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.