Geological prior information represents a new and emerging field within the geosciences. Prior information is the term used to describe previously existing knowledge that can be brought to bear on a new problem. This volume describes a range of methods that can be used to find solutions to practical and theoretical problems using geological prior information, and the nature of geological information that can be so employed.

Iowa's rock record is the product of more than three billion years of geological processes. The state endured multiple episodes of continental glaciation during the Pleistocene Ice Age, and the last glacier retreated from Iowa a mere (geologically speaking) twelve thousand years ago. Prior to that, dozens of seas came and went, leaving behind limestone beds with rich fossil records. Lush coal swamps, salty lagoons, briny basins, enormous alluvial plains, ancient rifts, and rugged Precambrian mountain belts all left their mark. In "Iowa's Geological Past, " Wayne Anderson gives us an up-to-date and well-informed account of the state's vast geological history from the Precambrian through the end of the Great Ice Age. Anderson takes us on a journey backward into time to explore Iowa's rock-and-sediment record. In the distant past, prehistoric Iowa was covered with shallow seas; coniferous forests flourished in areas beyond the continental glaciers; and a wide variety of animals existed, including mastodon, mammoth, musk ox, giant beaver, camel, and giant sloth. The presence of humans can be traced back to the Paleo-Indian interval, 9,500 to 7,500 years ago. Iowa in Paleozoic time experienced numerous coastal plain and shallow marine environments. Early in the Precambrian, Iowa was part of ancient mountain belts in which granite and other rocks were formed well below the earth's surface. The hills and valleys of the Hawkeye State are not everlasting when viewed from the perspective of geologic time. Overall, Iowa's geologic column records an extraordinary transformation over more than three billion years. Wayne Anderson's profusely illustrated volume provides a comprehensive and accessible survey of the state's remarkable geological past.

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.

Geoscience data and collections (such as, rock and sediment cores, geophysical data, engineering records, and fossils) are necessary for industries to discover and develop domestic natural resources to fulfill the nation's energy and mineral requirements and to improve the prediction of immediate and long term hazards, such as land slides, volcanic eruptions and global climate change. While the nation has assembled a wealth of geoscience data and collections, their utility remains incompletely tapped. Many could act as invaluable resources in the future but immediate action is needed if they are to remain available. Housing of and access to geoscience data and collections have become critical issues for industry, federal and state agencies, museums, and universities. Many resources are in imminent danger of being lost through mismanagement, neglect, or disposal. A striking 46 percent of the state geological surveys polled by the committee reported that there is no space available or they have refused to accept new material. In order to address these challenges, Geoscience Data and Collections offers a comprehensive strategy for managing geoscience data and collections in the United States.

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

This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications. The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.