Deep Learning-Driven Vector Acoustic Field Inversion: Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction - Softcover
Synopsis
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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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209141508
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Deep Learning-Driven Vector Acoustic Field Inversion
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Scholars' Press Okt 2025, 2025
ISBN 10: 6209141501
ISBN 13: 9786209141508
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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786209141508
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Deep Learning-Driven Vector Acoustic Field Inversion | Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction
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Taschenbuch. Condition: Neu. Deep Learning-Driven Vector Acoustic Field Inversion | Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction | Xiaoman Li | Taschenbuch | Englisch | 2025 | Scholars' Press | EAN 9786209141508 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 134249988
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Deep Learning-Driven Vector Acoustic Field Inversion
Published by
Scholars' Press Okt 2025, 2025
ISBN 10: 6209141501
ISBN 13: 9786209141508
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 296 pp. Englisch. Seller Inventory # 9786209141508
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Deep Learning-Driven Vector Acoustic Field Inversion : Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction
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