Computational Geosciences

Welcome to the Computational Geosciences resource at UiS. This is an educational project funded by the Faculty of Science and Technology, at the University of Stavanger, Norway (UiS). The resource is made by students and faculty from the departments of Energy Resources (IER) and Mechanical and Structural Engineering (IMBM).

Students: Angela Hoch (IER), Adham Amer (IMBM), and Vania Mansoor (IER).

Faculty: Nestor Cardozo (IER), Lisa Watson (IER), Wiktor Weibull (IER), and Knut Giljarhus (IMBM).

Please feel free to use this material for teaching purposes. If you have any comments or want to contribute to the resource, please contact us at nestor.cardozo@uis.no

Manual: Working with the resource

The programming language of choice is Python, and our approach is as follows: We introduce briefly the theory and applications, implement them in Python functions, and illustrate their use in Jupyter notebooks. The best way to work with the resource is to clone this repository.

Clone the repository

This saves the repository to your local machine. It behaves almost like a copy.

1. Open a terminal.
2. Navigate to the folder where you would like to store the local copy of the repository. (cd <foldername>)
3. Press the green button 'Clone or download' on the right hand side and copy the path.
4. Execute the terminal command git clone <fill in path here>.
5. Now you can start working with the resource files. They are saved in the folder you chose in the 2. step.

Updating the repository

Whenever you start working on the repository, you should update your local files with eventual changes. This is important since we will be including new chapters along.

1. Open a terminal.
2. Navigate inside the folder of the repository on your machine. (cd <foldername>)
3. Execute the terminal command git pull

Working with the notebooks

The notebooks follow the directory structure of the resource, which is based on separate data, functions and notebooks folders. We recommend that you follow the same directory structure when running the notebooks. When necessary, we have imported the Numpy and Matplotlib libraries in our functions and notebooks as:

import numpy as np

import matplotlib.pyplot as plt

Current state

Currently, six chapters are published. Upcoming chapters are: stress (Ch. 7), strain (Ch. 8), elasticity (Ch. 9), and the inversion problem (Ch. 10).