Computational Geodynamics

How can physical models and observations reveal the evolving Earth?

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Last updated: August 2026

About

Earth's interior cannot be observed directly, yet it controls plate motion, volcanism, mountain building, sea level, landscapes, and long-term climate. Computational geodynamics combines physical theory, numerical simulation, and observations to infer these hidden processes and to test whether proposed explanations are consistent with the geological record.

Our work links mantle convection and plate tectonics to surface observations across a wide range of spatial and temporal scales. We develop models that use geological, geophysical, geochemical, and geodetic data to reconstruct past Earth evolution, quantify uncertainty, and identify the processes that can explain the observations.

Research questions

  • How do mantle flow and plate tectonics produce dynamic topography, intraplate volcanism, and changes at Earth's surface?
  • Which observations constrain the past state and material properties of the mantle?
  • How can inverse methods and data assimilation make geodynamic models testable?

Methods and data

Projects can use mantle-convection modelling, adjoint methods, plate reconstructions, seismic tomography, gravity and geoid data, geochemistry, geological records, and high-performance computing. The balance between modelling, data analysis, and theory depends on the student's background and the research question.

Possible projects

Possible directions include reconstructing mantle flow from present-day observations, relating deep mantle structure to surface topography and volcanism, quantifying the effects of mantle structure on Antarctica and sea level, and developing efficient numerical methods for large inverse problems.

Essential background

Projects suit students from Earth sciences, physics, mathematics, computer science, engineering, geochemistry, or related disciplines. Useful preparation includes fluid dynamics, programming, numerical methods, geophysics, or data analysis. Students can learn missing computational skills during the project.

Members

Supervisor

Director
Professor

Dr Sia Ghelichkhan

Lecturer
Institute for Water Futures
ARC DECRA Fellow

Professor

Professor
Associate Director Research & Engagement