Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details

The ongoing accumulation of radio-echo sounding (RES) measurements in Antarctica in recent years has significantly expanded our understanding of subglacial structures. The effective use of RES-collected data to construct accurate Antarctic subglacial topography has emerged as a vital component of contemporary polar research. Various methods, including conventional interpolation, inversion techniques, and even deep learning methods, have been used to recreate Antarctic bed topography. However, these bed topographies are often plagued by over-smoothing, loss of small-scale features, low precision, and instability.The Siamese topographic generation model (STGM) is proposed here to address the above mentioned issues. After being trained on ArcticDEM, this model can generate Antarctic subglacial topography with stability and accuracy by merging the advantages of deep learning-based generative models, Siamese networks, kernel prediction, and deformable convolutions. In terms of evaluation, both quantitative and qualitative comparisons with current Antarctic subglacial digital elevation models demonstrate that our method can generate topographical features, such as mountains, ice streams, and valleys, with high precision and minimal artifacts. In quantitative validation, our model achieves over 20% improvement in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to the previously best-performing method (GEI), surpassing existing models in terms of accuracy and detail.Moreover, an error analysis specifically focusing on the effect of varying track intervals has been conducted, offering a benchmark for future investigations into the influence of track density on model errors. Finally, using STGM based on the RES data, the subglacial topography of Princess Elizabeth Land has also been successfully generated. In this area, the topography generated by STGM at a resolution of 500 m clearly depicts subglacial lakes and valleys, revealing the complexity and diversity of the subglacial topography.