听雨:用于宽带声学降雨检测的主要组件分析和线性差异分析
C Mallary1, C J Berg2, J R Buck2
1School of Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, Massachusetts 02744, USA.
The Journal of the Acoustical Society of America
|July 28, 2023
概括
使用宽带数据和主要组件分析 (PCA) 与线性区分分析 (LDA) 的新声学算法有效检测降雨事件. 这种方法改进了海洋学声学雨水检测的旧技术.
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:
- 在海洋上的降雨会产生声学信号.
- 以前的算法使用了窄带频率 (Ma和Nystuen,2005) 或宽带数据平均超过1小时 (Trucco等人,2022).
- 在浅,杂的环境中,超过1小时的平均值是不可行的.
研究的目的:
- 实施和评估一个雨量检测算法使用宽带声学数据与3分钟的时间分辨率.
- 使用主要组件分析 (PCA) 减少宽带声学数据的维度.
- 在PCA转换的数据上使用线性差异分析 (LDA) 检测降雨.
主要方法:
- 利用宽带声学数据,由水声机收集.
- 应用主要组件分析 (PCA) 用于缩小维度.
- 在雨探测的主要组件预测上使用线性差异分析 (LDA).
- 在5个月的数据中训练并测试了算法,这些数据来自一个浅,杂的海湾.
主要成果:
- 该PCA/LDA算法实现了78±5%的降雨事件>1毫米/小时和73±5%的降雨事件>0.1毫米/小时的检测.
- 这两种检测率均以大约1%的错误报警率实现.
- 马和尼斯图恩的算法在同一数据集上仅检测到32 ± 5%的降雨事件> 1.0 mm/h.
结论:
- 开发的PCA/LDA算法提供了有效的降雨检测从宽带声学数据在3分钟的分辨率.
- 这种方法适用于更长的平均时间不切实际的环境.
- 新的算法在检测降雨事件方面明显优于Ma和Nystuen的窄带方法.
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