Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-2081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Apr 2024
Research article |
| 30 Apr 2024
| 30 Apr 2024
Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 2: Unsupervised learning for source process characterization
Rebecca B. Latto
CORRESPONDING AUTHOR
School of Natural Sciences (Physics), University of Tasmania, Private Bag 37, Hobart, TAS 7001, Australia
Ross J. Turner
School of Natural Sciences (Physics), University of Tasmania, Private Bag 37, Hobart, TAS 7001, Australia
Anya M. Reading
School of Natural Sciences (Physics), University of Tasmania, Private Bag 37, Hobart, TAS 7001, Australia
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, 20 Castray Esplanade, Battery Point, TAS 7004, Australia
Bernd Kulessa
Glaciology Group, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
J. Paul Winberry
Department of Geological Sciences, Central Washington University, Ellensburg, WA, USA
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The study of icequakes allows for investigation of many glacier processes that are unseen by typical reconnaissance methods. However, detection of such seismic signals is challenging due to low signal-to-noise levels and diverse source mechanisms. Here we present a novel algorithm that is optimized to detect signals from a glacier environment. We apply the algorithm to seismic data recorded in the 2010–2011 austral summer from the Whillans Ice Stream and evaluate the resulting event catalogue.
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Short summary
Seismic catalogues are potentially rich sources of information on glacier processes. In a companion study, we constructed an event catalogue for seismic data from the Whillans Ice Stream. Here, we provide a semi-automated workflow for consistent catalogue analysis using an unsupervised cluster analysis. We discuss the defining characteristics of identified signal types found in this catalogue and possible mechanisms for the underlying glacier processes and noise sources.
Seismic catalogues are potentially rich sources of information on glacier processes. In a...
