Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into 2805 tiles and reconstruct their high-resolution topography by using a transfer learning approach with freely available low-resolution digital elevation models (DEMs) and limited high-resolution DEMs. A high-frequency terrain feature-based deep residual network is proposed to generate high-resolution global mid-ocean ridge DEMs. In this network, topographic knowledge related to mid-ocean ridges is integrated and quantified to improve the learning efficiency and reconstruction quality of the network. A series of verifications and evaluations demonstrate the reliability of reconstructed topographies for submarine topography research. We observe that reconstructed topography can achieve good environmental understanding and information acquisition in the global mid-ocean ridge range. We find that the complexity of the previous terrain environment is underestimated by 26.63% in terms of the slope gradient and by 14.95% in terms of terrain relief, while a 101.10% information improvement can be obtained for the reconstructed topography. The reconstructed topography indicates that diverse and intricate topographical environments of mid-ocean ridges exist among different ocean regions. The proposed transfer learning method for reconstructing high-resolution mid-ocean ridge topographies is valuable and can be utilized for reconstructing information in regions that are difficult to observe directly and lack sufficient data.

中文翻译：

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迁移学习重建全球洋中脊的海底地形


大洋中脊是地球上独特的、构造活跃的地理单元，深刻地控制着全球范围内的海洋环境和动态。然而，由于海底探测困难，大洋中脊的高分辨率地形数据很少获得，这进一步限制了全球范围内的海洋研究。在这里，我们将全球洋中脊系统划分为 2805 个区块，并通过使用迁移学习方法以及免费提供的低分辨率数字高程模型 (DEM) 和有限的高分辨率 DEM 来重建其高分辨率地形。提出了一种基于高频地形特征的深度残差网络来生成高分辨率的全球洋中脊 DEM。在该网络中，整合并量化了与洋中脊相关的地形知识，以提高网络的学习效率和重建质量。一系列的验证和评估证明了重建地形用于海底地形研究的可靠性。我们观察到，重建地形可以在全球洋中脊范围内实现良好的环境理解和信息获取。我们发现，先前地形环境的复杂性在坡度方面被低估了26.63％，在地形起伏方面被低估了14.95％，而重建地形可以获得101.10％的信息改进。重建的地形表明，不同洋区之间存在着多样且复杂的洋中脊地形环境。 所提出的用于重建高分辨率洋中脊地形的迁移学习方法很有价值，可用于重建难以直接观察和缺乏足够数据的区域的信息。