Reliable and calibrated imaging strategies are essential for tackling challenging real-world imaging problems. Developing these strategies involves interdisciplinary approaches that translate inverse problem tooling into avenues for real scientific insight. However, raw scientific measurements present many challenges that modern imaging techniques do not adequately address. For instance, many state-of-the-art imaging methods struggle with seismic imaging problems due to several factors including non-linear forward models, uncalibrated noise, sensitivity to initial conditions, and limited angular observations. This thesis aims to overcome these challenges by presenting methods that image under uncertainty, leverage unconventional sources of regularization, and reshape the way we solve imaging inverse problems, ultimately enabling new scientific discoveries.
Angela Fang Gao (Thu,) studied this question.