Machine Learning and Artificial Intelligence in Geosciences: Volume 61

Informationen zum Autor Ben Moseley works at the Department of Computer Science at the University of Oxford and is currently researching the use of machine learning for seismic simulation and inversion, as well as machine learning for space science. Previously he was a geophysicist in the hydrocarbon industry, with experience in seismic processing, imaging and exploration Lion Krischer works at the Department of Earth Sciences at the ETH Zurich in Switzerland. His works sits at the crossroads where seismology meets computational science, Big Data engineering, and machine learning.

Autorentext
Ben Moseley works at the Department of Computer Science at the University of Oxford and is currently researching the use of machine learning for seismic simulation and inversion, as well as machine learning for space science. Previously he was a geophysicist in the hydrocarbon industry, with experience in seismic processing, imaging and exploration Lion Krischer works at the Department of Earth Sciences at the ETH Zurich in Switzerland. His works sits at the crossroads where seismology meets computational science, Big Data engineering, and machine learning.

Inhalt

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