The abundant spectral data provided by satellite technology are crucial for interpreting the complex marine environment, and the effective and accurate analysis of these data is particularly important for coastal engineering. In this regard, this study proposes a Physically Informed ViT-GAN (PI-ViT-GAN) automatic partitioning method, based on CFOSAT satellite wave spectrum data. Specifically, the model consists of a generator and discriminator. The generator utilizes a contrastive learning strategy as pretraining and through the self-attention mechanism of the ViT model, it focuses on key parts of the spectrum to extract wave group features and wave element parameters. Partitioning-head joint training realizes the output of wave group partition element indices. Subsequently, the discriminator uses the wave group features and a parametric model for spectrum reconstruction and computes the error with the original observed spectrum to evaluate the partition and reconstruction effects. Additionally, this model incorporates two physically corrected functions, wave system classification loss and merging loss, based on the wave age criterion, thereby guiding the training process, and enhancing model efficiency. The results indicate that the reconstructed theoretical spectrum, obtained through the utilization of this method, aligns well with the original sea wave spectrum, demonstrating a precision superior to the spectral partitioning product of CFOSAT's own SWIM. Combining the robust learning capability of the transformer and the regularization of physical prior knowledge, this model can achieve precise, low-cost automated analysis of satellite wave spectra, providing a new scalable method for big data analysis in marine and coastal engineering.

中文翻译：

---

基于PI-vit-GAN(physically-informed ViT-GAN)方法的卫星波二维频谱划分

卫星技术提供的丰富的光谱数据对于解释复杂的海洋环境至关重要，而这些数据的有效、准确的分析对于海岸工程尤为重要。对此，本研究提出了一种基于CFOSAT卫星波谱数据的物理信息ViT-GAN（PI-ViT-GAN）自动划分方法。具体来说，该模型由生成器和判别器组成。生成器采用对比学习策略作为预训练，通过ViT模型的自注意力机制，关注频谱的关键部分，提取波群特征和波元参数。分区头联合训练实现波群分区元素索引的输出。随后，鉴别器利用波群特征和参数模型进行频谱重建，并计算与原始观测频谱的误差，以评估划分和重建效果。此外，该模型基于波龄标准，结合了波浪系统分类损失和合并损失两个物理校正函数，从而指导训练过程，提高模型效率。结果表明，利用该方法重建的理论频谱与原始海浪频谱吻合较好，精度优于CFOSAT自有SWIM的频谱划分产品。该模型结合变压器的鲁棒学习能力和物理先验知识的正则化，可以实现卫星波谱的精确、低成本自动化分析，为海洋和海岸工程大数据分析提供了一种新的可扩展方法。