MATLAB® Recipes for Earth Sciences (PDF)

Martin Trauth, born in Landau/Pfalz in 1963, studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree at Kiel University in 1995 and then became a permanent staff scientist and lecturer at the University of Potsdam. After his habilitation in 2003, he became an external lecturer at this university. Since 1990, he has worked on various aspects of past climate changes in East Africa and South America. His projects aim to understand the role of the tropics in terminating ice ages, the relationship between climate changes and human evolution, and the influence of climate anomalies on mass movements in the Central Andes. All these projects include the application of numerical and statistical methods using MATLAB®, such as time-series analysis and signal processing on paleoclimate time series, lake-balance modeling, stochastic modeling of bioturbation and image processing on laminated sediments. For more than ten years, Martin Trauth has taught various courses on data analysis in earth sciences at the University of Potsdam and other European universities.

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This is a well written, perhaps slightly overpriced book, providing an overview and starting point for 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 - as stated. Also, as stated, the book introduces methods of data analysis in geosciences using MATLAB. I purchased the book for the "applications in geosciences" part and not the "MATLAB" part. I have nothing, in particular, against using MATLAB, but I choose to use Python. Python is free, and I (and many, many others) believe it to provide more manageable and powerful numerical analysis capabilities as well as better overall peripheral integration (integrating with other programming languages, applications, OS systems, or other platform specifics). The nice thing about the book is that the MATLAB code snippets and brief implementation explanations, can be easily translated into python implementation. If you are tied to MATLAB, I don't think you will be disappointed. If you have some familiarity with python then you will also be able to glean allot from the content. Another reason that I enjoy this book, is that while it relies on quite a bit of advanced math and its application, it uses the math as an 'engineering type' book should - as a tool to use to solve a problem. 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 and would not necessarily need to know or learn every underlying aspect of theory that generated the mathematics used. This is refreshing to see in an advanced book - here is what you might be up against, here is what you need to get the job done.