首页|A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6 500 m

A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6 500 m

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Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R2 values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.

gravity anomalybathymetry inversionVGGNetmultibeam sonarsatellite altimetry

Xiaolun Chen、Xiaowen Luo、Ziyin Wu、Xiaoming Qin、Jihong Shang、Huajun Xu、Bin Li、Mingwei Wang、Hongyang Wan

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Key Laboratory of Submarine Geosciences,Second Institute of Oceanography,Ministry of Natural Resources,Hangzhou 310012,China

Ocean College,Zhejiang University,Zhoushan 316021,China

Key Laboratory of Ocean Space Resources Management Technology,Marine Academy of Zhejiang,Hangzhou 310012,China

School of Oceanography,Shanghai Jiao Tong University,Shanghai 200240,China

School of Civil Engineering and Architectures,Zhejiang University of Science and Technology,Hangzhou 310023,China

National Center for Archaeology,Beijing 100013,China

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National Key R&D Program of ChinaNational Key R&D Program of ChinaNational Key R&D Program of ChinaNational Natural Science Foundation of ChinaOpen Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural ResourcesDeep Blue Project of Shanghai Jiao Tong UniversitySpecial Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research InstitZhejiang Provincial Project

2022YFC30038002020YFC15217002020YFC152170541830540OR-SECCZ2022104SL2020ZD204SZ2102330000210130313013006

2024

10.1007/s13131-023-2203-9

海洋学报(英文版)
中国海洋学会

海洋学报(英文版)

CSTPCD
影响因子：0.323
ISSN：0253-505X
年,卷(期)：2024.43(1)
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