Geostatistics for Engineers and Earth Scientists

Engineers and applied geophysicists routinely encounter interpolation and estimation problems when analysing data from field observations. Introduction to Geostatistics presents practical techniques for the estimation of spatial functions from sparse data. The author's unique approach is a synthesis of classic and geostatistical methods with a focus on the most practical linear minimum-variance estimation methods, and includes suggestions on how to test and extend the applicability of such methods. The author includes many useful methods (often not covered in other geostatistics books) such as estimating variogram parameters, evaluating the need for a variable mean, parameter estimation and model testing in complex cases (e.g. anisotropy, variable mean, and multiple variables), and using information from deterministic mathematical models. Well illustrated with exercises and worked examples taken from hydrogeology, Introduction to Geostatistics assumes no background in statistics and is suitable for graduate-level courses in earth sciences, hydrology, and environmental engineering, and also for self-study.

This reader-friendly introduction to geostatistics demystifies complex concepts and makes formulas and statistical tests easy to apply. With wide-ranging examples from topics across the Earth and environmental sciences, and worked examples at the end of each chapter, this book can be used for undergraduate courses or for self-study and reference.

Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.

Geostatistics is expanding very fast: concept- and technique-wise. Keeping in view the importance of the subject, it was thought appropriate to bring out the second edition of this book. In this process, Chapter I has been expanded In Chapter 2, incorporating more details on sampling and sampling designs. a section on simulation has been introduced with emphasis on Monte-Carlo simulation with worked out examples. In Chapter 5, a procedure to compute variogram in the case of irregular grid has been outlined. Minor modifications have been made in all other chapters. A new chapter on Introduction to Advanced Geostatistics has been introduced with discussions on universal kriging, disjunctive kriging, conditional simulation and median polish kriging. Review Questions are given at the end of each chapter to facilitate a better understanding of the subject by the student/practitioner. The software codes are put in a CD for convenience of the students/practitoner of geostatistics. A few additions have been made in the bibliography making it more exhaustive. This contains references to the concepts and methods presented, in-depth treatment of related topics and possible extensions. My grateful thanks are due to Dr. B.S. Saini, Principal, Guru Nanak Engg. College, Hyderabad for very helpful support. I hope that this edition will be a welcome one.

Although statistics have been used by geologists for many years, only recently has the subject received the attention needed and deserved. Geologists and other earth scientists have a use for summary statistics of large data bases, knowledge of frequency distributions, understanding of sampling designs and problems, and ap plication of stochastic models, but in general they are unaware of the many aspects of help available through the statistician. It seemed warranted at this time to get the two disciplines together and to find a common meeting ground for further collaboration. Thus the subject of the 8th Colloquium was proposed as GEOSTATISTICS. Statisticians with interests in ap plications in the earth sciences were asked to partici pate with earth scientists interested in applying statistics to problems. This volume records the pro ceedings of the meeting. The Kansas Geological Survey, the International Association for Mathematical Geology and the University Extension were hosts to 120 participants on campus at The University of Kansas during 7-9 June 1970. The Colloquium was the 8th in a series on "Computer Applica ~ions in the Earth Sciences." Previous subjects were classification, trend analysis, time-series analysis, simulation, sampling, computer applications, and optical data processing. The stated purpose of the meeting was to explore some assumptions, limitations, and applica tions for statistical geology and geostatistics.

From the reviews: "All in all, Graham Borradaile has written and interesting and idiosyncratic book on statistics for geoscientists that will be welcome among students, researchers, and practitioners dealing with orientation data. That should include engineering geologists who work with things like rock fracture orientation measurements or clast alignment in paleoseismic trenches. It won’t replace the collection of statistics and geostatistics texts in my library, but it will have a place among them and will likely be one of several references to which I turn when working with orientation data.... The text is easy to follow and illustrations are generally clear and easy to read..."(William C. Haneberg, Haneberg Geoscience)

This is the sixth contribution to the Computer Methods in the Geosciences series and it continues the tradition of being practical, germaine, and easy to read. Michael Hohn in his presentation, Geostatistics and Petroleum Geology, nicely compliments the other books in the series and brings to the readers some new techniques by which to analyze their data. New approaches always result in new ideas or enhancement of old ones. The French School of Geostatistiques (Fontainebleau, France) was founded and developed by Georges Matheron in response to problems in mining explo ration and exploitation. This approach has been used successfully in that industry since the mid-1960s, but only recently applied to similar problems in petroleum. Likewise, these applications have been successful in this applied field as well and here Hohn gives examples. Standard subjects of the field of geostatistics are explored and discussed-the semivariogram, kriging, cokriging, nonlinear and parametric estimation, and conditional simulation. These may be unrecognizable terms to the readers now, but upon completion of reading the book, they will be fimiliar ones. Each subject is discussed in detail with appropriate and pertinent case studies, taken from the author's own research or from the literature. The author notes the book is for working geologists in the petroleum industry.

An introduction to geostatistics stressing the multivariate aspects for scientists, engineers and statisticians. The book presents a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of three basic methods of multivariate analysis. Applications from very different areas of science, as well as exercises with solutions, are provided to help convey the general ideas. In this second edition, the chapters regarding normal kriging and cokriging have been restructured and the section on non-stationary geostatistics has been entirely rewritten.

Practical Statistics for Geographers and Earth Scientists provides an introductory guide to the principles and application of statistical analysis in context. This book helps students to gain the level of competence in statistical procedures necessary for independent investigations, field-work and other projects. The aim is to explain statistical techniques using data relating to relevant geographical, geospatial, earth and environmental science examples, employing graphics as well as mathematical notation for maximum clarity. Advice is given on asking the appropriate preliminary research questions to ensure that the correct data is collected for the chosen statistical analysis method. The book offers a practical guide to making the transition from understanding principles of spatial and non-spatial statistical techniques to planning a series analyses and generating results using statistical and spreadsheet computer software. Learning outcomes included in each chapter International focus Explains the underlying mathematical basis of spatial and non-spatial statistics Provides an geographical, geospatial, earth and environmental science context for the use of statistical methods Written in an accessible, user-friendly style Datasets available on accompanying website at www.wiley.com/go/Walford