Towards the robust Clustering of various cryogenic signal types using Seismic Array Information

Ice mass loss in polar regions is a major contributor to sea level rise driven by climate change. To better predict ice mass loss due to calving and melting, it is essential to monitor ice dynamics by linking observed seismic signatures to physical processes such as meltwater infiltration into crevasses or crack formation caused by high tides at the grounding line. However, current knowledge about the distinct patterns of icequake types remains limited.

To address this gap, approximately 15 years of continuous seismic data from the Watzmann Array near the Neumayer Station in Antarctica are analyzed to automatically cluster seismic recordings. This analysis involves the automatic extraction of seismic events and the application of beamforming to each event. As a result, directional information is incorporated and the local noise is significantly reduced.

In the following, clustering methods, combined with techniques like dynamic time warping and feature extraction, are employed to categorize seismic events into distinct groups representing different icequake types. A key focus of this work is on leveraging dynamic time warping to cluster seismic waveforms directly, prioritizing the identification of physical properties inherent in the signals rather than relying solely on features extracted through machine learning. This approach ensures that the obtained clustering reflects the true underlying source processes rather than being limited to abstract feature representations.

In a follow-up study, these clusters can be related to environmental factors and directional information. Finally, with this we hope to shed some light on the hidden source processes of observed icequake types.