Subsurface Data Assimilation : Theory and Applications

Luo, Xiaodong (EDT)/ Leeuwenburgh, Olwijn (EDT)/ Emerick, Alexandre Anoze (EDT)

Elsevier - Health Sciences Division（2026/06発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 300 p.
• 言語 ENG
• 商品コード 9780443415432

Full Description

Subsurface Data Assimilation: Theory and Applications provides a comprehensive exploration of data assimilation algorithms applied to subsurface characterization and monitoring. The book begins by establishing the theoretical foundations of data assimilation methods, including multilevel data assimilation, coupled data assimilation with machine learning, and generative neural networks for geological parameterization. It also introduces Latent-Space Data Assimilation (LSDA), leveraging deep learning for feature-based analysis and forecasting, and geostatistical seismic inversion techniques. The second part of the book looks into the practical applications of data assimilation in various subsurface problems. Chapters explore CO2 monitoring, geologic CO2 sequestration, and the use of data assimilation for earthquake or CO2 storage scenarios. Hierarchical data assimilation procedures for carbon storage with uncertain geological scenarios are discussed, along with applications of data assimilation in geothermal energy contexts. The book also addresses practical uncertainty management practices and challenges related to CO2 storage and geothermal energy projects. By combining theoretical foundations with real-world applications, this book serves as a valuable resource for researchers and practitioners in the field of subsurface data assimilation, offering insights into cutting-edge methods and their practical implications for subsurface characterization and monitoring.

Contents

Part I: Theoretical Foundations of Data Assimilation Algorithms
1. Recent Progresses of Data Assimilation Methods Applied to Subsurface Characterization and Monitoring Problems
2. Multilevel Data Assimilation
3. Coupled Data Assimilation and Machine Learning
4. Generative Neural Networks for Geological Parameterization
5. Latent-Space Data Assimilation (LSDA): Leveraging Deep Learning for Feature-Based Analysis and Forecasting
6. Geostatistical Seismic Inversion

Part II: Applications to Various Subsurface Problems
7. CO₂ Monitoring
8. Geologic CO2 Sequestration
9. Earthquake or CO2 Storage
10. Hierarchical Data Assimilation Procedures for Carbon Storage with Uncertain Geological Scenario
11. Geothermal Energy
12. Practical Uncertainty Management, Practices, and Challenges in CO2 Storage/Geothermal Energy