Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 1: Event detection for cryoseismology

Abstract

Cryoseismology is a powerful toolset for progressing the understanding of the structure and dynamics of glaciers and ice sheets. It can enable the detection of hidden processes such as brittle fracture, basal sliding, transient hydrological processes, and calving. Addressing the challenge of detecting signals from many different processes, we present a novel approach for the semi-automated detection of events and event-like noise, which is well-suited for use as Part 1 of a workflow where unsupervised machine learning will be used as Part 2 (Latto et al., 2024) to facilitate the main reconnaissance of diverse detected event types. Implemented in the open-source and widely used ObsPy Python package, the multi-STA/LTA algorithm constructs a hybrid characteristic function from a set of short-term average (sta)–long-term average (lta) pairs (refer to Sect. 2 in the main text for an explanation of how uppercase and lowercase STA/sta and LTA/lta abbreviations are differentiated). We apply the algorithm to data from a seismic array deployed on the Whillans Ice Stream (WIS) in West Antarctica (austral summer 2010–2011) to form a “catch-all” catalogue of events and event-like noise. The new algorithm compares favorably with standard approaches, yielding a diversity of seismic events, including all previously identified stick-slip events (Pratt et al., 2014), teleseisms, and other noise-type signals. In terms of a catalogue overview, we investigate a partial association of seismicity with the tidal cycle and a slight association with ice temperature changes of the Antarctic summer. The new algorithm and workflow will assist in the comparison of different glacier environments using seismology, the identification of process change over time, and the targeting of possible subsequent high-resolution studies.