehu Volcano in New Zealand to answer questions like: 

•    How can DS be used to improve detection of small signals of unrest? 
•    What is the improvement of subsurface imaging using DS over traditional sensors? 
•    What additional understanding of volcanic processes can be gained from using DS? 
•    How can DS be integrated into regular monitoring operations? 

The student will use and adapt existing DS processing algorithms for a volcanic setting and test them on existing data from Mt. Ruapehu in 2022. They will then deploy equipment for new data acquisition and apply their methods in real-time to assist GNS Science to monitor the volcano. Results will be analysed in context with other observations. 

Training 

Training will be given in DS, geoscientific methods, machine learning techniques, and volcanic processes. You will spend time in New Zealand, learning about the field site and other observation data. 

Person specification 

Applicants must hold, or expect to receive, a degree in a relevant geoscience, computing or physical sciences discipline. You should be numerically literate and experience of using unix based operating systems and/or Python is desirable but not essential. 

For further information, please visit www.aries-dtp.ac.uk

Funding Details

Additional Funding Information

ARIES is awaiting confirmation of funding under the BBSRC-NERC DLA award scheme, which is expected shortly. Funding for this studentship is subject to this confirmation and UKRI terms and conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded ARIES studentship of fees, maintenance stipend (£19,237 p.a. for 2024/25) and research costs.

A limited number of ARIES studentships are available to International applicants. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK.

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