美国斯坦福大学Yongji Wang团队利用深度学习研究了南极冰架的流动规律。相关论文于2025年3月14日发表在《科学》杂志上。

南极冰架支撑着地面冰盖，减缓了全球海平面的上升。然而，冰的流动规律和粘度结构等基本力学性质仍存在争议。

在这项工作中，通过利用遥感数据和基于物理知识的深度学习，研究组提供了几个冰架的证据，证明压缩区的流动规律遵循粒度敏感的复合流变学。在扩展区，他们发现冰具有各向异性。研究组构建了冰架范围的各向异性黏度图，捕捉了抑制裂谷传播的缝合带。在接地带附近推断的应力指数决定了接地线冰通量和接地线稳定性，而推断的粘度图则为裂谷的预测提供了信息。两者对于预测南极冰盖未来的质量损失都是至关重要的。

附：英文原文

Title: Deep learning the flow law of Antarctic ice shelves

Author: Yongji Wang, Ching-Yao Lai, David J. Prior, Charlie Cowen-Breen

Issue&Volume: 2025-03-14

Abstract: Antarctic ice shelves buttress the grounded ice sheet, mitigating global sea level rise. However, fundamental mechanical properties, such as the ice flow law and viscosity structure, remain under debate. In this work, by leveraging remote-sensing data and physics-informed deep learning, we provide evidence over several ice shelves that the flow law follows a grain size–sensitive composite rheology in the compression zone. In the extension zone, we found that ice exhibits anisotropic properties. We constructed ice shelf–wide anisotropic viscosity maps that capture the suture zones, which inhibit rift propagation. The inferred stress exponent near the grounding zone dictates the grounding-line ice flux and grounding line stability, whereas the inferred viscosity maps inform the prediction of rifts. Both are essential for predicting the future mass loss of the Antarctic Ice Sheet.

DOI: adp3300

Source: https://www.science.org/doi/10.1126/science.adp3300