Automated analysis of volcano imagery with machine learning techniques

Details

Volcanic gas emissions provide an insight into subsurface magmatic processes; their accurate measurement is therefore critical to volcano monitoring and research. Indeed, gas measurements have been pivotal to eruption forecasting in a number of places, saving many thousands of lives through subsequent timely evacuations. Due to its low background atmospheric concentration and distinctive absorption features at ultraviolet (UV) wavelengths, sulphur dioxide (SO2) is the most commonly monitored gas with remote sensing instruments. In recent years, the volcanology group at Sheffield have developed low-cost UV cameras for SO2 detection, with permanent instruments now deployed on 6 volcanoes worldwide. Vast amounts of valuable data are therefore now being collected; however, automation of retrieval algorithms for the accurate determination of SO2 emission rates remains troublesome: typically, substantial expert-user interaction is required to ensure the data are processed in a robust manner.

This PhD project aims to address the above issue by developing a fully automated processing workflow for accurate retrieval of SO2 emission rates. To achieve this goal, the successful candidate will incorporate computer vision and machine learning algorithms into an easy-to-use tool that is robust to a wide range of measurement conditions. Furthermore, they will aim to accurately quantify measurement uncertainties, which are often neglected or poorly constrained. Having developed their tool, the student will analyse datasets from two active volcanoes (El Reventador, Ecuador; Kīlauea, US) to further our understanding of volcanism at these highly-active sites.

The successful applicant will be given opportunities to present their work at international conferences. Depending on the applicant’s experience and motivation, there may be opportunities for field work to deploy and/or maintain UV camera systems on/around active volcanoes. 

This project is most suitable for applicants with degrees in Computer Science, Mathematics, Physics or associated subjects. Applicants with degrees in Earth Sciences (or similar) are also welcomed but would need to adequately demonstrate proficiency in computer programming and quantitative analyses.  

 Essential Criteria

• Proficient computer programming skills (desirably in Python)
• Understanding of quantitative analyses
• Good communication 
• Self-motivated 

Desirable Criteria

• Experience with machine learning and/or image processing techniques
• Understanding of remote sensing and basic physics

Project Supervisors:

Dr Thomas Wilkes (Department of Geography) https://www.sheffield.ac.uk/geography/people/academic-staff/thomas-wilkes 

Dr Tom Pering (Department of Geography) https://www.sheffield.ac.uk/geography/people/academic-staff/tom-pering 

Dr Jefersson Alex dos Santos (Department of Computer Science) https://www.sheffield.ac.uk/dcs/people/academic/jefersson-alex-dos-santos 

The intended start date for this PhD position is February 2024