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

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

Xiaolun Chen, Xiaowen Luo, Ziyin Wu, Xiaoming Qin, Jihong Shang, Huajun Xu, Bin Li, Mingwei Wang, Hongyang Wan. A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6 500 m[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2203-9
Citation: Xiaolun Chen, Xiaowen Luo, Ziyin Wu, Xiaoming Qin, Jihong Shang, Huajun Xu, Bin Li, Mingwei Wang, Hongyang Wan. A VGGNet-based correction for satellite altimetry-derived gravity anomalies to improve the accuracy of bathymetry to depths of 6 500 m[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2203-9

doi: 10.1007/s13131-023-2203-9

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

Funds: The National Key R&D Program of China under contract No. 2022YFC3003800; the National Natural Science Foundation of China under contract No. 41830540; the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No. OR-SECCZ2022104; the Deep Blue Project of Shanghai Jiao Tong University under contract No. SL2020ZD204; Zhejiang Provincial Project under contract No. 330000210130313013006.
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  • Figure  1.  The research structure.

    Figure  2.  Architecture of the VGG-19 model. The boxes represent the size of each layer.

    Figure  3.  The calculation and processing flow of the filter method.

    Figure  4.  The training and validation loss from the experiments in the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).

    Figure  5.  Comparison of local details of gravity anomalies before and after correction from the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).

    Figure  6.  R2 values at different water depths compared with the bathymetry-only correction methos in the Southern Ocean (a), Pacific Ocean (b), Atlantic Ocean (c), and Caribbean Sea (d).

    Figure  7.  Percentage distributions of NRMSE (×10-3) performance in datasets from the Southern Ocean, Pacific Ocean, Atlantic Ocean, and Caribbean Sea.

    Table  1.   The default hyperparameters of the training model

    Content layer‘conv4_2’
    Style layers‘conv1_1’, ‘conv2_1’, ‘conv3_1’, ‘conv4_1’, ‘conv5_1’
    Weight of loss at content layer1
    Weights of loss at style layers1, 1, 1, 1
    Weights among content, style, and total variation loss1×10-4, 1, 1×10-5
    Learning ratestarts at 10 and linear decays over 100 iterations to 1
    下载: 导出CSV

    Table  2.   The parameters of the datasets

    LocationCenter point coordinateSpatial resolution/mData sizeArea/km2Bathymetry depth range/m
    Southern Ocean71°S, 173°E935 097 10443 700−4077−211
    Pacific Ocean9°S, 140°W9333 048 000283 337−4992−113
    Atlantic Ocean32°N, 65°W933 240 00027 778−4920−58
    Caribbean Sea18°N, 82°W12310 614 363150 310−6580−1
    下载: 导出CSV

    Table  3.   Overall accuracy of the gravity correction

    Southern Ocean0.90218.33313.6300.113
    Pacific Ocean0.95517.89210.0500.118
    Atlantic Ocean0.93019.56721.5490.114
    Caribbean Sea0.91921.05116.4850.113
    下载: 导出CSV

    Table  4.   The overall accuracy of the bathymetry correction

    Southern Ocean0.822104.790107.0240.027
    Pacific Ocean0.834117.630126.3660.026
    Atlantic Ocean0.833124.847136.6220.028
    Caribbean Sea0.783139.583164.4750.025
    下载: 导出CSV

    Table  5.   Proportion of corrected errors from true values within 2% and 1% of the depth range

    LocationProportion of corrected errors/%
    1% of depth2% of depth
    Southern Ocean68.0575.69
    Pacific Ocean61.5377.52
    Atlantic Ocean57.5868.69
    Caribbean Sea51.0364.58
    下载: 导出CSV
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