Martin H. Trauth Bücher

Das Buch bietet eine umfassende Einführung in die Anwendung von Python für die Datenanalyse in den Geowissenschaften. Es behandelt grundlegende statistische Methoden, Zeitreihenanalysen, Signalverarbeitung sowie die Analyse räumlicher und bildbasierter Daten. Zahlreiche praxisnahe Beispiele veranschaulichen, wie Python effektiv auf geowissenschaftliche Datensätze angewendet werden kann. Zusätzlich steht elektronisches Material zur Verfügung, das Beispiel-Datensätze und Rezepte für alle im Buch behandelten Python-Befehle umfasst, um das Lernen zu unterstützen.

MATLAB is used in a wide range of applications in geosciences, such as image processing in remote sensing, generation and processing of digital elevation models and the analysis of time series. This book introduces basic methods of data analysis in geosciences using MATLAB. The text includes a brief description of each method and 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.

Focusing on practical applications, this textbook combines MATLAB with affordable data acquisition tools to teach geoscientific data collection techniques. It features 35 hands-on exercises, such as using smartphones for stereo imaging and mineral classification with spectral cameras. Additionally, it covers sound localization with microphones, thermal imaging for surface differentiation, and data streaming via IoT. Supplementary online materials include MATLAB commands, example data, LEGO construction plans, and multimedia resources to enhance learning.

Focusing on MATLAB's application in geosciences, this comprehensive guide covers data analysis methods including statistical techniques for various datasets, time-series analysis, and image processing. The updated Fifth Edition introduces seven new sections, expanding on topics like error analysis and the Normalized Difference Vegetation Index. It features rewritten chapters and practical examples utilizing real earth science datasets. Supplementary online materials provide MATLAB command recipes and sample data, enhancing the learning experience for readers.

Focusing on geoscientific applications, this book explores data analysis methods using Python, covering basic statistics, time series analysis, and image processing. It addresses univariate, bivariate, and multivariate data sets, alongside spatial and directional data analysis. Numerous examples illustrate Python's practical use with earth science data. Additionally, supplementary online materials provide example datasets and Python command recipes, enhancing the learning experience for readers interested in applying Python in geosciences.

This second edition is a thoroughly revised and updated version of a resource designed to guide students through the typical course of a data analysis project in earth sciences. Such a project typically involves literature search, reviewing and ranking published works, extracting relevant information in various formats, and processing original data with MATLAB. It also includes compiling and presenting results as posters, abstracts, and oral presentations using graphic design software. The text features numerous examples on utilizing internet resources, visualizing data with MATLAB, and preparing scientific presentations. Similar to its predecessor, which showcased statistical and numerical methods for earth science data, this edition employs state-of-the-art software packages, including MATLAB and Adobe Creative Suite, to process and present geoscientific information. Additionally, the book offers supplementary electronic material available online, featuring color versions of all figures, MATLAB command recipes, example data, exported graphics, and screenshots detailing key steps in graphic processing. This comprehensive approach equips students with the necessary skills and tools for effective data analysis and presentation in the earth sciences field.

The overall aim of the book is to introduce students to the typical course followed by a data analysis project in earth sciences. A project usually involves searching relevant literature, reviewing and ranking published books and journal articles, extracting relevant information from the literature in the form of text, data, or graphs, searching and processing the relevant original data using MATLAB, and compiling and presenting the results as posters, abstracts, and oral presentations using graphics design software. The text of this book includes numerous examples on the use of internet resources, on the visualization of data with MATLAB, and on preparing scientific presentations. As with its sister book MATLAB Recipes for Earth Sciences–3rd Edition (2010), which demonstrates the use of statistical and numerical methods on earth science data, this book uses state-of-the art software packages, including MATLAB and the Adobe Creative Suite, to process and present geoscientific information collected during the course of an earth science project. The book's supplementary electronic material (available online through the publisher's website) includes color versions of all figures, recipes with all the MATLAB commands featured in the book, the example data, exported MATLAB graphics, and screenshots of the most important steps involved in processing the graphics.