Evaluating mechanisms for intraplate volcanism using the observed distribution of seamounts
This project aims to use sophisticated computational methods for improving our knowledge of the location and characteristics of submarine volcanoes.
Examples of seamounts shape and distribution using high resolution bathymetry (upper panel), seismic reflection data (lower panel left), and gravity anomaly from satellite altimetry (lower panel right).
The ocean floor is littered with volcanoes, whose formation, topography, and volcanic activity contribute to local and regional ocean circulation, as well as seafloor chemistry and habitat. This volcanism is attributed to magmatic processes connected to the formation of new oceanic crust, or to the modification of the crust by subsequent intra-plate volcanism.
To date hundreds of thousands submarine volcanoes have been identified using data collected remotely from autonomous underwater vehicles, ships and satellites (Figure 1). However, a much higher number remain undiscovered due to the lack of better detection methods, particularly for deeply submerged or thickly sedimented seafloor.
This project aims to use sophisticated computational methods for improving our knowledge of the location and characteristics of submarine volcanoes. We plan to use a variety of data with different resolutions as input for machine learning and automatic detection algorithms that will identify submarine features including seamounts.
Furthermore, we will combine models of oceanic lithospheric age with bathymetric and gravity anomaly data to infer the age of various seamounts. Finally, we will analyse patterns of seamount abundance and morphology together with geodynamic models to understand possible causes for submarine volcanism both for the present day and the geological past.
The study will be done in collaboration with the Institute for Marine and Antarctic Studies (IMAS) at the University of Tasmania, Australia.
- MSc in geosciences, preferably in geodynamics, marine geophysics .
- Candidates with documented experience in scientific programming and machine learning will be prioritized.
Call 2: Project start autumn 2022
This project is in call 2, starting autumn 2022.