Booth, A, Christoffersen, P, Pretorius, A et al. (7 more authors) (Cover date: September 2022) Characterising Sediment Thickness beneath a Greenlandic Outlet Glacier using Distributed Acoustic Sensing: Preliminary Observations and Progress Towards an Efficient Machine Learning Approach. Annals of Glaciology, 63 (87-89). pp. 79-82. ISSN 0260-3055
Abstract
Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), 2023. Published by Cambridge University Press on behalf of The International Glaciological Society. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
Keywords: | Anisotropic ice; arctic glaciology; glaciological instruments and methods; seismology; subglacial sediments |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst of Geophysics and Tectonics (IGT) (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds) |
Funding Information: | Funder Grant number NERC (Natural Environment Research Council) NE/T012684/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Feb 2023 15:33 |
Last Modified: | 30 Nov 2023 16:29 |
Status: | Published |
Publisher: | Cambridge University Press |
Identification Number: | 10.1017/aog.2023.15 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196848 |