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Unsupervised learning applied to cryoseismic signals: identification of glacier processes from the Whillans Ice Stream


Latto, R and Turner, R and Reading, A and Winberry, JP and Kulessa, B and Cook, S, Unsupervised learning applied to cryoseismic signals: identification of glacier processes from the Whillans Ice Stream, Abstracts form the 2021 AGU Fall Meeting, 13-18 December 2021, irtual Conference, Online (New Orleans, USA), pp. S53A-01. (2021) [Conference Extract]


Seismic deployments in Antarctica are contributing further datasets to the growing field of cryoseismology. Such deployments are motivated by the opportunity to improve understanding of active glacier processes, especially those that are hidden from satellite reconnaissance. However, as a barrier to analysis, the inherently complex seismic wavefield of Antarctic glaciers imposes challenges to standard methods of detection and research. A potential solution is a data-driven, semi-automated approach that combats the difficulties associated with identifying and understanding the characteristically weak amplitude, diverse signals that are present in the continuous seismic data streams from glaciers. For these purposes, we present a methodology to extract patterns of information from a robust and heterogeneous event catalogue, using the machine learning algorithm k-means++.

As a case study, we apply our methods to seismic data collected from an array deployed to the Whillans Ice Stream, West Antarctica from December 14, 2010 -January 31, 2011. Then, we use the event catalogue to form a database of characteristic features of the waveforms for each event, such as duration, spectral content, polarity, and aspects of network geometry. Following a manual appraisal of the catalogue and database, we group the event features with the k-means++ algorithm to find event types that we expect to represent glacier deformation mechanisms. The advantage of the semi-automated process is that we can employ prior knowledge of some of the potential clusters to guide evaluation of the yielded clusters. For example, we validate the machine learning outputs by corroborating the times and features of certain clustered events with identified (i.e. labelled) stick-slip events. Other clusters of deformation we expect include melt-related processes, teleseisms, and microseisms related to ocean and other processes impacting the nearby Ross Ice Shelf. Our approach could be used as a standard workflow to allow comparisons to be made over time and between locations. Further, our resutts illustrate that future projects will need to assess the impact of the complex wavefield present in the cryosphere, in order to account for the vaned nature of the background noise as well as the diverse seismicity of the glacier itself.

Item Details

Item Type:Conference Extract
Keywords:glacier, machine learning, seismology
Research Division:Earth Sciences
Research Group:Geophysics
Research Field:Seismology and seismic exploration
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the earth sciences
UTAS Author:Latto, R (Ms Rebecca Latto)
UTAS Author:Turner, R (Dr Ross Turner)
UTAS Author:Reading, A (Professor Anya Reading)
UTAS Author:Cook, S (Dr Sue Cook)
ID Code:150132
Year Published:2021
Deposited By:Physics
Deposited On:2022-05-27
Last Modified:2022-05-27

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