"The book is so easy to follow that many readers may not even need formal instruction and may find it suitable for self-tutoring. I predict the volume will be a standard geostatistical reference for the decade." --Mathematical Geology
The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms. This practical book provides a step-by-step guide to using SGeMS algorithms. It explains the underlying theory, demonstrates their implementation, discusses their potential limitations, and helps the user make an informed decision about the choice of one algorithm over another. Users can complete complex tasks using the embedded scripting language, and new algorithms can be developed and integrated through the SGeMS plug-in mechanism. SGeMS was the first software to provide algorithms for multiple-point statistics, and the book presents a discussion of the corresponding theory and applications. Incorporating the full SGeMS software (now available from www.cambridge.org/9781107403246), this book is a useful user-guide for Earth Science graduates and researchers, as well as practitioners of environmental mining and petroleum engineering.
In this introductory text the authors demonstrate how simple statistical methods can be used to analyze earth science data. In clear language, they explain how various forms of the estimation method called kriging can be employed for specific problems. The book highlights an instructive case study of a simulated deposit. This model helps students develop an understanding of how statistical tools work in real situations, and serves as a tutorial guide to help the reader through what may be their first independent geostatistical study. Though the authors have avoided mathematical formalism, the presentation is not simplistic and readers should be familiar with basic calculus and be able to find the minimum of a function by using the first derivative.
The papers in this volume provide a comprehensive account of the current methods and work in geostatistics, including recent theoretical developments and applications. Topics featured include: stochastic simulations, space-time modelling, and Bayesian framework.
This volume contains 40 selected full-text contributions from the Sixth European Conference on Geostatistics for Environmental Applications, geoENV IV, held in Rhodes, Greece, October 25-26, 2006. The objective of the editors was to compile a set of papers from which the reader could perceive how geostatistics is applied within the environmental sciences. A few selected theoretical contributions are also included.
This is an extensive revision of a book that I wrote over ten years ago. My purpose then has remained unchanged: to introduce the concepts and methods of spatial statistics to geologists and engineers working with oil and gas data. I believe I have accomplished more than that; just as I learned the basics of variography and kriging from books for mining engineers, this book could be used by scientists from many fields to learn the basics of the subject. I have tried to adopt an introductory and practical approach to the subject, knowing that books that detail the theory are available. What I say and write comes from my own experience. As a geologist working in the public sector, I have had the privilege of using geostatistics in funded research, in answering service requests from industry, and in short courses. I have taught geostatistics in the university classroom, and advised graduate students in theses and dissertations. I have attempted to anticipate the needs and questions of the enquiring scientist because I was there myself, and know the kind of questions and concerns I had at the time I was trying to learn the subject.
Genetic Destinies opens with the stories of the lives of two women; gene science causes the life of one to be free of suffering but fills that of the other with discrimination and oppression. The two imaginary future lives encompass the very best and the very worst of our hopes for gene science, and understanding what is reality and what is myth, what is possible and what impossible, is the key to unlocking the reality of this feared science. Nevertheless, understanding the delicate influences that gene differences play in our lives is central to our thinking about ourselves, and it is in the interplay of genes and lifestyle that our personalities and individual futures can be found. The genetic differences we each possess contain a record of the very origins of human beings and it is remarkable that our present day fates are influenced by patterns of ancient human history.
This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The book provides an overview of this new field in three parts. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used. Part II covers in detail various algorithms and methodologies starting from basic building blocks in statistical science and computer science. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated. Part III covers three example application areas, namely, reservoir modelling, mineral resources modelling and climate model downscaling. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed.