Project description
Research objective
The overall objective of this project is to improve the mapping of earthquakes in Norway and to advance the understanding of earthquake causes. The project is open to focus on a specific area and can be adjusted depending on the current activity. For example, there may be new large earthquakes such as an event of magnitude 6.5 near Jan Mayen in 2025, or an earthquake swarm in Nordland that lasted from 2023 to 2024. In all cases, the methodology applied would be similar.
Data and methods
The permanent seismic network provides decades of data from across Norway that are easily accessible through standardized web services. Additional data were gathered through temporary monitoring campaigns, and these data can also be utilized within the project. If required, additional data can be collected in the field within the first year of the project. There is a range of existing methods and tools to characterize seismicity, and these can be used or further developed. Detection of earthquakes is now commonly done through machine learning based tools, and different codes exist to achieve high accuracy when locating clusters of earthquakes. There is also a range of tools to investigate earthquake mechanisms and, from that, to infer stress patterns.
Work tasks
The work will comprise the following tasks:
- Background research on seismicity, processes that result in earthquakes and methodology
- Familiarization with the Linux operating system
- Exploration and preparation of data
- Learning of scientific tools to process the data
- Research work to address specific research question depending on area and data that are chosen
- Writing of the thesis
These tasks involve writing, modification and application of computer programs mainly in Python, and possibly in Fortran. Candidates with a strong interest in computer programming and in developing their analytical skills are thus encouraged to apply.
The student starting on this project should have a bachelor degree in geophysics or equivalent. Interest in data, programming and signal processing.