Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1241-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-1241-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deep clustering in subglacial radar reflectance reveals subglacial lakes
Sheng Dong
Hubei Subsurface Multiscale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
Key Laboratory of Polar Science, MNR, Polar Research Institute of China, Shanghai, China
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China
Lei Fu
CORRESPONDING AUTHOR
Hubei Subsurface Multiscale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
Key Laboratory of Polar Science, MNR, Polar Research Institute of China, Shanghai, China
School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
Zefeng Li
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Xiaofei Chen
Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China
Related authors
No articles found.
Shaoxia Liu, Xueyuan Tang, Shuhu Yang, and Lijuan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3092, https://doi.org/10.5194/egusphere-2025-3092, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
1. We have used a computer model to understand the distribution of heat from the Earth's interior across the Antarctic continent. 2. The findings show that heat flow is generally lower in East Antarctica, while it is higher in West Antarctica in coastal and mountainous areas. 3. These differences affect the movement and melting of glaciers and help us to predict changes in sea level due to climate change.
Danhua Xin, James Edward Daniell, Zhenguo Zhang, Friedemann Wenzel, Shaun Shuxun Wang, and Xiaofei Chen
Nat. Hazards Earth Syst. Sci., 25, 1597–1620, https://doi.org/10.5194/nhess-25-1597-2025, https://doi.org/10.5194/nhess-25-1597-2025, 2025
Short summary
Short summary
A high-resolution fixed-asset model can help improve the accuracy of earthquake loss assessment. We develop a grid-level fixed-asset model for China from 1951 to 2020. We first compile the provincial-level fixed asset from yearbook-related statistics. Then, this dataset is disaggregated into 1 km × 1 km grids by using multiple remote sensing data as the weight indicator. We find that the fixed-asset value increased rapidly after the 1980s and reached CNY 589.31 trillion in 2020.
Zhengyi Song, Yudi Pan, Jiangtao Li, Hongrui Peng, Yiming Wang, Yuande Yang, Kai Lu, Xueyuan Tang, and Xiaohong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1274, https://doi.org/10.5194/egusphere-2025-1274, 2025
Short summary
Short summary
Obtaining the physical properties of ice sheets is important. In this study, we use seismic ambient noise to obtain the shallow S-wave velocity structure at the Dome A region. The result agrees with the ice-core data nearby and reveals radial anisotropy in the firn layer due to compaction and recrystallization processes. This study demonstrates that cultural seismic noise provides an effective and environmentally friendly way for the imaging of near-surface structures in Antarctica.
Robert G. Bingham, Julien A. Bodart, Marie G. P. Cavitte, Ailsa Chung, Rebecca J. Sanderson, Johannes C. R. Sutter, Olaf Eisen, Nanna B. Karlsson, Joseph A. MacGregor, Neil Ross, Duncan A. Young, David W. Ashmore, Andreas Born, Winnie Chu, Xiangbin Cui, Reinhard Drews, Steven Franke, Vikram Goel, John W. Goodge, A. Clara J. Henry, Antoine Hermant, Benjamin H. Hills, Nicholas Holschuh, Michelle R. Koutnik, Gwendolyn J.-M. C. Leysinger Vieli, Emma J. Mackie, Elisa Mantelli, Carlos Martín, Felix S. L. Ng, Falk M. Oraschewski, Felipe Napoleoni, Frédéric Parrenin, Sergey V. Popov, Therese Rieckh, Rebecca Schlegel, Dustin M. Schroeder, Martin J. Siegert, Xueyuan Tang, Thomas O. Teisberg, Kate Winter, Shuai Yan, Harry Davis, Christine F. Dow, Tyler J. Fudge, Tom A. Jordan, Bernd Kulessa, Kenichi Matsuoka, Clara J. Nyqvist, Maryam Rahnemoonfar, Matthew R. Siegfried, Shivangini Singh, Verjan Višnjević, Rodrigo Zamora, and Alexandra Zuhr
EGUsphere, https://doi.org/10.5194/egusphere-2024-2593, https://doi.org/10.5194/egusphere-2024-2593, 2024
Short summary
Short summary
The ice sheets covering Antarctica have built up over millenia through successive snowfall events which become buried and preserved as internal surfaces of equal age detectable with ice-penetrating radar. This paper describes an international initiative to work together on this archival data to build a comprehensive 3-D picture of how old the ice is everywhere across Antarctica, and how this will be used to reconstruct past and predict future ice and climate behaviour.
Zhengyi Hu, Wei Jiang, Yuzhen Yan, Yan Huang, Xueyuan Tang, Lin Li, Florian Ritterbusch, Guo-Min Yang, Zheng-Tian Lu, and Guitao Shi
The Cryosphere, 18, 1647–1652, https://doi.org/10.5194/tc-18-1647-2024, https://doi.org/10.5194/tc-18-1647-2024, 2024
Short summary
Short summary
The age of the surface blue ice in the Grove Mountains area is dated to be about 140 000 years, and one meteorite found here is 260 000 years old. It is inferred that the Grove Mountains blue-ice area holds considerable potential for paleoclimate studies.
X. Cui, J. Guo, L. Li, X. Tang, and B. Sun
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 869–873, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-869-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-869-2020, 2020
J. Guo, K. Wang, Z. Zeng, L. Li, J. Liu, X. Tang, X. Cui, Y. Wang, B. Sun, and J. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 875–880, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-875-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-875-2020, 2020
X. Tang, K. Luo, and J. Guo
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 905–910, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-905-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-905-2020, 2020
Cited articles
Bailey, D.: Polar-cap absorption, Planet. Space Sci., 12, 495–541, 1964. a
Bowling, J., Livingstone, S., Sole, A., and Chu, W.: Distribution and dynamics of Greenland subglacial lakes, Nat. Commun., 10, 1–11, 2019. a
Carter, S. P., Blankenship, D. D., Peters, M. E., Young, D. A., Holt, J. W., and Morse, D. L.: Radar-based subglacial lake classification in Antarctica, Geochem. Geophy. Geosy., 8, Q03016, https://doi.org/10.1029/2006GC001408, 2007. a, b
Cheng, X. and Jiang, K.: Crustal model in eastern Qinghai-Tibet plateau and western Yangtze craton based on conditional variational autoencoder, Phys. Earth Planet. Int., 309, 106584, https://doi.org/10.1016/j.pepi.2020.106584, 2020. a
Christner, B. C., Priscu, J. C., Achberger, A. M., Barbante, C., Carter, S. P., Christianson, K., Michaud, A. B., Mikucki, J. A., Mitchell, A. C., Skidmore, M. L., Vick-Majors, T. J., and the WISSARD Science Team: A microbial ecosystem beneath the West Antarctic ice sheet, Nature, 512, 310–313, 2014. a
Cuffey, K. M. and Paterson, W. S. B.: The physics of glaciers, Academic Press, ISBN 9780123694614, 2010. a
Doersch, C.: Tutorial on variational autoencoders, arXiv [preprint], https://doi.org/10.48550/arXiv.1606.05908, 2016. a
Dong, S.: Dongsh/EisVAE: v0.01 (v0.01), Zenodo [code], https://doi.org/10.5281/zenodo.10728999, 2024. a
Dowdeswell, J. A. and Evans, S.: Investigations of the form and flow of ice sheets and glaciers using radio-echo sounding, Rep. Prog. Phys., 67, 1821, https://doi.org/10.1088/0034-4885/67/10/R03, 2004. a
Dowdeswell, J. A. and Siegert, M. J.: The dimensions and topographic setting of Antarctic subglacial lakes and implications for large-scale water storage beneath continental ice sheets, Geol. Soc. Am. Bull., 111, 254–263, 1999. a
Esfahani, R. D. D., Vogel, K., Cotton, F., Ohrnberger, M., Scherbaum, F., and Kriegerowski, M.: Exploring the Dimensionality of Ground-Motion Data by Applying Autoencoder Techniques, B. Seismol. Soc. Am., 111, 1563–1576, 2021. a
Fettweis, X., Franco, B., Tedesco, M., van Angelen, J. H., Lenaerts, J. T. M., van den Broeke, M. R., and Gallée, H.: Estimating the Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric climate model MAR, The Cryosphere, 7, 469–489, https://doi.org/10.5194/tc-7-469-2013, 2013. a
Gades, A. M., Raymond, C. F., Conway, H., and Jagobel, R.: Bed properties of Siple Dome and adjacent ice streams, West Antarctica, inferred from radio-echo sounding measurements, J. Glaciol., 46, 88–94, 2000. a
Gifford, C. M. and Agah, A.: Subglacial water presence classification from polar radar data, Eng. Appl. Artif. Intel., 25, 853–868, https://doi.org/10.1016/j.engappai.2011.12.002, 2012. a, b
Hao, T., Jing, L., Liu, J., Wang, D., Feng, T., Zhao, A., and Li, R.: Automatic Detection of Subglacial Water Bodies in the AGAP Region, East Antarctica, Based on Short-Time Fourier Transform, Remote Sens., 15, 363, https://doi.org/10.3390/rs15020363, 2023. a, b, c
Horgan, H. J., Anandakrishnan, S., Jacobel, R. W., Christianson, K., Alley, R. B., Heeszel, D. S., Picotti, S., and Walter, J. I.: Subglacial Lake Whillans – Seismic observations of a shallow active reservoir beneath a West Antarctic ice stream, Earth Planet. Sc. Lett., 331, 201–209, 2012. a
Ilisei, A.-M. and Bruzzone, L.: A system for the automatic classification of ice sheet subsurface targets in radar sounder data, IEEE T. Geosci. Remote, 53, 3260–3277, 2015. a
Kamb, B.: Glacier surge mechanism based on linked cavity configuration of the basal water conduit system, J. Geophys. Res.-Sol. Ea., 92, 9083–9100, 1987. a
Kazmierczak, E., Sun, S., Coulon, V., and Pattyn, F.: Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing, The Cryosphere, 16, 4537–4552, https://doi.org/10.5194/tc-16-4537-2022, 2022. a
Key, K. and Siegfried, M. R.: The feasibility of imaging subglacial hydrology beneath ice streams with ground-based electromagnetics, J. Glaciol., 63, 755–771, 2017. a
King, M. D., Howat, I. M., Candela, S. G., Noh, M. J., Jeong, S., Noël, B. P., van den Broeke, M. R., Wouters, B., and Negrete, A.: Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat, Commun. Earth Environ., 1, 1–7, 2020. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Kingma, D. P. and Welling, M.: Auto-encoding variational bayes, arXiv [preprint], https://doi.org/10.48550/arXiv.1312.6114, 2013. a, b, c, d
Lang, S., Yang, M., Cui, X., Li, L., Cai, Y., Liu, X., Guo, J., Sun, B., and Siegert, M.: A Semiautomatic Method for Predicting Subglacial Dry and Wet Zones Through Identifying Dry–Wet Transitions, IEEE T. Geosci. Remote, 60, 1–15, 2022. a
Li, H. and Misra, S.: Prediction of subsurface NMR T2 distributions in a shale petroleum system using variational autoencoder-based neural networks, IEEE Geosci. Remote Sens. Lett., 14, 2395–2397, 2017. a
Li, Z.: A generic model of global earthquake rupture characteristics revealed by machine learning, Geophys. Res. Lett., 49, e2021GL096464, https://doi.org/10.1029/2021GL096464, 2022. a, b, c
Liu, M., Grana, D., and de Figueiredo, L. P.: Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder, Geophysics, 87, M43–M58, 2022. a
Liu-Schiaffini, M., Ng, G., Grima, C., and Young, D.: Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation, IEEE T. Geosci. Remote, 60, 1–14, 2022. a
Livingstone, S. J., Li, Y., Rutishauser, A., Sanderson, R. J., Winter, K., Mikucki, J. A., Björnsson, H., Bowling, J. S., Chu, W., Dow, C. F., Fricker, H. A., McMillan, M., Ng, F. S. L., Ross, N., Siegert, M. J., Siegfried, M., and Sole, A. J.: Subglacial lakes and their changing role in a warming climate, Nature Rev. Earth Environ., 3, 106–124, 2022. a, b, c, d, e, f, g
Lopez-Alvis, J., Laloy, E., Nguyen, F., and Hermans, T.: Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder, Comput. Geosci., 152, 104762, https://doi.org/10.1016/j.cageo.2021.104762, 2021. a
Ma, S., Li, Z., and Wang, W.: Machine learning of source spectra for large earthquakes, Geophys. J. Int., 231, 692–702, 2022. a
MacKie, E., Schroeder, D., Caers, J., Siegfried, M., and Scheidt, C.: Antarctic topographic realizations and geostatistical modeling used to map subglacial lakes, J. Geophys. Res.-Earth, 125, e2019JF005420, https://doi.org/10.1029/2019JF005420, 2020. a
Mikucki, J. A., Lee, P. A., Ghosh, D., Purcell, A. M., Mitchell, A. C., Mankoff, K. D., Fisher, A. T., Tulaczyk, S., Carter, S., Siegfried, M. R., Fricker, H. A., Hodson, T., Coenen, J., Powell, R., Scherer, R., Vick-Majors, T., Achberger, A. A., Christner, B. C., Tranter, M., and the WISSARD Science Team: Subglacial Lake Whillans microbial biogeochemistry: a synthesis of current knowledge, Philos. T. Roy. Soc. A, 374, 20140290, https://doi.org/10.1098/rsta.2014.0290, 2016. a
Oswald, G. and Robin, G. D.: Lakes beneath the Antarctic ice sheet, Nature, 245, 251–254, 1973. a
Paden, J., Akins, T., Dunson, D., Allen, C., and Gogineni, P.: Ice-sheet bed 3-D tomography, J. Glaciol., 56, 3–11, 2010. a
Pattyn, F.: Antarctic subglacial conditions inferred from a hybrid ice sheet/ice stream model, Earth Planet. Sc. Lett., 295, 451–461, 2010. a
Peters, L., Anandakrishnan, S., Holland, C., Horgan, H., Blankenship, D., and Voigt, D.: Seismic detection of a subglacial lake near the South Pole, Antarctica, Geophys. Res. Lett., 35, L23501, https://doi.org/10.1029/2008GL035704, 2008. a
Rahnemoonfar, M., Fox, G. C., Yari, M., and Paden, J.: Automatic ice surface and bottom boundaries estimation in radar imagery based on level-set approach, IEEE T. Geosci. Remote, 55, 5115–5122, 2017. a
Robin, G. D. Q.: Ice movement and temperature distribution in glaciers and ice sheets, J. Glaciol., 2, 523–532, 1955. a
Robin, G. d. Q., Evans, S., and Bailey, J. T.: Interpretation of radio echo sounding in polar ice sheets, Philos. T. Roy. Soc. Lond.-A, 265, 437–505, 1969. a
Robin, G. D. Q., Swithinbank, C., and Smith, B.: Radio echo exploration of the Antarctic ice sheet, Int. Assoc. Sci. Hydrol. Publ., 86, 97–115, 1970. a
Schroeder, D. M., Broome, A. L., Conger, A., Lynch, A., Mackie, E. J., and Tarzona, A.: Radiometric analysis of digitized Z-scope records in archival radar sounding film, J. Glaciol., 68, 733–740, 2022. a
Siegert, M. J.: Antarctic subglacial lakes, Earth-Sci. Rev., 50, 29–50, 2000. a
Siegert, M. J. and Ridley, J. K.: Determining basal ice-sheet conditions in the Dome C region of East Antarctica using satellite radar altimetry and airborne radio-echo sounding, J. Glaciol., 44, 1–8, 1998. a
Siegfried, M. R., Fricker, H. A., Carter, S. P., and Tulaczyk, S.: Episodic ice velocity fluctuations triggered by a subglacial flood in West Antarctica, Geophys. Res. Lett., 43, 2640–2648, 2016. a
Smith, A. M., Woodward, J., Ross, N., Bentley, J., Hodgson, D. A., Siegert, M. J., and King, E. C.: Evidence for the long-term sedimentary environment in an Antarctic subglacial lake, Earth Planet. Sc. Lett., 504, 139–151, 2018. a
Stearns, L. A., Smith, B. E., and Hamilton, G. S.: Increased flow speed on a large East Antarctic outlet glacier caused by subglacial floods, Nat. Geosci., 1, 827–831, 2008. a
Studinger, M., Bell, R. E., and Tikku, A. A.: Estimating the depth and shape of subglacial Lake Vostok's water cavity from aerogravity data, Geophys. Res. Lett., 31, L12401, https://doi.org/10.1029/2004GL019801, 2004. a
Varshney, D., Rahnemoonfar, M., Yari, M., and Paden, J.: Deep ice layer tracking and thickness estimation using fully convolutional networks, in: 2020 IEEE International Conference on Big Data (Big Data), 3943–3952, https://doi.org/10.1109/BigData50022.2020.9378070, 2020. a
Varshney, D., Rahnemoonfar, M., Yari, M., Paden, J., Ibikunle, O., and Li, J.: Deep learning on airborne radar echograms for tracing snow accumulation layers of the Greenland ice sheet, Remote Sens., 13, 2707, https://doi.org/10.3390/rs13142707, 2021. a, b
Xu, M., Crandall, D. J., Fox, G. C., and Paden, J. D.: Automatic estimation of ice bottom surfaces from radar imagery, in: 2017 IEEE International Conference on Image Processing (ICIP), 340–344, https://doi.org/10.1109/ICIP.2017.8296299, 2017. a
Yari, M., Rahnemoonfar, M., and Paden, J.: Multi-scale and temporal transfer learning for automatic tracking of internal ice layers, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 6934–6937, https://doi.org/10.1109/IGARSS39084.2020.9323758, 2020. a
Zeising, O., Steinhage, D., Nicholls, K. W., Corr, H. F. J., Stewart, C. L., and Humbert, A.: Basal melt of the southern Filchner Ice Shelf, Antarctica, The Cryosphere, 16, 1469–1482, https://doi.org/10.5194/tc-16-1469-2022, 2022. a
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 known inventories and can be used to expand the subglacial lake inventory. Additionally, this analysis may also provide insights into interpreting other subglacial conditions.
Subglacial lakes are a unique environment at the bottom of ice sheets, and they have distinct...