Machine Learning and Artificial Intelligence in Geosciences

Inbunden, Engelska, 2020

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Beskrivning

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

Produktinformation

Utgivningsdatum:2020-09-22
Mått:152 x 229 x undefined mm
Vikt:610 g
Format:Inbunden
Språk:Engelska
Serie:Advances in Geophysics
Antal sidor:316
Förlag:Elsevier Science
ISBN:9780128216699

Utforska kategorier

Geologi inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Tillämpad fysik inom Naturvetenskap och teknik

Mer om författaren

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.

Innehållsförteckning

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