Decision-Making for Earth Resource Development

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.

List of contributors vii Preface ix Acknowledgments xi 1 Real-WorldDecision-MakingCases 1 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 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 GeologicalPriorsandInversion 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-MakinginDevelopingEarthResources 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