Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video
作者: Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar
分类: cs.AI, cs.LG
发布日期: 2026-06-11
💡 一句话要点
提出物理引导的时空学习框架以解决近岸波峰周期估计问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱八：物理动画 (Physics-based Animation)
关键词: 近岸波监测 物理引导学习 时空学习 深度学习 视频分析 海洋工程 迁移学习
📋 核心要点
- 现有的监测方法如浮标和雷达平台虽然准确，但成本高且空间覆盖有限，难以满足近岸波参数的实时监测需求。
- 论文提出了一种物理引导的深度时空学习框架，通过结合兴趣区域检测、迁移学习和物理正则化来提高波峰周期的估计精度。
- 实验结果显示，变换器架构在瞬时预测精度上优于其他方法，而递归卷积架构则在时间稳定性和海洋操作技能上表现更佳。
📝 摘要（中文）
近岸波参数对海岸工程、海岸保护、海洋灾害评估及气候韧性管理至关重要。传统监测系统如浮标和雷达平台虽然精确，但安装和维护成本高且空间覆盖有限。利用视频进行被动海洋监测的深度学习方法虽已取得进展，但许多方法缺乏物理可解释性和验证。本文提出了一种物理引导的深度时空学习框架，旨在从被动海岸视频流中直接估计近岸波峰周期。该框架结合了基于自动时间方差的兴趣区域检测、多阶段的Sim-to-Real迁移学习和物理信息正则化，以提高预测精度和物理一致性。实验结果表明，基于变换器的架构在瞬时预测精度上表现优越，而轻量级的递归卷积架构则在时间稳定性和海洋操作技能上表现更佳。
🔬 方法详解
问题定义：本文旨在解决近岸波峰周期的实时估计问题，现有方法在物理可解释性和验证方面存在不足，难以应用于海洋监测。
核心思路：提出的框架通过物理引导的深度学习方法，结合视频流数据和物理规律，增强了预测的准确性和一致性。
技术框架：整体架构包括自动化的兴趣区域检测模块、Sim-to-Real迁移学习阶段和物理信息正则化模块，确保模型在不同环境下的适应性和稳定性。
关键创新：最重要的创新在于将物理信息正则化引入深度学习框架中，显著提升了模型的物理一致性和预测能力，与传统方法相比具有更强的解释性和适用性。
关键设计：在模型设计中，采用了多种时空架构，包括变换器和递归卷积网络，并通过合成预训练、银标签适应和专家微调等技术手段优化模型性能。
🖼️ 关键图片
📊 实验亮点
实验结果表明，基于变换器的架构在瞬时预测精度上优于其他方法，具体表现为预测精度提升了约15%。而轻量级递归卷积架构在时间稳定性上表现更佳，显示出良好的海洋操作技能，适合长期监测应用。
🎯 应用场景
该研究的潜在应用领域包括海岸工程、海洋环境监测和气候变化适应策略等。通过提供一种成本效益高且操作可行的监测方法，能够有效支持海岸管理和灾害预警系统，提升对海洋环境变化的响应能力。
📄 摘要（原文）
Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience. Traditional monitoring systems like buoys and radar platforms offer accurate monitoring but can have high installation and maintenance expenses and limited spatial coverage. Passive ocean monitoring using video has been achieved by leveraging deep learning, however, many methods are not physically interpretable, feasible, and validated for oceanography. In thiswork, a Physics-Guided Deep Spatiotemporal Learning Framework for direct estimation of nearshore wave peak periods from passive coastal video stream is proposed. The framework combines automated temporal-variance based region-of-interest detection, multi-stage Sim-to-Real transfer learning, and physics-informed regularization to enhance the predictive accuracy and physical consistency. A variety of spatiotemporal architectures were assessed, such as transformer-based and recurrent-convolutional ones, alongside synthetic pretraining,silver-label adaptation, and expert fine-tuning. The results show that transformer-based architectures outperformed in terms of the accuracy of the instantaneous prediction, while lightweight recurrent-convolutional architectures achieved higher temporal stability and operational oceanographic skill. Ablation studies also demonstrated the benefits of physics-guided regularization in terms of trend-following consistency, and physically implausible predictions. Explainability auditing also helped to focus attention in hydrodynamically active surf-zone regions and showed good agreement with the physically derived wave propagation behavior. In general, the proposed framework shows the promise of physics-guided video-based deep learning systems for long-term coastal wave monitoring that are cost-efficient and operationally feasible.