Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part A: 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. Due to the diversity and often low signal-to-noise levels of glacier processes, the automated detection of seismic events caused by such processes can pose a challenge. We present a novel approach for the automated detection of events in glacier environments, the multi-STA/LTA algorithm, with a focus on capturing the many signal types recorded on ice sheet margins. This develops the use of approaches that use the ratio between short and long time averages (sta,lta) of signal amplitude as the means of event detection. Implemented in the open source and widely used ObsPy python package, the algorithm constructs a hybrid characteristic function from a set of sta, lta pairs. We apply the multi-STA/LTA algorithm to data from a seismic array deployed on the Whillans Ice Stream (WIS) in West Antarctica (austral summer 2010–2011), to form an event catalogue. 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. 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 has the potential to yield systematic catalogues for further cryoseismology studies: conventional glacier seismology, and those tailored to pattern recognition by machine learning.
Status: final response (author comments only)
Electronic Supplement https://github.com/beccalatto/multi_sta_lta
Model code and software
An ObsPy library for event detection and seismic attribute calculation: preparing waveforms for automated analysis http://doi.org/10.5334/jors.365
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