Three Dimensional Modeling With Geoscientific Information Systems

A. K. TURNER Department of Geology and Geological Engineering Colorado School of Mines Golden, Colorado 80401 USA Geology deals with three-dimensional data. Geoscientists are concerned with three dimensional spatial observations, measurements, and explanations of a great variety of phenomena. The representation of three-dimensional data has always been a problem. Prior to computers, graphical displays involved specialized maps, cross-sections, fence diagrams, and geometrical constructions such as stereonets. All were designed to portray three-dimensional relationships on two-dimensional paper products, and all were time consuming to develop. Until recently, computers were of little assistance to three-dimensional data handling and representation problems. Memory was too expensive to handle the huge amounts of data required by three-dimensional assessments; computational speeds were too slow to perform the necessary calculations within a reasonable time; and graphical displays had too Iowa resolution or were much too expensive to produce useful visualizations. Much experience was gained with two-dimensional geographic information systems (GIS), which were applied to many land-use management and resource assessment problems. The two-dimensional GIS field matured rapidly in the late 1980's and became widely accepted. The advent of the modern computer workstation, with its enhanced memory and graphical capabilities at ever more affordable prices, has largely overcome these earlier constraints.

Characterisation of the shallow subsurface has gained in importance as civil and geotechnical engineering and environmental applications have become more dependent on a precise definition of geomechanical and geohydrological properties. A better understanding of the subsurface conditions offers wide-ranging benefits to governments, industry and individual citizens. Subsurface geological modelling became an economic and technologic reality in the late 1980's, when competing 3-D geoscientific information systems were the subject of considerable research and evaluation, especially by the petroleum exploration industry. Investigations in the shallow subsurface impose additional requirements that have only recently become technically and economically achievable. The very shallow urban underground environment, where many infrastructure and utilities elements are located, presents the most difficult characterisation problems. Subsurface modelling techniques have matured, along with modern data base concepts. The evolution of the Internet and Web-browser technologies has expanded information transmission and dissemination capabilities. Subsurface models are being integrated with decision-support systems to provide predictions of technical and economic performance. Yet even the most sophisticated of these models leave some uncertainty in geologic interpretation. A variety of techniques for assessing uncertainty have been developed and are being evaluated.

This book is a result of a career spent developing and applying computer techniques for the geosciences. The need for a geoscience modeling reference became apparent during participation in several workshops and conferences on the subject in the last three years. For organizing these, and for the lively discussions that ensued and inevitably contributed to the contents, I thank Keith Turner, Brian Kelk, George Pflug and Johnathan Raper. The total number of colleagues who contributed in various ways over the preceding years to the concepts and techniques presented is beyond count. The book is dedicated to all of them. Compilation of the book would have been impossible without assistance from a number of colleagues who contributed directly. In particular, Ed Rychkun, Joe Ringwald, Dave Elliott, Tom Fisher and Richard Saccany reviewed parts of the text and contributed valuable comment. Mohan Srivastava reviewed and contributed to some of the geostatistical presentations. Mark Stoakes, Peter Dettlaff and Simon Wigzell assisted with computer processing of the many application examples. Anar Khanji and Randal Crombe assisted in preparation of the text and computer images. Klaus Lamers assisted with printing. The US Geological Survey, the British Columbia Ministry of Environment, Dave Elliott and others provided data for the application examples. My sincere thanks to all of them.

A significant part of understanding how people use geographic information and technology concerns human cognition. This book provides the first comprehensive in-depth examination of the cognitive aspects of human-computer interaction for geographic information systems (GIS). Cognitive aspects are treated in relation to individual, group, behavioral, institutional, and cultural perspectives. Extensions of GIS in the form of spatial decision support systems and SDSS for groups are part of the geographic information technology considered. Audience: Geographic information users, systems analysts and system designers, researchers in human-computer interaction will find this book an information resource for understanding cognitive aspects of geographic information technology use, and the methods appropriate for examining this use.

Large-Scale 3D Data Integration: Challenges and Opportunities examines the fundamental aspects of 3D geo-information, focusing on the latest developments in 3D GIS (geographic information) and AEC (architecture, engineering, construction) systems. This book addresses policy makers, designers and engineers, and individuals that need to overco

Geographic Information Systems for Geoscientists: Modelling with GIS provides an introduction to the ideas and practice of GIS to students and professionals from a variety of geoscience backgrounds. The emphasis in the book is to show how spatial data from various sources (principally paper maps, digital images and tabular data from point samples) can be captured in a GIS database, manipulated, and transformed to extract particular features in the data, and combined together to produce new derived maps, that are useful for decision-making and for understanding spatial interrelationship. The book begins by defining the meaning, purpose, and functions of GIS. It then illustrates a typical GIS application. Subsequent chapters discuss methods for organizing spatial data in a GIS; data input and data visualization; transformation of spatial data from one data structure to another; and the combination, analysis, and modeling of maps in both raster and vector formats. This book is intended as both a textbook for a course on GIS, and also for those professional geoscientists who wish to understand something about the subject. Readers with a mathematical bent will get more out of the later chapters, but relatively non-numerate individuals will understand the general purpose and approach, and will be able to apply methods of map modeling to clearly-defined problems.

Sustainable Geoscience for Natural Gas SubSurface Systems delivers many of the scientific fundamentals needed in the natural gas industry, including coal-seam gas reservoir characterization and fracture analysis modeling for shale and tight gas reservoirs. Advanced research includes machine learning applications for well log and facies analysis, 3D gas property geological modeling, and X-ray CT scanning to reduce environmental hazards. Supported by corporate and academic contributors, along with two well-distinguished editors, the book gives today’s natural gas engineers both fundamentals and advances in a convenient resource, with a zero-carbon future in mind. Includes structured case studies to illustrate how new principles can be applied in practical situations Helps readers understand advanced topics, including machine learning applications to optimize predictions, controls and improve knowledge-based applications Provides tactics to accelerate emission reductions Teaches gas fracturing mechanics aimed at reducing environmental impacts, along with enhanced oil recovery technologies that capture carbon dioxide

Provides a survey of the approaches used and the problems encountered in the model of real geophysical data.