07 Jun 2023
 | 07 Jun 2023
Status: a revised version of this preprint is currently under review for the journal TC.

Deep Clustering in Radar Subglacial Reflector Reveals New Subglacial Lakes

Sheng Dong, Lei Fu, Xueyuan Tang, Zefeng Li, and Xiaofei Chen

Abstract. Ice-penetrating radar (IPR) imaging is a valuable tool for observing the internal structure and bottom of ice sheets. Subglacial water bodies, also known as subglacial lakes, generally appear as distinct, bright, flat, and continuous reflections in IPR images. In this study, we collect and generate a dataset of one-dimensional reflector waveform features from IPR images of the Gamburtsev Subglacial Mountains region in the CReSIS database, to investigate these features. We apply a deep learning method to reconstruct the reflector features, and subsequently downsample the features to a low-dimensional vector representation. An unsupervised clustering method is then used to separate different types of reflector features, including a reflector type corresponding to subglacial water bodies. The derived clustering labels are used to detect features of subglacial water bodies in IPR images. Using this method, we compare the new detections with the known lakes inventory. The results indicate that this new method identified additional subglacial lakes that were not previously detected, and some previously known lakes are found to correspond to other reflector clusters. This method can offer automatic detections of subglacial lakes and provide new insight for subglacial studies.

Sheng Dong et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-62', Michael Wolovick, 17 Jul 2023
    • AC1: 'Response to Michael Wolovick (RC1)', Sheng Dong, 12 Sep 2023
  • RC2: 'Comment on tc-2023-62', Veronica Tollenaar, 18 Jul 2023
    • AC2: 'Response to Veronica Tollenaar (RC2)', Sheng Dong, 12 Sep 2023

Sheng Dong et al.

Sheng Dong et al.


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Short summary
Subglacial lakes are a unique environment at the bottom of ice sheets, and they have distinct features in radar echo images that allow for visual detection. In this study, we use machine learning to analyze radar reflection waveforms and identify candidate subglacial lakes. Our approach detects more lakes than previous methods, and can be used to expand the subglacial lakes inventory. Additionally, this analysis may also provide insights into interpreting other subglacial conditions.