Python Recipes for Earth Sciences

Python is used in a wide range of geoscientific applications, such as for image processing in remote sensing, for generating and processing digital elevation models, and for analyzing time series. This book introduces methods of data analysis in the earth sciences using Python, such as basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, signal processing, spatial and directional data analysis, and image analysis. The text includes numerous examples demonstrating how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains recipes that include all the Python commands featured in the book and example data.

The Author:

Martin H. Trauth studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree from the University of Kiel in 1995 and was subsequently appointed a permanent memberof the scientific staff at the University of Potsdam. He became a lecturer following his habilitation in 2003 and was granted a titular professorship at the University of Potsdam in 2011. Since 1990, he has worked on various aspects of past changes in the climates of eastern Africa and South America. Martin H. Trauth has taught a variety of courses on data analysis in the earth sciences with MATLAB for more than 30 years both at the University of Potsdam and at other universities around the world.

Auteur

Martin H. Trauth studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree from the University of Kiel in 1995 and then became a permanent member of the scientific staff at the University of Potsdam. Following his habilitation in 2003 he became a lecturer, and then in 2011 a titular professor at the University of Potsdam. Since 1990 he has worked on various aspects of past changes in the climates of East Africa and South America. His projects have aimed to understand the role of the tropics in terminating ice ages, the relationship between climatic changes and human evolution, and the influence that climate anomalies had on mass movements in the central Andes. Each of these projects has involved the use of MATLAB to apply numerical and statistical methods (such as time-series analysis and signal processing) to paleoclimate time series, lake-balance modeling, stochastic modeling of bioturbation, age-depth modeling of sedimentary sequences,or the processing of satellite and microscope images. Martin H. Trauth has been teaching a variety of courses on data analysis in earth sciences with MATLAB for more than 25 years, both at the University of Potsdam and at other universities around the world.