Deep Learning-Driven Vector Acoustic Field Inversion : Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction

Language: English

Published by Scholars' Press, 2025

6209141501 / 9786209141508

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9786209141508

Title
Deep Learning-Driven Vector Acoustic Field Inversion : Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction
Author
Xiaoman Li
Publisher
Scholars' Press
Publication year
2025
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6209141501
ISBN 13
9786209141508
Item weight
459 grams
Dimensions
220x150x18 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

Item7 to 10 business days5 to 7 business days
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