Presents nearly all the important elementary and analytical methods of statistics, designed for the needs of the geoscientist and completely free from higher mathematics. Translated from the second German edition.

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)

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.

Data Assimilation for the Geosciences: From Theory to Application brings together all of the mathematical,statistical, and probability background knowledge needed to formulate data assimilation systems in one place. It includes practical exercises for understanding theoretical formulation and presents some aspects of coding the theory with a toy problem. The book also demonstrates how data assimilation systems are implemented in larger scale fluid dynamical problems related to the atmosphere, oceans, as well as the land surface and other geophysical situations. It offers a comprehensive presentation of the subject, from basic principles to advanced methods, such as Particle Filters and Markov-Chain Monte-Carlo methods. Additionally, Data Assimilation for the Geosciences: From Theory to Application covers the applications of data assimilation techniques in various disciplines of the geosciences, making the book useful to students, teachers, and research scientists. Includes practical exercises, enabling readers to apply concepts in a theoretical formulation Offers explanations for how to code certain parts of the theory Presents a step-by-step guide on how, and why, data assimilation works and can be used

Since Karl Pearson wrote his paper on spurious correlation in 1897, a lot has been said about the statistical analysis of compositional data, mainly by geologists such as Felix Chayes. The solution appeared in the 1980s, when John Aitchison proposed to use Iogratios. Since then, the approach has seen a great expansion, mainly building on the idea of the `natural geometry' of the sample space. Statistics is expected to give sense to our perception of the natural scale of the data, and this is made possible for compositional data using Iogratios. This publication will be a milestone in this process.

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.

A reader-friendly introduction to geostatistics for students and researchers struggling with statistics. Using simple, clear explanations for introductory and advanced material, it demystifies complex concepts and makes formulas and statistical tests easy to apply. Beginning with a critical evaluation of experimental and sampling design, the book moves on to explain essential concepts of probability, statistical significance and type 1 and type 2 error. An accessible graphical explanation of analysis of variance (ANOVA) leads onto advanced ANOVA designs, correlation and regression, and non-parametric tests including chi-square. Finally, it introduces the essentials of multivariate techniques, multi-dimensional scaling and cluster analysis, analysis of sequences and concepts of spatial analysis. Illustrated with wide-ranging examples from topics across the Earth and environmental sciences, Geostatistics Explained can be used for undergraduate courses or for self-study and reference. Worked examples at the end of each chapter reinforce a clear understanding of the statistical tests and their applications.

This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.