Machine Learning for Geophysical Hazard Sequences

(JOHNSON_UENV22ARIES)

Machine Learning for Geophysical Hazard Sequences

(JOHNSON_UENV22ARIES)

Project Description

Supervisors

Dr Jessica Johnson (School of Environmental Sciences, University of East Anglia) – Contact me

Dr Ben Milner (School of Computing Sciences, University of East Anglia)

Dr Jason Lines (School of Computing Sciences, University of East Anglia)

Dr Zoë Mildon (School of Geography, Earth and Environmental Sciences, University of Plymouth)

Dr Irene Manzella (School of Geography, Earth and Environmental Sciences, University of Plymouth)

Gill Jolly (Natural Hazards and Risk Theme Leader, GNS Science, New Zealand)

 

Project Background

Geophysical hazards, such as volcanic eruptions, large earthquakes and landslides, threaten millions of people around the world. Many of these hazards are unpredictable, but there are often precursors that may be used to forecast damaging events. These precursors could be in the form of monitoring observations such as seismicity, deformation and gas emissions (1), or from data processed after the event has occurred, recognised in hindsight (2).

In typical hazard forecasting, observations of pre-cursors are studied and researchers use their prior knowledge to estimate the probability of an event occurring (3). However, Machine Learning (ML) tools are increasingly being applied to geosciences and hazard monitoring due to ML’s strength in identifying patterns (4,5). If patterns can be identified in hindsight, then they could be used as a precursor to forecast future events.

Research methodology

This project will use precursor sequences with ML techniques to investigate the relationship between datasets, answering questions like:

  • Which data are the most important for forecasting?
  • What is the minimum monitoring network required?
  • Which data would be needed to decrease uncertainty?
  • How does ML compare to expert elicitation?

The student will use geophysical, geological and geochemical data relating to specific geophysical hazards in New Zealand. Data will be obtained from online sources, published literature, and expert accounts. The student will train state-of-the-art time series classification algorithms on the precursor data, and devise performance measures for predictions. ML results will be quantitatively compared to existing methodologies using expert elicitation.

Training

Training will be given in geoscientific methods, machine learning techniques, and geophysical hazard processes (including earthquakes, volcanoes and landslides). The student will spend time in New Zealand, learning about the hazards and datasets.

Person specification

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

References

  • 1) Johnson, J. H., Prejean, S., Savage, M. K., & Townend, J. (2010). Anisotropy, repeating earthquakes, and seismicity associated with the 2008 eruption of Okmok volcano, Alaska. Journal of Geophysical Research: Solid Earth, 115(B9).
  • 2) 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).
  • 3) Bebbington, M. S., Stirling, M. W., Cronin, S., Wang, T., & Jolly, G. (2018). National-level long-term eruption forecasts by expert elicitation. Bulletin of Volcanology, 80(6), 56.
  • 4) Dempsey, D. E., Cronin, S. J., Mei, S., & Kempa-Liehr, A. W. (2020). Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand. Nature Communications, 11(1), 1-8.
  • 5) Oastler, G., & Lines, J. (2019). A Significantly Faster Elastic-Ensemble for Time-Series Classification. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 446-453). Springer, Cham.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2022. The closing date for applications is 23:59 on 12th January 2022.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£15,609 p.a. for 2021-22) and research funding. International applicants (EU and non-EU) are eligible for fully-funded UKRI studentships. Please note ARIES funding does not cover visa costs (including immigration health surcharge) or other additional costs associated with relocation to the UK.
  • ARIES students benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences. 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 in the subject area.
  • 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, visa requirements notwithstanding
  • For further information, please contact the supervisor. To apply for this Studentship click on the “Apply now” link below.
  • 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.

Applications are open

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