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

Xiaoman Li

ISBN 10: 6209141501 / ISBN 13: 9786209141508

Published by Scholars' Press, 2025

About this Item

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

Bibliographic Details

• Title: Deep Learning-Driven Vector Acoustic Field Inversion | Intelligent Estimation of Shallow-Water Sediment Parameters and Normal-Mode Dispersion Curve Prediction
• Publisher: Scholars' Press
• Publication Date: 2025
• Language: English
• ISBN 10: 6209141501
• ISBN 13: 9786209141508
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 18D millimeters 220 millimeters width by 150 millimeters height by 18 millimeters depth
• Weight: 459 grams
• Seller catalogs:
  • Bücher

About this title

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.