SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
Zhou, Shuai; Guo, Jinyun; Zhang, Huiying; Jia, Yongjun; Sun, Heping; Liu, Xin; An, Dechao;
SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data.
- Chinese Academy of Sciences China (People's Republic of)
- Sun Yat-sen University China (People's Republic of)
- Shandong University of Science and Technology China (People's Republic of)
- Ministry of Natural Resources Rwanda
Gravity anomaly, Global bathymetry, Multi-layer perceptron neural network, Mean dynamic topography, Vertical gravity gradient, Vertical deflection
Gravity anomaly, Global bathymetry, Multi-layer perceptron neural network, Mean dynamic topography, Vertical gravity gradient, Vertical deflection
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selected citations
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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