Image Segmentation in the Geosciences

From Fundamentals to Frontiers
Erscheinungsjahr 2027
ISBN: 978-0-443-44472-2
Verlag: Elsevier Science

Buch, Englisch, 280 Seiten, Format (B × H): 152 mm x 229 mm

Image Segmentation in Geosciences: From Fundamentals to Frontiers is the definitive resource for researchers, practitioners, and students working with complex geological imagery. This volume addresses the increasing need for precise, quantitative interpretation of diverse datasets, from micro-CT scans to satellite imagery, by consolidating classical and modern computational methodologies. As the volume of geoscience data expands, the book emphasizes the necessity of advanced segmentation techniques, including machine learning and deep learning, to transform raw data into actionable insights. It systematically covers core principles, innovative AI-driven approaches, and applications across reservoir characterization, geophysics, remote sensing, and environmental monitoring, supported by detailed case studies. The content spans methods, fractal and multifractal analysis, and cutting-edge AI architectures like CNNs, GANs, and transformers. It guides readers through practical workflows, visualization tools, and evaluation metrics, enabling them to bridge the gap between theoretical algorithms and real-world geoscience problems. This book empowers geoscientists, data scientists, and engineers to leverage the latest computational advances for more accurate resource assessment, hazard detection, and environmental management, fostering a new era of data-driven Earth sciences.

Autoren/Hrsg.

Koeshidayatullah, Ardiansyah

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

1. Introduction: The Imperative of Image Segmentation in Modern Geosciences

Part I: Foundational Methodologies: The Building Blocks of Segmentation
2. Classical Image Segmentation Techniques for Geological Data
3. Characterizing Complexity: Fractal and Multifractal Analysis of Geological Structures

Part II: The AI Revolution: Machine Learning and Deep Learning for Advanced Segmentation
4. Fundamentals of Machine Learning for Geoscience Image Segmentation
5. Deep Learning Architectures: Unleashing Automated Segmentation Power

Part III: Subsurface Realms: Segmentation for Reservoir Characterization, Geophysics, and Geomechanics
6. Pore-Scale to Core-Scale Segmentation for Reservoir Characterization
7. Seismic Image Segmentation: Unlocking Subsurface Structures and Stratigraphy
8. Quantifying Rock Mass Discontinuities: Image Segmentation in Geomechanics

Part IV: Surface Expressions: Segmentation in Remote Sensing, Environmental Monitoring, and Geohazards
9. Remote Sensing Image Segmentation for Geological Mapping and Mineral Exploration
10. Time-Lapse Image Segmentation: Monitoring Dynamic Earth Processes and Hazards
11. Environmental Geoscience Applications of Image Segmentation

Part V: The Path Forward: Challenges, Integration, and Future Frontiers
12. Data Preprocessing, Augmentation, and Ground Truth Generation for Geoscience Imagery
13. Validation, Uncertainty Quantification, and Explainable AI in Geoscience Segmentation
14. Integrating Image Segmentation with Geospatial and Geostatistical Workflows
15. Conclusion: The Future of Quantitative Geoscience Through Advanced Image Segmentation

Über Autor(innen)

Koeshidayatullah, Ardiansyah
Ardiansyah Koeshidayatullah is an Associate Professor in the Geosciences Department, College of Petroleum Engineering and Geosciences (CPG), KFUPM, and currently leads the Paleo3, Reservoir, Diagenesis Characterization and Technology (PREDICT) group. As a multidisciplinary geoscientist, his core research interests lie at the intersection of carbonate sedimentology-geochemistry and artificial intelligence. He particularly focuses on (i) coupling multi-proxy sedimentary-geochemistry analysis with numerical models to understand past Earth conditions and diagenetic fluid origins; and (ii) leveraging state-of-the-art artificial intelligence to automate and optimize the identification, interpretation, and reservoir characterization of sedimentary rocks.