使用基于深度学习方法的近场脉冲信号,对分层的海底进行地声逆转
Yuxuan Ma1,2, Li He1, Zhaohui Peng1,2
1State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
The Journal of the Acoustical Society of America
|January 21, 2026
概括
这项研究引入了一种深度学习方法,用于分析海底声学反射的地球声学逆转. 交叉ViT模型准确地检索海底层和参数,优于其他深度学习方法.
科学领域:
- 海洋地质物理学 海洋地质物理学
- 声学海洋学是一门学科.
- 深度学习应用程序深度学习应用程序
背景情况:
- 海底声学反射系数显示频率和角度依赖的振荡.
- 这些振荡与海底分层和地球声学特性密切相关.
研究的目的:
- 研究海底层和地声学参数对声学反射模式的影响.
- 开发和验证一个深度学习的地球声学倒置方法,用于海底特征.
主要方法:
- 数字模拟来分析底部反射系数 (BRC).
- 实现一个具有自我注意力和交叉注意力机制的Cross-ViT深度神经网络.
- 多任务学习策略与梯度正常化 (GradNorm) 用于地球声学参数反转.
主要成果:
- 交叉ViT模型有效地学习了BRC特征和地声学参数之间的复杂映射.
- 与CNN和变压器模型相比,提出的深度学习方法表现出卓越的性能和噪声稳定性.
- 通过使用来自南中国海的真实世界声学逆转数据成功验证.
结论:
- 深度学习,特别是Cross-ViT架构,为海底地声逆转提供了一个强大的工具.
- 开发的方法准确地从声学反射数据中检索近场海底分层和地声学参数.
- 这种方法有助于理解和描述复杂的海底环境.
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