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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Learning-Driven Vector Acoustic Field Inversion

Deep Learning-Driven Vector Acoustic Field Inversion

Li, Xiaoman

Paperback

Physics

ISBN10: 6209141501
ISBN13: 9786209141508
Publisher: Scholars' Press
Published: Oct 22 2025
Pages: 296
Weight: 0.88
Height: 0.67 Width: 6.00 Depth: 9.00
Language: English
This monograph presents a deep learning framework for seabed characterization by fusing vector acoustic field physics with neural networks. It introduces Stokes parameters from vector hydrophones as robust features for geoacoustic inversion, and develops specialized networks (BP, MTL-TCN, U-Net + ATT-BP) to estimate sediment parameters and extract dispersion curves. Validated in the Yellow Sea, the method achieves core-comparable accuracy in minutes, significantly outperforming traditional techniques in speed and robustness. The work highlights the synergy between physical principles and data-driven learning, offering a scalable solution for real-time seabed mapping and advancing autonomous ocean sensing.

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