Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1543-2026
© Author(s) 2026. 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-20-1543-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mapping Antarctic geothermal heat flow with deep neural networks optimized by particle swarm optimization algorithm
Shaoxia Liu
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Key Laboratory of Polar Science of Ministry of Natural Resources (MNR), Polar Research Institute of China, Shanghai 200136, China
Key Laboratory of Polar Science of Ministry of Natural Resources (MNR), Polar Research Institute of China, Shanghai 200136, China
School of Oceanography, Shanghai Jiao Tong University, Shanghai 200230, China
Key Laboratory of Polar Atmosphere-Ocean-Ice System for Weather and Climate, Ministry of Education, Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
Ocean College, Zhejiang University, Zhoushan 316021, China
Shuhu Yang
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Lijuan Wang
Key Laboratory of Polar Science of Ministry of Natural Resources (MNR), Polar Research Institute of China, Shanghai 200136, China
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Jianjie Liu
Key Laboratory of Polar Science of Ministry of Natural Resources (MNR), Polar Research Institute of China, Shanghai 200136, China
School of Oceanography, Shanghai Jiao Tong University, Shanghai 200230, China
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Chengyan Liu, Zhaomin Wang, Dake Chen, Xianxian Han, Hengling Leng, Xi Liang, Liangjun Yan, Xiang Li, Craig Stevens, Andrew Hogg, Kazuya Kusahara, Kaihe Yamazaki, Kay Ohshima, Meng Zhou, Xiao Cheng, Dongxiao Wang, Changming Dong, Jiping Liu, Qinghua Yang, Xichen Li, Ruibo Lei, Minghu Ding, Zhaoru Zhang, Dujuan Kang, Di Qi, Tongya Liu, Jihai Dong, Lu An, Ru Chen, Tong Zhang, Xiaoming Hu, Bo Han, Haibo Bi, Qi Shu, Longjiang Mu, Shiming Xu, Hu Yang, Hailong Liu, Tingfeng Dou, Zhixuan Feng, Lei Zheng, Xueyuan Tang, Guitao Shi, Yongqing Cai, Bingrui Li, Yang Wu, Xia Lin, Wenjin Sun, Yu Liu, Kai Yu, Yu Zhang, Weizeng Shao, Xiaoyu Wang, Shaojun Zheng, Chengyi Yuan, Chunxia Zhou, Jian Liu, Yang Liu, Yue Xia, Xiaoyu Pan, Jiabao Zeng, Kechen Liu, Jiahao Fan, Chen Cheng, and Qi Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-6487, https://doi.org/10.5194/egusphere-2025-6487, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We developed a high-resolution computer model to simulate how the ocean, sea ice, and ice shelves interact around Antarctica. This helps us understand their critical role in global climate and sea-level rise. Our model successfully captures essential features like major currents and seasonal ice changes. Despite some remaining biases, it provides a useful tool for predicting future changes in this vital and rapidly evolving region.
Zhengyi Song, Yudi Pan, Jiangtao Li, Hongrui Peng, Yiming Wang, Yuande Yang, Kai Lu, Xueyuan Tang, and Xiaohong Zhang
The Cryosphere, 19, 6341–6353, https://doi.org/10.5194/tc-19-6341-2025, https://doi.org/10.5194/tc-19-6341-2025, 2025
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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. 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, Vjeran Višnjević, Rodrigo Zamora, and Alexandra Zuhr
The Cryosphere, 19, 4611–4655, https://doi.org/10.5194/tc-19-4611-2025, https://doi.org/10.5194/tc-19-4611-2025, 2025
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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 working together on these archival data to build a comprehensive 3-D picture of how old the ice is everywhere across Antarctica and how this is being used to reconstruct past and to predict future ice and climate behaviour.
Haifeng Huo, Hui Xu, Jixiu Wu, Tao Li, Jingjin Liu, Enzhao Xiao, and Xueyuan Tang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4768, https://doi.org/10.5194/egusphere-2025-4768, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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Through a series of direct shear tests, this study analyzes the variation of shear strength parameters (cohesion and internal friction angle) in compacted snow under different conditions of density, sintering time, and temperature. A Genetic Algorithm-Back Propagation neural network model was subsequently developed to establish systematic benchmark values for these parameters. This work provides essential data and a predictive framework for the reliable design of snow structures in cold regions.
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
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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.
Sheng Dong, Lei Fu, Xueyuan Tang, Zefeng Li, and Xiaofei Chen
The Cryosphere, 18, 1241–1257, https://doi.org/10.5194/tc-18-1241-2024, https://doi.org/10.5194/tc-18-1241-2024, 2024
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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.
L. Wang, G. Qiao, I. V. Florinsky, and S. Popov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 785–791, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-785-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-785-2022, 2022
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Short summary
Heat from inside the Earth beneath Antarctica affects how fast ice melts and how quickly the sea level rises, but direct measurements are very limited. We built a data-driven computer model that learns the complex links between geophysical features and geothermal heat flow and reports confidence. We find lower heat flow in East Antarctica and higher heat flow in West Antarctica, especially near coasts.
Heat from inside the Earth beneath Antarctica affects how fast ice melts and how quickly the sea...