Emerging Directions in Geophysical Inversion
arXiv:2110.06017 · doi:10.1017/9781009180412.003
Abstract
In this chapter, we survey some recent developments in the field of geophysical inversion. We aim to provide an accessible general introduction to the breadth of current research, rather than focussing in depth on particular topics. In particular, we hope to give the reader an appreciation for the similarities and connections between different approaches, and their relative strengths and weaknesses.
32 pages, 2 figures. Original manuscript submitted for review as a chapter of "Data Assimilation and Inverse Problems in Geophysical Sciences", eds. Alik Ismail-Zadeh, Fabio Castelli, Dylan Jones and Sabrina Sanchez
References in corpus (5)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Edward: A library for probabilistic modeling, inference, and criticism
- Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
- HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
- Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models