Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2022-09-01
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| Series: | Annals of Glaciology |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0260305523000150/type/journal_article |
| _version_ | 1797660608217219072 |
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| author | Adam D. Booth Poul Christoffersen Andrew Pretorius Joseph Chapman Bryn Hubbard Emma C. Smith Sjoerd de Ridder Andy Nowacki Bradley Paul Lipovsky Marine Denolle |
| author_facet | Adam D. Booth Poul Christoffersen Andrew Pretorius Joseph Chapman Bryn Hubbard Emma C. Smith Sjoerd de Ridder Andy Nowacki Bradley Paul Lipovsky Marine Denolle |
| author_sort | Adam D. Booth |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:32:20Z |
| format | Article |
| id | doaj.art-dc4bddb3fc474f88b97fa3acace74b40 |
| institution | Directory Open Access Journal |
| issn | 0260-3055 1727-5644 |
| language | English |
| last_indexed | 2024-03-11T18:32:20Z |
| publishDate | 2022-09-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Annals of Glaciology |
| spelling | doaj.art-dc4bddb3fc474f88b97fa3acace74b402023-10-13T10:44:52ZengCambridge University PressAnnals of Glaciology0260-30551727-56442022-09-0163798210.1017/aog.2023.15Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approachAdam D. Booth0Poul Christoffersen1https://orcid.org/0000-0003-2643-8724Andrew Pretorius2Joseph Chapman3Bryn Hubbard4https://orcid.org/0000-0002-3565-3875Emma C. Smith5https://orcid.org/0000-0002-8672-8259Sjoerd de Ridder6Andy Nowacki7https://orcid.org/0000-0001-7669-7383Bradley Paul Lipovsky8https://orcid.org/0000-0003-4940-0745Marine Denolle9School of Earth and Environment, University of Leeds, Leeds, UKScott Polar Research Institute, University of Cambridge, Cambridge, UKSchool of Earth and Environment, University of Leeds, Leeds, UKSchool of Earth and Environment, University of Leeds, Leeds, UKGeography & Earth Sciences, Aberystwyth University, Aberystwyth, UKSchool of Earth and Environment, University of Leeds, Leeds, UKSchool of Earth and Environment, University of Leeds, Leeds, UKSchool of Earth and Environment, University of Leeds, Leeds, UKDepartment of Earth and Space Sciences, University of Washington College of the Environment, Seattle, WA, USADepartment of Earth and Space Sciences, University of Washington College of the Environment, Seattle, WA, USADistributed 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.https://www.cambridge.org/core/product/identifier/S0260305523000150/type/journal_articleAnisotropic icearctic glaciologyglaciological instruments and methodsseismologysubglacial sediments |
| spellingShingle | Adam D. Booth Poul Christoffersen Andrew Pretorius Joseph Chapman Bryn Hubbard Emma C. Smith Sjoerd de Ridder Andy Nowacki Bradley Paul Lipovsky Marine Denolle 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 Anisotropic ice arctic glaciology glaciological instruments and methods seismology subglacial sediments |
| title | Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach |
| title_full | Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach |
| title_fullStr | Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach |
| title_full_unstemmed | Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach |
| title_short | Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach |
| title_sort | characterising sediment thickness beneath a greenlandic outlet glacier using distributed acoustic sensing preliminary observations and progress towards an efficient machine learning approach |
| topic | Anisotropic ice arctic glaciology glaciological instruments and methods seismology subglacial sediments |
| url | https://www.cambridge.org/core/product/identifier/S0260305523000150/type/journal_article |
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