使用多通道船舶噪声进行深度学习海底分类的整体方法)
Ginger E Lau1, Michael C Mortenson2, Tracianne B Neilsen2
1Department of Physics, Emory University, Atlanta, Georgia 30322, USA.
The Journal of the Acoustical Society of America
|March 26, 2025
概括
神经网络预测了海底类别,使用来自水音机数据的航运噪声. 整体建模和多通道输入提高了准确性,揭示了与地球声学倒置一致的海底相似之处.
科学领域:
- 海洋声学 海洋声学
- 机器学习 机器学习
- 海底特征的描述
背景情况:
- 船舶噪音的水音录音包含了关于浅水环境中的海底的信息.
- 以前用于海底分类的方法在复杂的声学条件下受到限制.
研究的目的:
- 训练神经网络以预测海底类别,使用船舶噪声的多通道液电声谱图.
- 为了评估不同数量的水声道对预测准确性的影响.
- 应用集体建模来改善性能和信心评估.
主要方法:
- 合成数据被用来训练ResNet-18神经网络在一个,两个,四个和八个水电话通道上.
- 训练网络应用于2017年海底表征实验 (SBCEX 2017) 的测量船舶光谱图.
- 采用数据预处理和集合建模技术来增强结果.
主要成果:
- 神经网络成功地从测量的船舶光谱图中预测了海底等级.
- 预测趋向于两个海底类别,具有相似的浅层沉积物特性.
- 结果与2017年SBCEX的独立地球声学逆转发现一致.
- 合并建模为预测提供了一定的信心和精度.
结论:
- 在船舶噪音上训练的神经网络对于海底分类是有效的.
- 多通道水声器数据和整体建模提高了预测的准确性和可靠性.
- 这种方法在各种海洋学条件下对海床的表征有希望.
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