Finding the patterns in mantle convection

It is very difficult to study mantle convection over periods of millions of years because convection is a non-linear process. In this thesis, I present a new method for studying the underlying statistics in convection patterns. I use neural networks to find patterns in these statistics to make inferences about the mantle and its history. I can make inferences about constant rheological parameters, evolving time dependent parameters, such as the development of LLSVPs, and the compositional, thermal and viscosity structure of the mantle. All of these parameters have important implications for the formation of Earth, evolution of plate tectonics and therefore life, interpretation of geophysical observations and understanding of dynamic processes. They are all current poorly constrained, making any new method potentially powerful. I also use neural networks as a predictive tool. Every inference is made using a Bayesian approach and is therefore fully probabilistic and includes uncertainty estimates. These uncertainty estimates are in themselve novel to geodynamics.