PhD Studentship: Listening to La Soufrière: Fibre Optics and AI for the Next Generation of Volcano Monitoring
University of East Anglia
Scientific Background A major challenge in volcanology is understanding and anticipating transitions in volcanic activity. Subtle changes within volcanic systems can precede hazardous eruptions, yet remain difficult to detect and interpret. This challenge is the focus of the NERC Large Grant Expecting the Unexpected (Ex-X): Understanding Dangerous Volcanic Transitions (dangerousvolcanoes.org). The 2020-21 eruption of La Soufrière, St Vincent, highlighted the importance of improved monitoring and hazard assessmen...
Scientific Background A major challenge in volcanology is understanding and anticipating transitions in volcanic activity. Subtle changes within volcanic systems can precede hazardous eruptions, yet remain difficult to detect and interpret. This challenge is the focus of the NERC Large Grant Expecting the Unexpected (Ex-X): Understanding Dangerous Volcanic Transitions (dangerousvolcanoes.org). The 2020-21 eruption of La Soufrière, St Vincent, highlighted the importance of improved monitoring and hazard assessmen...
Opportunity details
Scientific Background A major challenge in volcanology is understanding and anticipating transitions in volcanic activity. Subtle changes within volcanic systems can precede hazardous eruptions, yet remain difficult to detect and interpret. This challenge is the focus of the NERC Large Grant Expecting the Unexpected (Ex-X): Understanding Dangerous Volcanic Transitions (dangerousvolcanoes.org). The 2020-21 eruption of La Soufrière, St Vincent, highlighted the importance of improved monitoring and hazard assessment. Recent advances in Distributed Acoustic Sensing (DAS) offer new opportunities for volcano monitoring by transforming fibre-optic telecommunications cables into dense seismic arrays. As part of Ex-X, a DAS interrogator has been installed on a subsea fibre-optic cable around northern St Vincent. This provides a unique opportunity to investigate volcanic and tectonic processes in previously unmonitored areas. The enhanced spatial and temporal resolution afforded by DAS can reveal subtle seismic changes indicative of magmatic unrest that are usually missed by conventional networks. Research Methodology The candidate will develop automated workflows for processing continuous DAS data from St Vincent. Machine learning and signal-processing approaches will be used to detect, classify and characterise earthquakes. They will integrate DAS observations with the permanent seismic network operated by the University of the West Indies Seismic Research Centre (SRC) and a dense nodal deployment acquired through Ex-X. They will improve earthquake location calculations, apply ambient-noise imaging techniques to investigate subsurface structure, and evaluate how DAS can contribute to volcano monitoring. Particular emphasis will be placed on developing monitoring products tha...
Eligibility
Research Methodology The candidate will develop automated workflows for processing continuous DAS data from St Vincent. Training The candidate will receive training in seismology, volcanology, DAS, machine learning, scientific programming and geophysical imaging. They will gain experience in developing operational software for geohazard monitoring and working with large datasets. Person Specification We seek an enthusiastic individual with a degree in geophysics or a related discipline.
Requirements
Research Methodology The candidate will develop automated workflows for processing continuous DAS data from St Vincent. Training The candidate will receive training in seismology, volcanology, DAS, machine learning, scientific programming and geophysical imaging. They will gain experience in developing operational software for geohazard monitoring and working with large datasets. Person Specification We seek an enthusiastic individual with a degree in geophysics or a related discipline.
Funding and benefits
Funding eligibility: UK Students, EU Students, International Students. Funding amount: £21,805.
How to apply
They will improve earthquake location calculations, apply ambient-noise imaging techniques to investigate subsurface structure, and evaluate how DAS can contribute to volcano monitoring.
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Benefits
Funding eligibility: UK Students, EU Students, International Students. Funding amount: £21,805.
Eligibility summary
Research Methodology The candidate will develop automated workflows for processing continuous DAS data from St Vincent. Training The candidate will receive training in seismology, volcanology, DAS, machine learning, scientific programming and geophysical imaging. They will gain experience in developing operational software for geohazard monitoring and working with large datasets. Person Specification We seek an enthusiastic individual with a degree in geophysics or a related discipline.
Requirements
Research Methodology The candidate will develop automated workflows for processing continuous DAS data from St Vincent. Training The candidate will receive training in seismology, volcanology, DAS, machine learning, scientific programming and geophysical imaging. They will gain experience in developing operational software for geohazard monitoring and working with large datasets. Person Specification We seek an enthusiastic individual with a degree in geophysics or a related discipline.
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