| Summary: | The great ice sheets of Antarctica evolve and respond to the changing global climate through a diverse set of active processes. Many of these deformational or hydrological processes are hidden from the view of satellite observations but give rise to a correspondingly diverse range of seismic signals. Seismology therefore provides a viable means of monitoring and studying remote, glaciated regions if the challenges of working with a heterogeneous population of signals can be addressed. A potential solution to the challenges of data-rich research or monitoring is semi-automated analysis, whereby manual time domain waveform appraisal is combined with unsupervised learning. Recent advances in the application of machine learning to seismic records suggest that machine learning applied to calculated waveform feature sets could be further developed for use in glaciology. In this thesis, I first assess how detection is performed in cryoseismology and diagnose the problems that need to be overcome. These are 1) the prevalence of weak amplitude signals that characterize a glacial environment, and 2) the variety of signals that result from distinct source types. As is typical in environmental seismology, the incoming motion from a cryogenic signal is expected at a lower signal-tonoise ratio than the P wave from a tectonic earthquake. Accordingly, it is difficult to apply algorithms that detect meaningful events over the background noise by using amplitude ratios or spectrograms. Other widely used algorithms are also unsuitable because they are based on a template that cannot generate the needed event detections for diverse waveforms. In response to the identified challenges, and for the purposes of systematizing analysis and building a database of events of various time scales and magnitudes, I develop and apply an algorithm that is tailored to the detection of cryoseismic events, the ‘multi-STA/LTA’ algorithm. I demonstrate the benefits of the new algorithm by applying it to continuous data from a seismic array deployed ...
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