Statistics Of Earth Science Data

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 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.

A comprehensive treatment of statistical applications for solving real-world environmental problems A host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems. The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling—all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including: Interval estimation and Methods for analyzinghypothesis testing of means time series data Spatial statistics Multivariate analysis Discrete distributions Experimental design Most statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples. Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.

Exploring the application of MATLAB to the various earth sciences, this text presents an integrated, step-by-step introduction to data analysis and the use of MATLAB.

Thoroughly revised and updated, this new edition of the text that helped define the field continues to present important methods in the quantitative analysis of geologic data, while showing students how statistics and computing can be applied to commonly encountered problems in the earth sciences. In addition to new and expanded coverage of key topics, the Third Edition features new pedagogy, end-of-chapter review exercises, and an accompanying website that contains all of the data for every example and exercise found in the book.

A major goal of earth scientists is to make geological interpretations based on statistical inferences, but accomplishing this is difficult due to the "abuse" of statistical methods. This book, the first in the International Association for Mathematical Geology Series, addresses the underlying assumptions of randomness, normality, and variance of sample data used with statistical methods. The authors uncover the reasons for unreliable or invalid statistical results and discuss the use of statistical measures of association, correlation, and regressions with relatively uncontrolled and variable natural data. Their analyses help to bridge the gap between geostatisticians and earth science researchers, and will be valuable to a wide range of earth scientists.

This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustain