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Machine Learning and Artificial Intelligence in Geosciences - 1st Edition - ISBN: 9780128216699

Machine Learning and Artificial Intelligence in Geosciences, Volume 61

1st Edition

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Serial Volume Editors: Benjamin Moseley Lion Krischer
Hardcover ISBN: 9780128216699
Imprint: Academic Press
Published Date: 22nd September 2020
Page Count: 316
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Table of Contents

  1. Preface
  2. 70 years of machine learning in geoscience in review
  3. Jesper Sören Dramsch

  4. Machine learning and fault rupture: A review
  5. Christopher X. Ren, Claudia Hulbert, Paul A. Johnson and Bertrand Rouet-Leduc

  6. Machine learning techniques for fractured media
  7. Shriram Srinivasan

  8. Seismic signal augmentation to improve generalization of deep neural networks
  9. Weiqiang Zhu , S. Mostafa Mousavi and Gregory C. Beroza

  10. Deep generator priors for Bayesian seismic inversion
  11. Zhilong Fang, Hongjian Fang and L. Demanet

  12. 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.

Key Features

  • 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


No. of pages:
© Academic Press 2020
22nd September 2020
Academic Press
Hardcover ISBN:

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About the Serial Volume Editors

Benjamin Moseley

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

Affiliations and Expertise

Department of Computer Science, University of Oxford NASA Frontier Development Lab, Mountain View, CA, USA

Lion Krischer

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.

Affiliations and Expertise

Department of Earth Sciences at the ETH Zurich in Switzerland.