Machine Learning and Artificial Intelligence in Geosciences
- 1st Edition, Volume 61 - September 22, 2020
- Editors: Benjamin Moseley, Lion Krischer
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 6 6 9 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 6 8 4 - 2
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
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Request a sales quoteAdvances 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter One: 70 years of machine learning in geoscience in review
- Abstract
- 1: Historic machine learning in geoscience
- 2: Contemporary machine learning in geoscience
- Chapter Two: Machine learning and fault rupture: A review
- Abstract
- 1: Introduction
- 2: Machine learning: A shallow dive
- 3: Laboratory studies
- 4: Field studies
- 5: Conclusion
- Acknowledgments
- Chapter Three: Machine learning techniques for fractured media
- Abstract
- 1: Introduction
- 2: Preliminaries
- 3: Graph as a DFN reduced-order model
- 4: Pruned DFN as a reduced-order model
- 5: Machine learning methods for backbone identification
- 6: Further scope for ML in fractured media
- Chapter Four: Seismic signal augmentation to improve generalization of deep neural networks
- Abstract
- 1: Introduction
- 2: Benchmark data and training procedure
- 3: Augmentations
- 4: Discussion
- 5: Conclusions
- Acknowledgments
- Chapter Five: Deep generator priors for Bayesian seismic inversion
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Seismic inversion applications
- 4: Numerical examples
- 5: Conclusions and discussion
- Acknowledgments
- Chapter Six: An introduction to the two-scale homogenization method for seismology
- Abstract
- 1: Introduction
- 2: Mathematical notions and notations
- 3: A numerical introduction to the subject
- 4: Two-scale homogenization: the 1-D periodic case
- 5: Two-scale homogenization: The 1-D nonperiodic case
- 6: Two-scale homogenization: Higher dimensions
- 7: What we skipped
- 8: Examples of applications
- 9: Discussion and conclusions
- Acknowledgments
- No. of pages: 316
- Language: English
- Edition: 1
- Volume: 61
- Published: September 22, 2020
- Imprint: Academic Press
- Hardback ISBN: 9780128216699
- eBook ISBN: 9780128216842
BM
Benjamin Moseley
LK