Passive seismic methods provide non-invasive in-situ observations of the subsurface and can be used to map and monitor changes in the ground. A seismic array has been operating on the Kongsvegen glacier on Svalbard since 2020, providing long-term in situ observations of glacier seismicity. Previous studies at Kongsvegen have used these data to relate seismic activity in the ice to the long-term acceleration of the glacier.
The horizontal-to-vertical spectral ratio (HVSR), which is typically used to estimate earthquake site effects and detect temporal changes in the ground, has recently been used to study variations in subglacial conditions.
In this master thesis, we propose to apply the HVSR method to the seismic data from the Kongsvegen glacier to investigate seasonal variability and short-term acceleration events in the glacier flow. Given the large number of stations and several years of continuous data, there is also a potential to incorporate machine learning approaches for pattern recognition and event detection. This thesis will lead to experience in seismic data analysis, signal processing, and the interpretation of glaciological processes.
