Decision-Making for Earth Resource Development

Quantifying Uncertainty
1. Auflage 2026
ISBN: 978-1-394-30676-3
Verlag: John Wiley & Sons Inc

Buch, Englisch, 384 Seiten

Reihe: Geophysical Monograph Series

Earth’s subsurface offers many vital resources—such as minerals, geothermal energy, and clean water—but decisions regarding exploration and extraction must balance resource value against environmental impact. This can only be addressed by accepting uncertainty as an integral part of most decisions.

Decision-Making for Earth Resource Development presents uncertainty quantification strategies tested on real cases using a Bayesian methodology that can be applied to a wide variety of decision problems.

Volume highlights include: - Six substantial case studies, covering mineral exploration, geothermal heat feasibility, groundwater management, and more
- Popper-Bayes protocol for formulating and solving uncertainty quantification problems
- Machine learning approaches for Bayesian inversion
- Decision-making with AI and high-performance computing
- Investigation models using global sensitivity analysis in the geosciences
- Software development for large-scale practical implementation

The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.

Autoren/Hrsg.

Caers, Jef

Scheidt, Céline

Li, Lewis

Fachgebiete

• Technische Wissenschaften Umwelttechnik | Umwelttechnologie Umwelttechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Technologien für Fossile Energieträger
• Geowissenschaften Geologie Geologie

Inhaltsverzeichnis

List of Contributors vii
Preface ix
Acknowledgments xi

1 Real-World Decision-Making Cases 1
Jef Caers, John Mern, Jihoon Park, Troels Vilhelmsen, Torsten Clemens, Markus Zechner, Anthony Corso, Maria-Magdalena Chiotoroiu, LukaTas, Thomas Hermans, Luk Peeters, Cameron Huddlestone-Holmes, Kate Holland, Rebecca Doble, Céline Scheidt, and Lewis Li

2 Decision-Making Under Uncertainty Using Artificial Intelligence 35
Jef Caers, Mansur Arief, Céline Scheidt, and Lewis Li

3 Data Science and Machine Learning for Uncertainty Quantification 83
Jef Caers, Céline Scheidt, and Lewis Li

4 Sensitivity Analysis 147
Jef Caers, Céline Scheidt, and Lewis Li

5 How to Think About Uncertainty: A Popper–Bayes Philosophy 173
Jef Caers, Céline Scheidt, and Lewis Li

6 Geological Priors and Inversion 199
Jef Caers, Céline Scheidt, and Lewis Li

7 A Popper–Bayes Protocol for Uncertainty Quantification in the Context of Decision-Making 265
Jef Caers, Céline Scheidt, and Lewis Li

8 Decision-Making in Developing Earth Resources 287
Jef Caers, John Mern, Jihoon Park, Troels Vilhelmsen, Torsten Clemens, Markus Zechner, Anthony Corso, Maria-Magdalena Chiotoroiu, Luka Tas, Thomas Hermans, Luk Peeters, Cameron Huddlestone-Holmes, Kate Holland, Rebecca Doble, Céline Scheidt, and Lewis Li

9 Software Engineering and Implementation 345
Duncan Eddy

Index 359

Über Autor(innen)

Jef Caers, Stanford University, USA.

Céline Scheidt, Stanford University, USA.

Lewis Li, Chevron, USA.