This project will use global satellite datasets and machine learning approaches to investigate the prevalence and characteristics of explosive submarine eruptions.
The majority of the Earth’s volcanoes are on the ocean floor, but direct observations of submarine eruptions are very rare. This means that fundamental characteristics of submarine volcanism, including eruption repeat times, remain largely unknown. Although only a small subset of submarine events will result in changes at the ocean surface, many of these are detectable in satellite imagery. Localised ocean colour change occurs both when erupted material is sufficiently shallow, and in the period after an eruption, when volcanic material may stimulate algal blooms (e.g., Urai & Machida, 2005). Eruptions that breach the ocean surface can produce distinctive sub-aerial plumes dominated by steam (e.g., Carey et al., 2014), as well as new, often transient, land. The most distinctive satellite signals are produced by pumice rafts, which can persist for long periods of time, travel great distances and pose a hazard to shipping (Mantas et al., 2011). Past observations of submarine eruptions from satellite imagery have required the manual analysis of satellite images and are limited to individual case studies. This project will develop a systematic approach to detecting submarine volcanic events from global satellite data sets.
This studentship will use freely available, global satellite datasets, especially Sentinel-2 and MODIS imagery, to characterise the signals produced by historical submarine eruptions. Preliminary work has identified a set of ten historical eruptions observable in MODIS or Sentinel-2 imagery, all of which were associated with ocean colour change, while four produced subaerial plumes and three major pumice rafts. The student will use these past eruptions as the starting point for training data sets for supervised classification of imagery, initially to search for evidence of other eruptions at the same volcano, with the aim of developing a classification approach that can be applied over larger regions of the world’s oceans. This will be achieved using cloud computing where possible, starting with algorithms tested in Google Earth Engine. Our CASE partner, Geollect, will provide input into the machine learning approaches developed, with the aim for maintaining some transferability for other applications relating to shipping and safety at sea. For the subset of events producing pumice rafts, we will compare to the properties of available hand specimens at NOC (from Metis Shoal 2019, Le Havre 2012 and others) to investigate relationships between satellite signals and the texture or composition of the erupted material.
The subset of submarine eruptions that produce satellite-detectable signals have the potential to provide fundamental information about eruption recurrence intervals. The optical satellite measurements that form the basis of this project will also provide independent information about potential eruption times and locations that can be tested against seismic, infrasound and satellite radar datasets where available. This project is timely, as our ability to detect relatively localised events is increased by the higher resolution of Sentinel-2 relative to older global data sets that only allowed detection of the largest of the eruptions that affect the ocean surface.
This project would suit a student with a strong interest in satellite remote sensing with large datasets and enthusiasm for coding. The student will also have the opportunity to learn about marine volcanism from experts at NOC, including studying rock samples from submarine eruptions. They will also have the opportunity to apply their skills to complementary problems in marine remote sensing during an internship at Geollect.
- Carey, R.J., Wysoczanski, R., Wunderman, R.& Jutzeler, M. (2014). Discovery of the largest historic silicic submarine eruption. Eos, 95, 157–159.
- Mantas, V.M., Pereira, A.J. & Morais, P.V. (2011). Plumes of discolored water of volcanic origin and possible implications for algal communities. The case of the Home Reef eruption of 2006 (Tonga, Southwest Pacific Ocean). Remote Sensing of Environment, 115, 1341–1352.
- Urai, M. & Machida, S. (2005). Discolored seawater detection using ASTER reflectance products : A case study of Satsuma-Iwojima , Japan. Remote Sensing of Environment, 99, 95–104.