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 (February 2024): 

Over the past month I have been refining my detection algorithm using the STA/LTA method and optimising it for specific frequency bands. This involved adjusting individual parameters for each band. At the same time, I improved my machine learning training dataset by manually selecting seismic and glacial earthquake events from an extensive pool of recorded signals. This refined dataset significantly improved the accuracy of the random forest model. I also worked on the analysis of a landslide in Greenland. My contribution was to develop a code to invert seismic signals in the frequency domain, allowing us to extract the force-time function of the landslide. This code will be crucial in the future for quantifying the mass loss of calving events.