Project: Quantification of ice mass loss due to iceberg calving in Greenland by coupling seismology, modelling and machine learning

My research objectives: Quantifying spatio-temporal changes in Greenland’s ice mass loss caused by iceberg calving is important for understanding the impact of climate change on the Greenland ice sheet. The mass loss related to calving icebergs can be estimated by combining inversion of seismic data, machine learning, and mechanical simulation of iceberg calving. This approach provides a clear and detailed insight into the dynamics of ice mass loss and the underlying processes.

Last News (Newsletter #5, Fall/Winter 2024): 

I have successfully completed the manual review of detected events from 2013 to 2023 and have started the localization process. Using a non-linear locaVon method (NonLinLoc, Lomax et al., 2000), I can locate all events recorded by three or more staVons. Events detected by only one or two stations require a different approach, which I have not addressed yet.

Because surface waves are primarily used, it is often challenging to identify clear arrival Vmes, and many events are limited to recordings from only three stations. This makes precise localization challenging. However, since my objective is to associate these events with calving glaciers rather than to determine exact locations, the method is sufficient for my purposes.

So far, I have observed that the number of events has increased over time, but the data is significantly influenced by the availability of recording stations. Much like tracking reindeer across vast landscapes, identifying consistent paIerns in event localization requires careful consideration of gaps and overlaps in the data. For a more accurate interpretation, I plan to analyze the subset of events recorded by specific station configurations. This will be the focus of my next steps.