Subsurface Data Assimilation
Theory and Applications
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Product details:
- Publisher Elsevier
- Date of Publication 25 October 2026
- ISBN 9780443415432
- Binding Paperback
- No. of pages300 pages
- Size 229x152 mm
- Weight 450 g
- Language English 700
Categories
Long description:
Subsurface Data Assimilation: Theory and Applications provides a comprehensive exploration of data assimilation algorithms applied to subsurface characterization and monitoring. The book begins with 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.
- Bridges the potential gaps between theoretical analysis and practical applicability
- Analyzes state-of-the-art research fronts in subsurface data assimilation problems
- Describes hands-on experience from respective field experts in diverse subsurface data assimilation cases
Table of 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. CO? 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
