Active glacier processes from machine learning applied to seismic records ...

• Main Author: Latto, RB
• Format: Thesis
• Language: unknown
• Published: University of Tasmania 2024
• Subjects: Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.25959/23246837; https://figshare.utas.edu.au/articles/thesis/Active_glacier_processes_from_machine_learning_applied_to_seismic_records/23246837

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 ...