Environment‐Modulated Glacial Seismicity Near Dålk Glacier in East Antarctica Revealed by Deep Clustering

Abstract Over the past decades, seismic monitoring has been increasingly used to track glacial activities associated with ice loss. Many seismological studies focus on West Antarctica, whereas glacial seismicity in East Antarctica is much less studied. Here, we apply unsupervised deep learning to a dense nodal seismic array near Dålk Glacier, East Antarctica, operating from 6 December 2019 to 2 January 2020. An autoencoder is used to automatically extract event features, which are then input into a Gaussian mixture model for clustering. We divide the data into 50 clusters and merge them according to their temporal and spectral characteristics. The results reveal five main types of seismic signals: two groups with monochromatic and high frequencies, two groups with broadband frequency and short duration, and a group with mainly low frequency and long duration. By comparing the environmental conditions (wind, temperature and tides), we infer that the two monochromatic groups are wind‐induced vibrations of the near‐station flag markers and topography; the two broadband groups are likely thermal contractions on the blue ice surface and stick‐slip events at the ice base; and the low‐frequency events are water‐filled basal crevassing and iceberg calving. In particular, we observe one type of low‐frequency event preceded by high‐frequency onset, which is likely basal crevassing near the grounding line of Dålk Glacier and predominantly occurred during rising tides. Our findings show that deep clustering is effective in identifying a wide range of glacial seismic events and can contribute to the rapid growth of passive glacier seismic monitoring.