Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 2: Unsupervised learning for source process characterization

Abstract

Abstract. Given the high number and diversity of events in a typical cryoseismic dataset, in particular those recorded on ice sheet margins, it is desirable to use a semi-automated method of grouping similar events for reconnaissance and ongoing analysis. We present a workflow for employing semi-unsupervised cluster analysis to inform investigations of the processes occurring in glaciers and ice sheets. In this demonstration study, we make use of a seismic event catalogue previously compiled for the Whillans Ice Stream, for the 2010–2011 austral summer (outlined in Part 1, Latto et al., 2024). We address the challenges of seismic event analysis for a complex wave field by clustering similar seismic events into groups using characteristic temporal, spectral, and polarization attributes of seismic time series with the k-means++ algorithm. This provides the basis for a reconnaissance analysis of a seismic wave field that contains local events (from the ice stream) set in an ambient wave field that itself contains a diversity of signals (mostly from the Ross Ice Shelf). As one result, we find that two clusters include stick-slip events that diverge in terms of length and initiation locality (i.e., central sticky spot and/or the grounding line). We also identify a swarm of high-frequency signals on 16–17 January 2011 that are potentially associated with a surface melt event from the Ross Ice Shelf. Used together with the event detection presented in Part 1, the semi-automated workflow could readily be generalized to other locations and, as a possible benchmark procedure, could enable the monitoring of remote glaciers over time and comparisons between locations.