Del 293 i serien Geophysical Monograph Series

Decision-Making for Earth Resource Development

Nyhet

Quantifying Uncertainty

Inbunden, Engelska, 2026

AvJef Caers,Céline Scheidt,Lewis Li

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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 morePopper-Bayes protocol for formulating and solving uncertainty quantification problemsMachine learning approaches for Bayesian inversionDecision-making with AI and high-performance computingInvestigation models using global sensitivity analysis in the geosciencesSoftware development for large-scale practical implementationThe 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.

Produktinformation

Utgivningsdatum2026-07-15
Mått218 x 277 x 23 mm
Vikt1 270 g
FormatInbunden
SpråkEngelska
SerieGeophysical Monograph Series
Antal sidor384
Upplaga26001
FörlagJohn Wiley & Sons Inc
ISBN9781394306763

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List of contributors viiPreface ixAcknowledgments xi1 Real-WorldDecision-MakingCases 1Jef 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 li2 Decision-Making Under Uncertainty Using Artificial Intelligence 35Jef Caers, Mansur Arief, Céline Scheidt, and Lewis li3 Data Science and Machine Learning for Uncertainty Quantification 83Jef Caers, Céline Scheidt, and Lewis li4 Sensitivity Analysis 147Jef Caers, Céline Scheidt, and Lewis li5 How to Think About Uncertainty: A Popper–Bayes Philosophy 173Jef Caers, Céline Scheidt, and Lewis li6 GeologicalPriorsandInversion 199Jef Caers, Céline Scheidt, and Lewis li7 A Popper–Bayes Protocol for Uncertainty Quantification in the Context of Decision-Making 265Jef Caers, Céline Scheidt, and Lewis li8 Decision-MakinginDevelopingEarthResources 287Jef 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 li9 Software Engineering and Implementation 345Duncan EddyIndex 359