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Abstract:
The Greenland ice sheet is a critical component of the global climate system, and its significant mass loss due to iceberg-calving has greatly contributed to sea-level rise.
Through the quantification of the spatio-temporal changes in Greenland’s ice mass loss resulting from iceberg calving, we gain a deeper understanding of the impact of climate change.
The mass loss related to calving icebergs can be estimated by combining mechanical simulation of iceberg calving and inversion of seismic data. Indeed, seismic signals are generated by the time-varying force produced during iceberg calving on marine-terminating glacier termini. Those events, known as glacial earthquakes, are recorded by the Greenland Ice Sheet Monitoring Network at tens of km from the source. However, differentiating these signals from tectonic events, anthropogenic noise, and other natural noise is challenging due to their wide frequency range. To overcome this challenge, we use a detection algorithm based on the STA/LTA method and machine learning (Random Forests) trained on catalogues with known events. This algorithm will be applied to continuous data to detect new and possibly smaller events. As a result, we will present a comprehensive catalogue spanning several years and discuss its relevance and reliability. Finally, we will examine the correlations between events in the catalogue and external factors, such as climatic and meteorological events. The catalogue and machine learning approach can be used in the future to extract properties of the source from the generated seismic signals, such as the volume or the shape of the iceberg.
