从船舶交通记录和多视图神经网络中估计水下声速概况
Joseph L Walker1, Zheng M Zeng2, Vanessa M ZoBell1
1Scripps Institution of Oceanography, University of California San Diego, San Diego, California 92093-0238, USA.
The Journal of the Acoustical Society of America
|May 8, 2024
概括
这项研究使用水下船舶噪声和深度学习来估计海洋声速概况. 利用多个声学记录显著提高了水下声学应用的准确性.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 声学 声学
- 机器学习 机器学习
背景情况:
- 音速对于解释水下声学数据至关重要.
- 海洋声速的概况是复杂的和可变的.
- 测量声音速度的传统方法可能是资源密集的.
研究的目的:
- 研究使用水下船舶环境噪声作为声源来估计海洋声速概况.
- 为此目的,开发和评估基于深度学习的反转方案.
- 评估使用船舶过境的多次声学观测的好处.
主要方法:
- 一个深度学习倒置模型是使用来自移动船只的水下辐射噪声来开发的.
- 数据包括自动识别系统 (AIS) 和来自圣巴巴拉频道 (2015-2017) 的声学录音.
- 该模型使用船舶噪声和描述器回归了水柱顶部200米的声速配置文件.
主要成果:
- 单次过境模型实现了深度平均根平均平方误差 (RMSE) 的1.79 m/s.
- 多通道模型提高了准确性,产生了1.55m/s的RMSE.
- 这两种模型的表现都超过了季节平均预测 (2.80 m/s RMSE).
结论:
- 水下船舶噪声可以有效地用于估计海洋声速概况.
- 深度学习方法,特别是使用多次传输数据,为准确估计声音速度提供了一个有希望的方法.
- 这种技术为海洋声学研究和监测提供了宝贵的工具.
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