混合数据和模型驱动的三维海洋声速场超分辨率:扩散模型遇到低级张量器
Yifan Sun1, Siyuan Li1, Shikai Fang2
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
The Journal of the Acoustical Society of America
|May 16, 2025
概括
一个新的混合超分辨率 (SR) 算法Hybrid-DOT通过结合扩散模型和低级张量法来增强3D声速场 (SSF). 这提高了水下声学分析,即使有噪音,低分辨率的数据.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 信号处理 信号处理
背景情况:
- 高分辨率的3D声速场 (SSF) 对水下声学至关重要.
- 目前的SSF观测系统产生低分辨率,噪音数据,限制应用.
- 现有的超分辨率 (SR) 方法在稀疏,杂的SSF数据中存在微妙的变化.
研究的目的:
- 开发一种先进的SR方法,用于准确的3DSSF重建.
- 克服纯粹基于模型或数据驱动的SR技术的局限性.
- 改进海洋声传播和声传输损失的表征.
主要方法:
- 提出了混合DOT,这是一个混合SR算法,集成了数据驱动和模型驱动的方法.
- 采用预训练的扩散模型来获取数据分布洞察力.
- 利用低级张量 (LRT) 建模用于域知识集成和计算效率.
主要成果:
- 混合DOT在最先进的方法中表现出优越的性能.
- 该算法在各种SR因素和噪声水平上实现了高精度.
- 改进的SSF重建使得细粒度声传输损失的精确表征成为可能.
结论:
- 混合DOT有效地从有限的数据中重建高分辨率的3DSSF.
- 混合方法为水下声学应用提供了更高的精度和效率.
- 这种方法提升了分析复杂海洋声音传播环境的能力.
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