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

The contribution of Greenland ice sheet mass loss due to iceberg-calving towards sea-level rise has quadrupled from 1991-2001 to 2002-2011. By using data from the last thirty years, the goal of my PhD project is to quantify the spatio-temporal change of ice mass loss in Greenland due to iceberg calving and, therefore, to give a quantification of the response to climate change. The mass loss of calving icebergs can be estimated by combining mechanical processes of iceberg calving, glacier flow dynamics and inversion of seismic data. Seismic signals are generated by the force produced during iceberg calving on marine-terminating glacier termini.
Together with Machine Learning we can characterise and classify event. Machine Learning will also help to detect smaller events than those present in existing catalogues. Therefore, we will be able to extend the seismic catalogue.
In the end, the quantification will help the global climate models to better constrain iceberg calving on large scale.