AI 中文总结
针对水下传输损耗预测中FNO的频谱偏差问题，提出S2RL框架，通过频谱全局传播器与空间局部细化器的从粗到细结构，在保持毫秒级推理速度的同时，显著优于FNO基线。
AI 中文摘要
快速准确预测水下声学传输损耗对实时海洋声学应用至关重要。傅里叶神经算子（Fourier Neural Operators, FNO）凭借其全局感受野成为强大的替代模型，但存在频谱偏差问题。FNO中的频率截断机制会过滤掉高频分量，导致预测结果过度平滑，无法捕捉细粒度的干涉模式。为克服这一局限，本文提出一种频谱-空间残差学习（Spectral-Spatial Residual Learning, S2RL）框架。S2RL将预测任务分解为从粗到细的过程：频谱全局传播器首先生成全局一致的预测，空间局部细化器随后恢复高频残差。在中国南海数据集上的实验表明，所提方法显著优于FNO基线，同时保持毫秒级推理速度。
英文摘要
Predicting underwater acoustic transmission loss rapidly and accurately is crucial for real-time ocean acoustic applications. While Fourier Neural Operators (FNO) have emerged as powerful surrogate models due to their global receptive fields, they suffer from spectral bias. The frequency truncation mechanism in FNO filters out high-frequency components, resulting in over-smoothed predictions that fail to capture fine-grained interference patterns. To overcome this limitation, this paper proposes a Spectral-Spatial Residual Learning (S2RL) framework. S2RL decomposes the prediction task into a coarse-to-fine process: a spectral Global Propagator first generates a globally consistent prediction, and a spatial Local Refiner subsequently recovers the high-frequency residuals. Experimental results on a South China Sea dataset show that the proposed method significantly outperforms FNO baselines while maintaining millisecond-level inference speeds.
