Mesh generation for underwater acoustic modeling with KRAKEN

Monteiro, Nuno M.R.; Oliveira, Tiago C. A.

doi:10.1016/j.advengsoft.2023.103455

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2023

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“…In this section, the objective inversion parameters are first listed, and the simulation environment is described, which is based on the Kraken acoustic model [24]. Furthermore, the experimental setup is given for reproducibility.…”

Section: Experiments and Analysismentioning

confidence: 99%

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

Feng

Zhu

et al. 2023

JMSE

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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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Section: Experiments and Analysismentioning

confidence: 99%