使用深度神经网络对区域声传播场的快速预测方法的研究
Chuxiong Wang1,2, Cheng Chen1,2, Xiao Feng1,2
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China.
JASA express letters
|January 23, 2026
概括
这项研究介绍了一种快速卷积神经网络,用于预测水下声学传播场. 该方法使用地形数据进行快速,准确的传输损失预测,非常适合实时应用.
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:
- 传统的水下声学传播建模是计算密集的.
- 对声场的准确预测对于海军和环境应用至关重要.
研究的目的:
- 开发一种使用卷积神经网络 (CNN) 的水下声学传播场的快速预测方法.
- 为了降低与传统声学传播模型相关的计算成本.
主要方法:
- 开发了一种基于CNN的方法,利用区域地形特征来训练模型.
- 建立了一个数据集,以捕捉不同地形条件下的声传输损失模式.
- 该模型在西太平洋进行了测试,以评估其预测准确性和速度.
主要成果:
- 在CNN模型中,非平滑字段的根平均平方误差为3.48dB.
- 平均预测时间为每批10个样本1.95毫秒,显著提高了速度.
- 该模型准确地学习了基于简化地形输入的声传输损失模式.
结论:
- 开发的CNN方法提供了一个计算效率高的替代方案,用于预测水下声学传播.
- 这种方法显示了在各种水下环境中实时声学传播预测的潜力.
- 简化输入功能可以有效地驱动准确的声场预测.
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