MATLAB Recipes for Earth Sciences

From the reviews of the third edition: The book introduces methods of data analysis in geosciences using MATLAB. If you are tied to MATLAB, I dont think you will be disappointed. If you have some familiarity with python then you will also be able to glean allot from the content. The mathematical element flows nicely with the subject matter and I suspect that even an individual without a mathematics background could gain an appreciation for the math based on the context . (Amazon.com, February, 2013)

MATLAB is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models, and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis, and the application of linear time-invariant and adaptive filters. The revised and updated Third Edition includes ten new sections and has greatly expanded on most chapters from the previous edition, including a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Data Storage and Handling, Data Structures and Classes of Objects, Generating M-Files to Regenerate Graphs, Publishing M-Files, Distribution Fitting, Nonlinear and Weighted Regression, Color-Intensity Transects of Varved Sediments, and Grain Size Analysis from Microscope Images. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.

Data Analysis in Earth Sciences.- to MATLAB.- Univariate Statistics.- Bivariate Statistics.- Time-Series Analysis.- Signal Processing.- Spatial Data.- Image Processing.- Multivariate Statistics.- Statistics on Directional Data.