The lithosphere underneath the Antarctic ice-sheet is geologically complex. It interacts with the ice-sheet by providing fundamental control for ice-sheet flow. However, its heterogeneity is poorly understood, which limits our capacity to predict Antarctic ice-sheet evolution in the future. In this thesis, I use machine learning method and gravity inversion to build a self-consistent 3D lithosphere model. Model results reveal density distributions and important surfaces, including sedimentary basin, Moho, and lithosphere and asthenosphere boundary. Based on the newly resolved structure, I explore the related physical processes and basal boundary conditions for understanding ice-sheet flow.
|Qualification||Doctor of Philosophy|
|Award date||13 Apr 2023|
|Publication status||Unpublished - 2022|