Quantifying Uncertainty in Subsurface Systems

Under the Earth's surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge. Volume highlights include: A multi-disciplinary treatment of uncertainty quantification Case studies with actual data that will appeal to methodology developers A Bayesian evidential learning framework that reduces computation and modeling time Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. Read the Editors' Vox: eos.org/editors-vox/quantifying-uncertainty-about-earths-resources

Preface vii Authors xi 1. The Earth Resources Challenge 1 2. Decision Making Under Uncertainty 29 3. Data Science for Uncertainty Quantification 45 4. Sensitivity Analysis 107 5. Bayesianism 129 6. Geological Priors and Inversion 155 7. Bayesian Evidential Learning 193 8. Quantifying Uncertainty in Subsurface Systems 217 9. Software and Implementation 263 10. Outlook 267 Index 273