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portada Deep Learning-Driven Vector Acoustic Field Inversion
Type
Physical Book
Publisher
Author
Language
English
Pages
296
Format
Paperback
Dimensions
22.9 x 15.2 x 1.7 cm
ISBN13
9786209141508

Deep Learning-Driven Vector Acoustic Field Inversion

Li, Xiaoman (Author) · Scholars' Press · Paperback

Deep Learning-Driven Vector Acoustic Field Inversion - Li, Xiaoman

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Synopsis "Deep Learning-Driven Vector Acoustic Field Inversion"

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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