Machine Learning and Artificial Intelligence in Geosciences, Volume 61

Erscheinungsjahr 2020
ISBN: 978-0-12-821669-9
Verlag: ACADEMIC PR INC

Buch, Englisch, Band 61, 316 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 610 g

Reihe: Advances in Geophysics, Volume

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.

Zielgruppe

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

Autoren/Hrsg.

Moseley, Benjamin

Weitere Mitwirkende

Moseley, Benjamin

Krischer, Lion

Fachgebiete

• Geowissenschaften Geologie Geologie
• Naturwissenschaften Physik Angewandte Physik Geophysik
• Geowissenschaften Geologie Geophysik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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

Über Autor(innen)

Moseley, Benjamin
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

Krischer, Lion
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.