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Modeling Uncertainty in the Earth Sciences

Author: Jef Caers

Publisher: John Wiley & Sons

Published: 2011-05-25

Total Pages: 294

ISBN-13: 1119998719

Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Applied Multidimensional Geological Modeling

Author: Alan Keith Turner

Publisher: John Wiley & Sons

Published: 2021-06-21

Total Pages: 51

ISBN-13: 1119163129

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Over the past decades, geological survey organizations have digitized their data handling and holdings, unlocking vast amounts of data and information for computer processing. They have undertaken 3-D modeling alongside, and in some cases instead of, conventional geological mapping and begun delivering both data and interpretations to increasingly diverse stakeholder communities. Applied Multidimensional Geological Modeling provides a citable central source that documents the current capabilities and contributions of leading geological survey organization and other practitioners in industry and academia that are producing multidimensional geological models. This book focuses on applications related to human interactions with conditions in the shallow subsurface, within 100-200 m of the surface. The 26 chapters, developed by 100 contributors associated with 37 organizations, discuss topics relevant to any geologist, scientist, engineer, urban planner, or decision maker whose practice includes assessment or planning of underground space.

Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

Author: Wenzhong Shi

Publisher: CRC Press

Published: 2009-09-30

Total Pages: 456

ISBN-13: 1420059289

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When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t

Spatial Modeling Principles in Earth Sciences

Author: Zekai Sen

Publisher: Springer Science & Business Media

Published: 2009-06-10

Total Pages: 358

ISBN-13: 1402096720

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Spatial Modeling Principles in Earth Sciences presents fundamentals of spatial data analysis used in hydrology, geology, meteorology, atmospheric science and related fields. It examines methods for the quantitative determination of the spatial distribution patterns. This book brings together the material from the current literature in earth sciences and practical examples. It provides a sound background of philosophical, logical, rational and physical principles of spatial data and analysis, and explains how it can be modeled and applied in earth sciences projects and designs. It collects information not previously available in one source, and provides methodology for the treatment of spatial data to find the most rational and practical solution. The book is a valuable resource for students, researchers and practitioners of a broad range of disciplines including geology, geography, hydrology, meteorology, environment, image processing, spatial modeling and related topics.

The Science and Management of Uncertainty

Author: Bruce G. Marcot

Publisher: CRC Press

Published: 2020-11-26

Total Pages: 278

ISBN-13: 1000244512

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Uncertainty can take many forms, can be represented in many ways, and can have important implications in decision-making and policy development. This book provides a rigorous scientific framework for dealing with uncertainty in real-world situations, and provides a comprehensive study of concepts, measurements, and applications of uncertainty in ecological modeling and natural resource management. The focus of this book is on the kinds and implications of uncertainty in environmental modeling and management, with practical guidelines and examples for successful modeling and risk analysis in the face of uncertain conditions and incomplete information. Provided is a clear classification of uncertainty; methods for measuring, modeling, and communicating uncertainty; practical guidelines for capturing and representing expert knowledge and judgment; explanations of the role of uncertainty in decision-making; a guideline to avoiding logical fallacies when dealing with uncertainty; and several example cases of real-world ecological modeling and risk analysis to illustrate the concepts and approaches. Case topics provide examples of structured decision-making, statistical modeling, and related topics. A summary provides practical next steps that the reader can take in analyzing and interpreting uncertainty in real-world situations. Also provided is a glossary and a suite of references.

Spatial Modeling Principles in Earth Sciences

Author: Zekai Sen

Publisher: Springer

Published: 2016-10-04

Total Pages: 413

ISBN-13: 3319417584

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This is a revised and updated second edition, including new chapters on temporal and point uncertainty model, as well as on sampling and deterministic modeling. It is a comprehensive presentation of spatial modeling techniques used in the earth sciences, outlining original techniques developed by the author. Data collection in the earth sciences is difficult and expensive, but simple, rational and logical approaches help the reader to appreciate the fundamentals of advanced methodologies. It requires special care to gather accurate geological, hydrogeological, meteorological and hydrological information all with risk assessments. Spatial simulation methodologies in the earth sciences are essential, then, if we want to understand the variability in features such as fracture frequencies, rock quality, and grain size distribution in rock and porous media. This book outlines in a detailed yet accessible way the main spatial modeling techniques, in particular the Kriging methodology. It also presents many unique physical approaches, field cases, and sample interpretations. Since Kriging’s origin in the 1960s it has been developed into a number of new methods such as cumulative SV (CSV), point CSV (PCSV), and spatial dependence function, which have been applied in different aspects of the earth sciences. Each one of these techniques is explained in this book, as well as how they are used to model earth science phenomena such as geology, earthquakes, meteorology, and hydrology. In addition to Kriging and its variants, several alternatives to Kriging methodology are presented and the necessary steps in their applications are clearly explained. Simple spatial variation prediction methodologies are also revised with up-to-date literature, and the ways in which they relate to more advanced spatial modeling methodologies are explained. The book is a valuable resource for students, researchers and professionals of a broad range of disciplines including geology, geography, hydrology, meteorology, environment, image processing, spatial modeling and related topics. Keywords »Data mining - Geo-statistics - Kriging - Regional uncertainty - Spatial dependence - Spatial modeling - geographic data - geoscience - hydrology - image processing

Natural Hazard Uncertainty Assessment

Author: Karin Riley

Publisher: John Wiley & Sons

Published: 2016-12-12

Total Pages: 356

ISBN-13: 1119027861

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Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists. Read an interview with the editors to find out more: https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction

Uncertainty Analysis and Reservoir Modeling

Author: Y. Zee Ma

Publisher: AAPG

Published: 2011-12-20

Total Pages: 329

ISBN-13: 0891813780

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Quantifying Uncertainty in Subsurface Systems

Author: Céline Scheidt

Publisher: John Wiley & Sons

Published: 2018-06-19

Total Pages: 306

ISBN-13: 1119325838

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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

Random Field Models in Earth Sciences

Author: George Christakos

Publisher: Elsevier

Published: 2013-10-22

Total Pages: 503

ISBN-13: 1483288307

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This book is about modeling as a prinicipal component of scientific investigations. In general terms, modeling is the funamental process of combining intellectual creativity with physical knowledge and mathematical techniques in order to learn the properties of the mechanisms underlying a physical phenomenon and make predictions. The book focuses on a specific class of models, namely, random field models and certain of their physical applications in the context of a stochastic data analysis and processing research program. The term application is considered here in the sense wherein the mathematical random field model is shaping, but is also being shaped by, its objects. This book explores the application of random field models and stochastic data processing to problems in hydrogeology, geostatistics, climate modeling, and oil reservoir engineering, among others Researchers in the geosciences who work with models of natural processes will find discussion of; Spatiotemporal random fields Space transformation Multidimensional estimation Simulation Sampling design Stochastic partial differential equations