Inference and machine learning across the spatial scales in geophysics - Szenicer, A (2021)
The Anthropocene and the concomitant Great Acceleration – defined by super-exponential growth rates in a collection of measures of human activity – are having a tremendous impact on Earth and its ecosystems. To better understand the consequences of our activity, we must increase our knowledge of all the elements in the Geosystem, loosely defined as Earth, the oceans, atmosphere, and the Sun, and in constant evolution as human activity ties in new elements, e.g. the Moon or Mars. In this thesis, I am focusing on improving our knowledge of elements pertaining to geophysics, while also showcasing the diversity of thematics in the Geosystem. I also emphasize the common grounds and cross-cutting themes in our sundry of projects, in particular the importance of a data-centric approach, including the creation of new sensors, and the necessity of both methodological improvements to our numerical tools, and the adoption of novel methods from machine learning to help us exploit the newly available vast amount of data.