Machine Learning and Artificial Intelligence in Geosciences

by Benjamin Moseley (Editor), Lion Krischer (Editor)

Overview

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. - 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

Product Details

ISBN-13:	9780128216842
Publisher:	Elsevier Science & Technology Books
Publication date:	09/22/2020
Series:	Advances in Geophysics , #61
Sold by:	Barnes & Noble
Format:	eBook
Pages:	316
File size:	39 MB
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About the Author

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.

Table of Contents

1. Preface2. 70 years of machine learning in geoscience in reviewJesper Sören Dramsch3. Machine learning and fault rupture: A reviewChristopher X. Ren, Claudia Hulbert, Paul A. Johnson and Bertrand Rouet-Leduc4. Machine learning techniques for fractured mediaShriram Srinivasan5. Seismic signal augmentation to improve generalization of deep neural networksWeiqiang Zhu , S. Mostafa Mousavi and Gregory C. Beroza6. Deep generator priors for Bayesian seismic inversionZhilong Fang, Hongjian Fang and L. Demanet7. An introduction to the two-scale homogenization method for seismologyYann Capdeville, Paul Cupillard and Sneha Singh

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