Name: Deep Learning-Driven Vector Acoustic Field Inversion
Price: 3829.00 TWD
Availability: InStock
Author: Li, Xiaoman
ISBN: 9786209141508

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portada Deep Learning-Driven Vector Acoustic Field Inversion

Deep Learning-Driven Vector Acoustic Field Inversion

Type

Physical Book

Publisher

Scholars' Press

Author

Li, Xiaoman

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

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

Synopsis "Deep Learning-Driven Vector Acoustic Field Inversion"

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