GIT: A Transformer-Based Deep Learning Model for Geoacoustic Inversion

被引:1
|
作者
Feng, Sheng [1 ]
Zhu, Xiaoqian [2 ]
Ma, Shuqing [2 ]
Lan, Qiang [2 ]
机构
[1] Natl Univ Def Technol, Coll Comp Sci, Changsha 410073, Peoples R China
[2] Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Peoples R China
关键词
geoacoustic inversion; deep learning; transformer model; 2-D positional embedding;
D O I
10.3390/jmse11061108
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue with satisfactory results. As a data-driven method, deep learning networks aim to approximate the inverse function of acoustic propagation by extracting knowledge from multiple replicas, outperforming conventional inversion methods. However, existing deep learning networks, mainly incorporating stacked convolution and fully connected neural networks, are simple and may neglect some meaningful information. To extend the network backbone for geoacoustic inversion, this paper proposes a transformer-based geoacoustic inversion model with additional frequency and sensor 2-D positional embedding to perceive more information from the acoustic input. The simulation experimental results indicate that our proposed model achieves comparable inversion results with the existing inversion networks, demonstrating its effectiveness in marine research.
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页数:13
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