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.
arXiv comment32 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 (21)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Auto-Encoding Variational Bayes
- TensorFlow: A system for large-scale machine learning
- Generative Adversarial Networks: An Overview
- MCMC using Hamiltonian dynamics
- Variational Inference with Normalizing Flows
- Normalizing Flows: An Introduction and Review of Current Methods
- A Conceptual Introduction to Hamiltonian Monte Carlo
- Data analysis recipes: Using Markov Chain Monte Carlo
- Solving the acoustic VTI wave equation using physics-informed neural networks
- Edward: A library for probabilistic modeling, inference, and criticism
- EikoNet: Solving the Eikonal equation with Deep Neural Networks
- Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
- Fast approximate simulation of seismic waves with deep learning
- HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks
- Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
- Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
- Probabilistic Neural Network Tomography across Grane field (North Sea) from Surface Wave Dispersion Data
- Informed Proposal Monte Carlo
- Accelerating Bayesian microseismic event location with deep learning
- Travel time tomography with adaptive dictionaries