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3 June 2024 Bathymetry prediction in Mariana Trench seabed terrain based on altimetry gravity anomalies and vertical gravity gradients
Chunzhu Yuan, Xiaohong Sui, Running Zhang, Sihan Shi, Hongjie Zhang
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Proceedings Volume 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024); 131700F (2024) https://doi.org/10.1117/12.3032272
Event: Third International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 2024, Wuhan, China
Abstract
Bathymetry data is an essential element in marine survey and mapping, and plays important roles in researching the Earth and guiding the underwater works. Satellite altimetry is one of the most effective ways to obtain global ocean gravity anomalies and vertical gravity gradients. In this study, the transfer function between gravity anomalies and gravity gradients and seabed topography is derived in spectral domain. Based on the error propagation analysis between altimetry data and gravity filed products, a bathymetry prediction method using different data fusion according to different water depths is proposed. A simulation is carried out in the Mariana Trench Seabed Terrain to verify the proposed method. The results show that vertical gravity gradients data does perform better in shallow water areas than gravity anomaly data does. And when the vertical gravity gradient data is used in the area with water depth shallower than 1000m, and the gravity anomaly data is used in the area with water depth deeper than 1000m, the predicted bathymetry has higher accuracy than that derived from gravity anomalies alone or vertical gravity gradients alone, which improves the RMS 11.35% compared to just using anomalies alone.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chunzhu Yuan, Xiaohong Sui, Running Zhang, Sihan Shi, and Hongjie Zhang "Bathymetry prediction in Mariana Trench seabed terrain based on altimetry gravity anomalies and vertical gravity gradients", Proc. SPIE 13170, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024), 131700F (3 June 2024); https://doi.org/10.1117/12.3032272
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KEYWORDS
Data fusion
Satellites
Data modeling
Water
Spatial resolution
Data processing
Error analysis
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