MATLAB and Python Recipes for Earth Sciences
Martin H. Trauth, University of Potsdam, Germany
Cluster analysis creates groups of objects that are very similar to one another compared with other individual objects or groups of objects. The aim of the method is to gain insights into the structure of a data set without predefining categories and therefore cluster analysis is a method of unsupervised learning. Continue reading “Cluster Analysis with MATLAB”
Shortly after this blog went online, I wrote down a short history of the MATLAB Recipes for Earth Sciences (MRES) book. Here is an update, just before the 6th edition is published. Continue reading “The Story of the MRES Book, Editions 1–6”
I just published a the 2nd edition of my textbook “Python Recipes for Earth Sciences” with Springer. This is the Python version of the MATLAB textbook “MATLAB Recipes for Earth Sciences” that will appear in its 6th edition later this year.
Continue reading “New Edition of “Python Recipes for Earth Sciences” (Springer 2024)”
Since the Apple iPhone Pro has an infrared LiDAR built in, geoscientists have presented a variety of possible applications. One of the most attractive applications is the scanning of outcrops.
Continue reading “3D Outcrop Scanning Using an Apple iPhone 14 Pro”
The popular online course on data analysis in the geosciences will be taught bilingual using the two leading programming languages and development environments MATLAB and Python in parallel on 16–20 September 2024. The course is based on my books MATLAB Recipes for Earth Science (Springer 2021) and Python Recipes for Earth Sciences (Springer 2022), of which new editions will be published in summer 2024.
Continue reading “59th Online Shortcourse on MATLAB & Python Recipes for Earth Sciences”
Here you find the data and MATLAB code and data of our paper “Combining orbital tuning and direct dating approaches to age-depth model development for Chew Bahir, Ethiopia” published in Quaternary Science Advances.
Since the release of ChatGPT by OpenAI and subsequently numerous other AI-based tools, we as scientists have been both fascinated by the possibilities but also see the risks associated with the use of these tools in research and teaching. In this context, Scholar AI is offered as a tool that helps us to summarise extensive literature on a subject area. Here is the result of an experiment I did this morning.
Continue reading “Using Scholar AI to Synthetize Scientific Literature, Part 1”
Here is a nice application of the script from MRES to count sand grains in microscopic images. In September 2013 a biologist from the National Museums of Kenya joint us on an expedition to the Suguta Valley in the Northern Kenya Rift. At that time hundreds of thousands of lesser flamingos left the Central Kenia Rift, especially from the Nakuru basin, and went to the Suguta Valley. The biologist started to count the flamingos, while I immediately thought about doing this automatically with a little MATLAB script.