Machine Learning and Artificial Intelligence in Geosciences

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.

Sommario

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

Dettagli sul prodotto

• Autori: Benjamin (EDT)/ Krischer Moseley
• Con la collaborazione di: Lion Krischer (Editore), Benjamin Moseley (Editore)
• Editore: Academic Press London
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 01.09.2020
• EAN: 9780128216699
• ISBN: 978-0-12-821669-9
• Pagine: 232
• Serie: Advances in Geophysics
• Categorie: Scienze naturali, medicina, informatica, tecnica > Chimica > Altro; machine learning, Geophysics, SCIENCE / Earth Sciences / Geology, SCIENCE / Physics / Geophysics, The environment, Geology, geomorphology and the lithosphere, Geology & The Lithosphere, SCIENCE / Chemistry / Environmental