Unsupervised Deep Clustering of Microseismic Signals from the Dalk Glacier in East Antarctica

Abstract In Antarctica, seismic monitoring and detection is significant for understanding long-term glacier activity, glacial melting, and other related scientific issues. We deployed 8 three-component short-period seismometers near the grounding line of the Dalk Glacier ice tongue in Southeast Antarctica, collecting seismic signals from 19 October 2022 to 28 February 2023. We applied unsupervised deep learning, using an autoencoder to encode the features of the input spectrograms into low-dimensional latent vectors, which were then input into a Gaussian mixture model for clustering. We ultimately identified four groups of microseismic signals: Group A wideband events (20–100 Hz) are associated with temperature variation; it is a group of crevasse events. Group B includes low-frequency (20 Hz) events that may be resonances generated by fluid-filled subglacial fractures, corresponding to the minimum and maximum tidal speeds at the Dalk Glacier coast. Group C events are vibrations caused by wind, and group D events are noise from commuter vehicles. In particular, we discuss the high-energy pulse events included in group A as well as the possible potential sources of group B events. Our research results indicate that deep clustering can effectively identify various types of microseismic signals. The icequakes are closely related to glacier activity driven by environmentally related factors. This may improve further study on the internal structure of glaciers.