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
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vii | |
| Preface |
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ix | |
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1 70 years of machine learning in geoscience in review |
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1 | (56) |
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1 Historic machine learning in geoscience |
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3 | (7) |
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2 Contemporary machine learning in geoscience |
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10 | (33) |
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43 | (14) |
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2 Machine learning and fault rupture: A review |
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57 | (52) |
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57 | (4) |
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2 Machine learning: A shallow dive |
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61 | (5) |
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66 | (9) |
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75 | (20) |
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95 | (2) |
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97 | (1) |
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97 | (12) |
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3 Machine learning techniques for fractured media |
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109 | (42) |
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109 | (4) |
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113 | (9) |
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3 Graph as a DFN reduced-order model |
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122 | (1) |
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4 Pruned DFN as a reduced-order model |
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122 | (2) |
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5 Machine learning methods for backbone identification |
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124 | (20) |
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6 Further scope for ML in fractured media |
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144 | (1) |
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144 | (6) |
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150 | (1) |
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4 Seismic signal augmentation to improve generalization of deep neural networks |
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151 | (28) |
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151 | (3) |
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2 Benchmark data and training procedure |
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154 | (2) |
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156 | (14) |
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170 | (3) |
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173 | (1) |
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174 | (1) |
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174 | (5) |
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5 Deep generator priors for Bayesian seismic inversion |
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179 | (38) |
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179 | (4) |
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183 | (4) |
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3 Seismic inversion applications |
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187 | (1) |
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188 | (24) |
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5 Conclusions and discussion |
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212 | (2) |
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214 | (1) |
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214 | (3) |
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6 An introduction to the two-scale homogenization method for seismology |
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217 | |
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218 | (4) |
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2 Mathematical notions and notations |
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222 | (4) |
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3 A numerical introduction to the subject |
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226 | (11) |
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4 Two-scale homogenization: the 1-D periodic case |
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237 | (15) |
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5 Two-scale homogenization: The 1-D nonperiodic case |
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252 | (14) |
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6 Two-scale homogenization: Higher dimensions |
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266 | (7) |
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273 | (3) |
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8 Examples of applications |
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276 | (23) |
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9 Discussion and conclusions |
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299 | (3) |
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302 | (1) |
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302 | |
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.
