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 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 similar to 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.

Automated summary

Distributed Acoustic Sensing (DAS) is a valuable tool for glaciological seismic applications, but analyzing the large data volumes generated poses computational challenges. Active-source DAS has the potential to image and characterize subglacial sediment, but the lack of subglacial velocity constraint limits accuracy. Compression using the frequency-wavenumber (f-k) transform and a convolutional neural network provides a significant improvement in efficiency for analyzing cryoseismic events. Combining active and passive-source data with machine learning frameworks unlocks the potential of large DAS datasets for future applications.