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.
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Table of Contents
1. Preface 2. 70 years of machine learning in geoscience in review Jesper Sï¿½ren 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