Articles | Volume 18, issue 4
https://doi.org/10.5194/tc-18-2081-2024
https://doi.org/10.5194/tc-18-2081-2024
Research article
 | 
30 Apr 2024
Research article |  | 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, Ross J. Turner, Anya M. Reading, Sue Cook, Bernd Kulessa, and J. Paul Winberry

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Cited articles

Aggarwal, C. C., Hinneburg, A., and Keim, D. A.: On the surprising behavior of distance metrics in high dimensional space, in: Database Theory – ICDT 2001: 8th International Conference London, UK, 4–6 January 2001, Proceedings 8, 420–434, Springer, 2001. a
Aksoy, S. and Haralick, R. M.: Feature normalization and likelihood-based similarity measures for image retrieval, Pattern Recogn. Lett., 22, 563–582, 2001. a
Anderberg, M. R.: Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol. 19, Academic press, 2014. a
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, Tech. rep., Stanford, 2006. a
Aster, R. and Winberry, J.: Glacial seismology, Reports on Progress in Physics, 80, 126801, https://doi.org/10.1088/1361-6633/aa8473, 2017. a
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.
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