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Model-based convolutional neural network approach to underwater source-range estimation
The Journal of the Acoustical Society of America ( IF 2.4 ) Pub Date : 2021-01-19 , DOI: 10.1121/10.0003329
R. Chen 1 , H. Schmidt 1
Affiliation  

This paper is part of a special issue on machine learning in acoustics. A model-based convolutional neural network (CNN) approach is presented to test the viability of this method as an alternative to conventional matched-field processing (MFP) for underwater source-range estimation. The networks are trained with simulated data generated under a particular model of the environment. When tested with data simulated in environments that deviate slightly from the training environment, this approach shows improved prediction accuracy and lower mean-absolute-error (MAE) compared to MFP. The performance of this model-based approach also transfers to real data, as demonstrated separately with field data collected in the Beaufort Sea and off the coast of Southern California. For the former, the CNN predictions are consistent with expected source range while for the latter, the CNN estimates have lower MAE compared to MFP. Examination of the trained CNNs' intermediate outputs suggests that the approach is more constrained than MFP from outputting very inaccurate predictions when there is a slight environmental mismatch. This improvement appears to be at the expense of decreased certainty in the correct source range prediction when the environment is precisely modeled.

中文翻译:

基于模型的卷积神经网络方法在水下源范围估计中的应用

本文是关于声学中机器学习的一个特殊问题的一部分。提出了一种基于模型的卷积神经网络(CNN)方法来测试该方法的可行性,以替代传统匹配场处理(MFP)进行水下源范围估计。使用在特定环境模型下生成的模拟数据来训练网络。当在与训练环境略有不同的环境中使用模拟数据进行测试时,与MFP相比,此方法显示出更高的预测准确性和更低的平均绝对误差(MAE)。这种基于模型的方法的性能也可以转换为真实数据,如在波弗特海和南加州沿海地区收集的现场数据分别说明的那样。对于前者 CNN的预测与预期的源范围一致,而后者的CNN估计的MAE低于MFP。对受过训练的CNN的中间输出的检查表明,当环境略有不匹配时,该方法比MFP的输出结果非常不准确,因此受到的约束更大。当对环境进行精确建模时,此改进似乎是以正确源范围预测的确定性降低为代价的。
更新日期:2021-01-20
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