finelybook
Decision-Making for Earth Resource Development: Quantifying Uncertainty (Geophysical Monograph Series)
Author(s): Jef Caers (Editor), Céline Scheidt (Editor), Lewis Li (Editor)
- Publisher Finelybook 出版社: American Geophysical Union
- Publication Date 出版日期: July 27, 2026
- Edition 版本: 1st
- Language 语言: English
- Print length 页数: 384 pages
- ISBN-10: 1394306768
- ISBN-13: 9781394306763
Book Description
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.
Editorial Reviews
Editorial Reviews
From the Back Cover
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.
About the Author
Jef Caers,Stanford University, USA.
Céline Scheidt,Stanford University, USA.
Lewis Li,Chevron, USA.
