基于物理和机器学习的海流速度从流动噪声中获取)
Tsuwei Tan1, Oleg A Godin2, Matthew W Walters2,3
1Department of Marine Science, Republic of China (ROC) Naval Academy, 813 Kaohsiung, Taiwan.
The Journal of the Acoustical Society of America
|February 12, 2025
概括
研究人员使用声学噪声记录器测量了亚特兰蒂斯二号海底山上的深海电流. 他们发现电流速度和超声波声学噪声之间存在很强的相关性,这使得使用机器学习能够准确地估计电流速度.
科学领域:
- 海洋学 海洋学 海洋学
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 深海电流对于了解海洋动态至关重要.
- 在很深的海底测量这些电流带来了巨大的挑战.
- 声学噪声可能会受到水流的影响.
研究的目的:
- 为了研究近底电流速度和深海海底山上的声噪强度之间的关系.
- 开发一种量化流量噪声的方法,并将其与环境声音区分开来.
- 利用机器学习从声学数据中估计深海电流的速度.
主要方法:
- 在深度>2500米处部署固定式自主声学噪声记录仪 (MANR).
- 对声学数据的分析,专注于超声波频率 (<20 Hz).
- 开发和应用一个回归树机器学习模型.
主要成果:
- 观察到电流速度与声噪声强度之间存在强烈的相关性.
- 流量噪声和环境噪声 (包括船舶噪声) 被确定为具有明显光谱特性的可比贡献因素.
- 机器学习模型仅使用声学数据,成功估计了1分钟分辨率的当前速度.
结论:
- 声学噪声测量,特别是在超声波频率,提供了一个可行的方法来估计深海电流的速度.
- 开发的机器学习方法为远程监测深海水流提供了一种新的方法.
- 这种技术可以提高我们对数据稀疏的深海环境中的海洋学过程的理解.
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