Fibre optics and machine learning for the future of volcano monitoring

JOHNSON_UENV25ARIES

Fibre optics and machine learning for the future of volcano monitoring

JOHNSON_UENV25ARIES

Project Description

Supervisors

Dr Jess Johnson, Environmental Sciences, University of East Anglia – contact me

Dr Lidong Bie, UEA

Dr Leighton Watson, University of Canterbury, New Zealand

 

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.

Acceptable first degree subjects: Geophysics, Physics, Geology, Environmental Science, Computing, Maths, or a related discipline

References

  • Keats, B. S., Johnson, J. H., & Savage, M. K. (2011). The Erua earthquake cluster and seismic anisotropy in the Ruapehu region, New Zealand. Geophysical research letters, 38(16).
  • Jousset, P., Currenti, G., Schwarz, B., Chalari, A., Tilmann, F., Reinsch, T., ... & Krawczyk, C. M. (2022). Fibre optic distributed acoustic sensing of volcanic events. Nature communications, 13(1), 1753.
  • Mitchinson, S., Johnson, J. H., Milner, B., & Lines, J. (2024). Identifying earthquake swarms at Mt. Ruapehu, New Zealand: a machine learning approach. Frontiers in Earth Science, 12, 1343874.
  • Watson, L. M. (2020). Using unsupervised machine learning to identify changes in eruptive behavior at Mount Etna, Italy. Journal of Volcanology and Geothermal Research, 405, 107042.
  • Lindsey, N. J., & Martin, E. R. (2021). Fiber-optic seismology. Annual Review of Earth and Planetary Sciences, 49, 309-336.

Key Information

  • This studentship has been shortlisted for funding under the UKRI NERC DLA funding scheme and will commence on 1 October 2025. The closing date for applications is 23:59 on 8th January 2025.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£19,237 p.a. for 2024/25) and research funding. A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note however that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.
  • ARIES postgradute researcher (PGRs) benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences, on top of all Research Costs associated with the project. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside non-academic experience, and our recruitment process considers potential with the same weighting as past experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis. International applicants should check whether there are any conditions of visa or imigration permission that preclude part-time study. All advertised project proposals have been developed with consideration of a safe, inclusive, and appropriate research and fieldwork environment with respect to protected characteristics. If you have any concerns please contact us.
  • For further information, please contact the supervisor. To apply for this Studentship follow the instructions at the bottom of the page or click the 'apply now' link.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential, and will be stored on the UEA sharepoint server. Data will not be shared with those involved in making decisions on the award of Studentships, and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be ask to complete the form by the University to which you apply.
  • If funded under the BBSRC-NERC DLA scheme, ARIES studentships will be subject to UKRI terms and conditions. Postgraduate Researchers are expected to live within reasonable distance of their host organisation for the duration of their studentship. See https://www.ukri.org/publications/terms-and-conditions-for-training-funding/ for more information

Apply Now

Apply via the  University of East Anglia application portal