这是一个深度学习框架,用于四维海洋声速场预测
Yingjie Li1,2, Jixing Qin1,2, Shuanglin Wu1,2
1College of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
The Journal of the Acoustical Society of America
|February 12, 2026
概括
我们开发了一个新的深度学习模型,Swin Transformer-UNet (ST-UNet),用于准确的海洋声速场 (SSF) 预测. 该模型捕捉了四维的时空信息,显著提高了水下应用的预测准确性.
科学领域:
- 海洋学 海洋学 海洋学
- 地质物理学 地质物理学
- 人工智能的人工智能
背景情况:
- 准确预测海洋声速场 (SSF) 对水下通信,海洋勘探和环境监测至关重要.
- 深度学习模型对SSF预测有希望,但与高维数据作斗争,限制它们到3D特征提取和不完整的时空信息捕获.
研究的目的:
- 开发一种新的深度学习模型,用于四维 (4D) SSF预测,捕获完整的时空信息.
- 提高现有的SSF预测方法的准确性和能力.
主要方法:
- 提出了Swin变压器-UNet (ST-UNet) 模型,集成U-Net和Swin变压器网络.
- 使用Swin变压器通过多头自我注意来进行时空特征提取.
- 使用U-Net通过卷积特征恢复来完善空间细节.
主要成果:
- ST-UNet模型在使用南中国海7天历史数据的24小时SSF预测中实现了0.783m/s的根平均平方误差.
- 与基线架构相比,表现出优越的性能,改进幅度从33%到72%不等.
结论:
- ST-UNet模型有效地预测4DSSF,优于现有方法.
- 这种进步具有改善水下通信,海洋资源勘探和环境监测的巨大潜力.
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