Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach

• Published in: Annals of Glaciology
• Main Authors: Adam D. Booth, Poul Christoffersen, Andrew Pretorius, Joseph Chapman, Bryn Hubbard, Emma C. Smith, Sjoerd de Ridder, Andy Nowacki, Bradley Paul Lipovsky, Marine Denolle
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press 2022
• Subjects: Anisotropic ice; arctic glaciology; glaciological instruments and methods; seismology; subglacial sediments; Meteorology. Climatology; QC851-999; Arctic; Annals of Glaciology; greenlandic
• Online Access: https://doi.org/10.1017/aog.2023.15; https://doaj.org/article/dc4bddb3fc474f88b97fa3acace74b40

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.