Skip to main content
Elsevier
View Cart

Machine Learning and Artificial Intelligence in Geosciences

  • 1st Edition, Volume 61 - September 22, 2020
  • Latest edition
  • Editors: Benjamin Moseley, Lion Krischer
  • Language: English

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysic… Read more

World Book Day celebration

Where learning shapes lives

Up to 25% off trusted resources that support research, study, and discovery.

Explore resources

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

  • Edition: 1
  • Latest edition
  • Volume: 61
  • Published: September 22, 2020
  • Language: English

About the editors

BM

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

LK

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.

View book on ScienceDirect

Read Machine Learning and Artificial Intelligence in Geosciences on ScienceDirect