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04/06/2018 Suzanne Atkins (LGLTPE)

Finding the patterns in mantle convection using machine learning (à la Doua, salle Fontannes)
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Finding the patterns in mantle convection using machine learning

The surface and the interior of the Earth have a complex and mutually dependent existence. Trying to unravel the physics and history of one requires detailed knowledge about the state of the other. Unfortunately, neither the physics nor the history of either are particularly well constrained.  This leaves many open questions for geophysicists, such as what is the deep interior of the Earth doing today, how did the Earth get a dynamo and how did plate tectonics start. One of many limiting factors for these questions is the sheer scale of the problems: highly non-linear systems, hundreds of unknowns, and few observations. These features mean that we have to approach the problem in a probabilistic manner, whilst at the same time making conventional probabilistic studies fantastically expensive. Machine learning gives us a feasible way to tackle problems of this scale, and gives us a way to study mantle convection in a statistical manner for the first time.

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