Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences

1st Edition - September 22, 2020
This is the Latest Edition
  • Editors: Benjamin Moseley, Lion Krischer
  • eBook ISBN: 9780128216842
  • Hardcover ISBN: 9780128216699

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Description

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

Readership

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

Table of Contents

  • 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

Product details

  • No. of pages: 316
  • Language: English
  • Copyright: © Academic Press 2020
  • Published: September 22, 2020
  • Imprint: Academic Press
  • eBook ISBN: 9780128216842
  • Hardcover ISBN: 9780128216699
  • 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.