IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting

📄 arXiv: 2507.00036v1 📥 PDF

作者: Rohan Putatunda, Sanjay Purushotham, Ratnaksha Lele, Vandana P. Janeja

分类: cs.LG, physics.ao-ph

发布日期: 2025-06-18

备注: 16 pages, 4 figures

💡 一句话要点

提出IDRIFTNET以解决冰山漂移预测问题

🎯 匹配领域: 支柱八：物理动画 (Physics-based Animation)

关键词: 冰山漂移 深度学习 物理驱动模型 时空预测 南极研究 环境监测 机器学习

📋 核心要点

  1. 现有方法在冰山轨迹预测中面临数据稀缺和环境变量复杂性等挑战，导致预测效果不佳。
  2. 本文提出的IDRIFTNET模型结合了物理驱动的分析公式与深度学习，能够有效捕捉冰山漂移的动态特性。
  3. 实验结果显示，IDRIFTNET在南极冰山A23A和B22A上的预测性能显著优于其他先进模型，降低了误差指标。

📝 摘要（中文）

漂流冰山在极地海洋中对地球气候系统起着关键作用，影响海洋淡水流入和区域生态系统，同时对极地导航构成挑战。然而，准确预测冰山轨迹仍然是一项艰巨的任务，主要由于时空数据的稀缺和冰山运动的复杂非线性特性。为了解决这些挑战，本文提出了一种混合模型IDRIFTNET，该模型结合了冰山漂移物理的分析公式与增强残差学习模型，能够有效捕捉冰山漂移的复杂动态。实验结果表明，IDRIFTNET在两个南极冰山上的表现优于现有模型，具有更低的最终位移误差和平均位移误差。

🔬 方法详解

问题定义：本文旨在解决冰山漂移预测中的复杂非线性运动问题，现有方法因数据不足和环境因素影响而难以准确预测轨迹。

核心思路：IDRIFTNET模型通过结合冰山漂移的物理分析与深度学习，学习分析解与实际观测之间的差异，从而提高预测精度。

技术框架：模型包括物理驱动的分析模块和旋转增强的谱神经网络，前者提供物理背景，后者捕捉数据中的全局和局部模式。

关键创新：IDRIFTNET的创新在于将物理模型与深度学习相结合，克服了传统深度学习模型在复杂动态系统中的局限性。

关键设计：模型采用了特定的损失函数来优化分析解与观测值之间的匹配，同时设计了多层次的网络结构以增强特征提取能力。

📊 实验亮点

实验结果表明，IDRIFTNET在南极冰山A23A和B22A上的最终位移误差（FDE）和平均位移误差（ADE）均显著低于现有最先进模型，展示了其在复杂环境下的优越性能。

🎯 应用场景

该研究的潜在应用领域包括极地航行安全、气候变化监测和海洋生态系统管理。通过提高冰山漂移预测的准确性，可以更好地应对极地环境变化带来的挑战，保障航行安全和生态保护。

📄 摘要（原文）

Drifting icebergs in the polar oceans play a key role in the Earth's climate system, impacting freshwater fluxes into the ocean and regional ecosystems while also posing a challenge to polar navigation. However, accurately forecasting iceberg trajectories remains a formidable challenge, primarily due to the scarcity of spatiotemporal data and the complex, nonlinear nature of iceberg motion, which is also impacted by environmental variables. The iceberg motion is influenced by multiple dynamic environmental factors, creating a highly variable system that makes trajectory identification complex. These limitations hinder the ability of deep learning models to effectively capture the underlying dynamics and provide reliable predictive outcomes. To address these challenges, we propose a hybrid IDRIFTNET model, a physics-driven deep learning model that combines an analytical formulation of iceberg drift physics, with an augmented residual learning model. The model learns the pattern of mismatch between the analytical solution and ground-truth observations, which is combined with a rotate-augmented spectral neural network that captures both global and local patterns from the data to forecast future iceberg drift positions. We compare IDRIFTNET model performance with state-of-the-art models on two Antarctic icebergs: A23A and B22A. Our findings demonstrate that IDRIFTNET outperforms other models by achieving a lower Final Displacement Error (FDE) and Average Displacement Error (ADE) across a variety of time points. These results highlight IDRIFTNET's effectiveness in capturing the complex, nonlinear drift of icebergs for forecasting iceberg trajectories under limited data and dynamic environmental conditions.