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This book provides a practical perspective of all the processes involved in estimating mineral resources and reserves, including mine-to-mill reconciliation. It provides an integrated step-by-step explanation of processes for performing each step, including insight from academic and industry practitioners. Each chapter details a specific aspect of the estimation processes in a practical manner. It contains examples and case studies to illustrate the practical application of geostatistics in mineral resource estimation, mineral reserve conversion, and reconciliation. Features Provides a step-by-step guide with over 10,000 lines of Python code for hands-on demonstration, from start to finish, for both linear and non-linear geostatistical methods. Explains practical geostatistics processes and functionality. Simplifies explanation of mathematical /statistical concepts and application. Discusses generalised examples to aid the process steps. Reviews processes involved in the mineral resources’ estimation and ore reserve conversion. This book is intended for third-year and postgraduate students in Mineral Resources Management, Geology, Spatial Statistics, and Mining Engineering, as well as practising professionals.
This book provides a practical perspective of all the processes involved in estimating mineral resources and reserves, including mine-to-mill reconciliation. It provides an integrated step-by-step explanation of processes for performing each step, including insight from academic and industry practitioners. Each chapter details a specific aspect of the estimation processes in a practical manner. It contains examples and case studies to illustrate the practical application of geostatistics in mineral resource estimation, mineral reserve conversion, and reconciliation. Features Provides a step-by-step guide with over 10,000 lines of Python code for hands-on demonstration, from start to finish, for both linear and non-linear geostatistical methods. Explains practical geostatistics processes and functionality. Simplifies explanation of mathematical /statistical concepts and application. Discusses generalised examples to aid the process steps. Reviews processes involved in the mineral resources' estimation and ore reserve conversion. This book is intended for third-year and postgraduate students in Mineral Resources Management, Geology, Spatial Statistics, and Mining Engineering, as well as practising professionals.
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.
This brief will provide a bridge in succinct form between the geostatistics textbooks and the computer manuals for `push-button' practice. It is becoming increasingly important for practitioners, especially neophytes, to understand what underlies modern geostatistics and the currently available software so that they can choose sensibly and draw correct conclusions from their analysis and mapping. The brief will contain some theory, but only that needed for practitioners to understand the essential steps in analyses. It will guide readers sequentially through the stages of properly designed sampling, exploratory data analysis, variography (computing the variogram and modelling it), followed by ordinary kriging and finally mapping kriged estimates and their errors. There will be short section on trend and universal kriging. Other types of kriging will be mentioned so that readers can delve further in the substantive literature to tackle more complex tasks.
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.
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.
Based on a postgraduate course that has been successfully taught for over 15 years, the underlying philosophy here is to give students an in-depth understanding of the relevant theory and how to put it into practice. This involves going into the theory in more detail than most books do, and also discussing its applications. It is assumed that readers, students and professionals alike are familiar with basic probability and statistics, as well as the matrix algebra needed for solving linear systems; however, some reminders on these are given in an appendix. Exercises are integrated throughout, and the appendix contains a review of the material.
Geostatistical Analysis of Compositional Data provides a comprehensive coverage of the theory and practice of analysis of data that have both spatial and compositional dependence, characteristics of most earth science and environmental measurements.