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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
Graduate students, scientists and engineers of geophysics, physics, acoustics, civil engineering, environmental sciences, geology and planetary sciences
- 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
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, USALK
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.Read Machine Learning and Artificial Intelligence in Geosciences on ScienceDirect