Machine Learning and Artificial Intelligence in Geosciences, Volume 61

1. Preface
2. 70 years of machine learning in geoscience in review
Jesper Sören Dramsch
3. Machine learning and fault rupture: A review
Christopher X. Ren, Claudia Hulbert, Paul A. Johnson and Bertrand Rouet-Leduc
4. Machine learning techniques for fractured media
Shriram Srinivasan
5. Seismic signal augmentation to improve generalization of deep neural networks
Weiqiang Zhu , S. Mostafa Mousavi and Gregory C. Beroza
6. Deep generator priors for Bayesian seismic inversion
Zhilong Fang, Hongjian Fang and L. Demanet
7. An introduction to the two-scale homogenization method for seismology
Yann Capdeville, Paul Cupillard and Sneha Singh

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more.

• Provides high-level reviews of the latest innovations in geophysics
• Written by recognized experts in the field
• Presents an essential publication for researchers in all fields of geophysics

Graduate students, scientists and engineers of geophysics, physics, acoustics, civil engineering, environmental sciences, geology and planetary sciences