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Acoustic field extrapolation in shallow water using Gaussian process with Normal-Mode-Based Kernel
An Yang1,2,3, Peng Xiao1,2,3, Zhengyu Hou1,2,3
1School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China.
Abstract:
In ocean acoustics, vertical array depth shifts caused by currents lead to missing shallow-water acoustic field data. This study applies a Gaussian process (GP) regression framework with a Normal-Mode-Based Kernel to address the extrapolation problem in transmission loss reconstruction. Based on simulations of the 2024 South China Sea experiment environment and validation with measured data from the SWellEx-96 experiment, the GP approach successfully extrapolates the acoustic field to missing depths. Results demonstrate that, compared to using the transmission loss value at the closest available depth as a reference, the reconstruction error is significantly reduced. Furthermore, by incorporating dominant modal prior information, the Normal-Mode-Based Kernel-GP method maintains reliable performance even for complex sound fields, improving reconstruction stability under array deployment uncertainties.
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