Volume 43 Issue 1
Jan.  2024
Turn off MathJax
Article Contents
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, 2024, 43(1): 112-122. 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, 2024, 43(1): 112-122. 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

doi: 10.1007/s13131-023-2203-9
Funds:  The National Key R&D Program of China under contract Nos 2022YFC3003800, 2020YFC1521700 and 2020YFC1521705; 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; the Special Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research Institutes of Public Interest of China under contract No. SZ2102; the Zhejiang Provincial Project under contract No. 330000210130313013006.
More Information
  • Corresponding author: E-mail: cdslxw@163.comzywu@vip.163.com
  • Received Date: 2022-12-28
  • Accepted Date: 2023-04-10
  • Available Online: 2023-07-11
  • Publish Date: 2024-01-01
  • 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.
  • loading
  • Airy G B. 1855. III. On the computation of the effect of the attraction of mountain-masses, as disturbing the apparent astronomical latitude of stations in geodetic surveys. Philosophical Transactions of the Royal Society of London, 145: 101–104, doi: 10.1098/rstl.1855.0003
    An Dechao, Guo Jinyun, Li Zhen, et al. 2022. Improved gravity-geologic method reliably removing the long-wavelength gravity effect of regional seafloor topography: a case of bathymetric prediction in the South China Sea. IEEE Transactions on Geoscience and Remote Sensing, 60: 4211912, doi: 10.1109/TGRS.2022.3223047
    Annan R F, Wan Xiaoyun. 2022. Recovering bathymetry of the gulf of guinea using altimetry-derived gravity field products combined via convolutional neural network. Surveys in Geophysics, 43(5): 1541–1561, doi: 10.1007/s10712-022-09720-5
    Benedetti P, Ienco D, Gaetano R, et al. 2018. M3Fusion: a deep learning architecture for multiscale multimodal multitemporal satellite data fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12): 4939–4949, doi: 10.1109/JSTARS.2018.2876357
    Bonvalot S, Balmino G, Briais A, et al. 2012. World gravity map. commission for the geological map of the world. Paris, France: BGI–CGMW-CNES-IRD
    Braitenberg C, Wienecke S, Wang Yong. 2006. Basement structures from satellite-derived gravity field: South China Sea ridge. Journal of Geophysical Research: Solid Earth, 111(B5): B05407, doi: 10.1029/2005JB003938
    Chen Xiaolun, Luo Xiaowen, Wu Ziyin, et al. 2022. A VGGNet-based method for refined bathymetry from satellite altimetry to reduce errors. Remote Sensing, 14(23): 5939, doi: 10.3390/rs14235939
    Colbo K, Ross T, Brown C, et al. 2014. A review of oceanographic applications of water column data from multibeam echosounders. Estuarine, Coastal and Shelf Science, 145: 41–56,
    Coley K. 2022. A global ocean map is not an ambition, but a necessity to support the ocean decade. Marine Technology Society Journal, 56(3): 9–12, doi: 10.4031/MTSJ.56.3.3
    Fan Diao, Li Shanshan, Li Xinxing, et al. 2021. Seafloor topography estimation from gravity anomaly and vertical gravity gradient using nonlinear iterative least square method. Remote Sensing, 13(1): 64, doi: 10.3390/rs13010064
    Fan Diao, Li Shanshan, Meng Shuyu, et al. 2020. Applying iterative method to solving high-order terms of seafloor topography. Marine Geodesy, 43(1): 63–85, doi: 10.1080/01490419.2019.1670298
    Gatys L A, Ecker A S, Bethge M. 2016. A neural algorithm of artistic style. Journal of Vision, 16(12): 326, doi: 10.1167/16.12.326
    Gatys L A, Ecker A S, Bethge M, et al. 2017. Controlling perceptual factors in neural style transfer. In: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 3730–3738, doi: 10.1109/CVPR.2017.397
    Gong Zheng, Zhang Peizhen, Zheng Wenjun, et al. 2021. The effect of altimetry data in estimating the elastic thickness of the lithosphere in the western Pacific Ocean. Geodesy and Geodynamics, 12(5): 315–322, doi: 10.1016/j.geog.2021.07.001
    Hu Minzhang, Zhang Shengjun, Jin Taoyong, et al. 2020. A new generation of global bathymetry model BAT_WHU2020. Acta Geodaetica et Cartographica Sinica (in Chinese), 49(8): 939–954, doi: 10.11947/j.AGCS.2020.20190526
    Hughes Clarke J E. 2018. Multibeam echosounders. In: Micallef A, Krastel S, Savini A, eds. Submarine Geomorphology. Cham: Springer, 25–41,
    Huo Guanying, Wu Ziyin, Li Jiabiao. 2020. Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data. IEEE Access, 8: 47407–47418, doi: 10.1109/ACCESS.2020.2978880
    Ibrahim A, Hinze W J. 1972. Mapping buried bedrock topography with gravity. Groundwater, 10(3): 18–23, doi: 10.1111/j.1745-6584.1972.tb02921.x
    Islam M J, Xia Youya, Sattar J. 2020. Fast underwater image enhancement for improved visual perception. IEEE Robotics and Automation Letters, 5(2): 3227–3234, doi: 10.1109/LRA.2020.2974710
    Jena B, Kurian P J, Swain D, et al. 2012. Prediction of bathymetry from satellite altimeter based gravity in the Arabian Sea: mapping of two unnamed deep seamounts. International Journal of Applied Earth Observation and Geoinformation, 16: 1–4, doi: 10.1016/j.jag.2011.11.008
    Jia Yangqing, Shelhamer E, Donahue J, et al. 2014. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, USA: Association for Computing Machinery, 675–678,
    Koh Z W, Nimmo F, Lunine J I, et al. 2022. Assessing the detectability of Europa’s seafloor topography from Europa clipper’s gravity data. The Planetary Science Journal, 3(8): 197, doi: 10.3847/PSJ/ac82aa
    Krizhevsky A, Sutskever I, Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84–90, doi: 10.1145/3065386
    Li Jiabiao. 1999. Multibeam Survey Principles, Techniques and Methods (in Chinese). Beijing: China Ocean Press
    Moran N P. 2020. Machine learning model selection for predicting global bathymetry [dissertation]. New Orleans, USA: University of New Orleans
    NOAA National Centers for Environmental Information. 2004. Multibeam bathymetry database (MBBDB). NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/maps/bathymetry/[2022-6-14]
    NOAA National Centers for Environmental Information. 2015. Marine trackline geophysical database. NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/maps/geophysics/[2022-06-14]
    Oldenburg D W. 1974. The inversion and interpretation of gravity anomalies. Geophysics, 39(4): 526–536, doi: 10.1190/1.1440444
    Otter D W, Medina J R, Kalita J K. 2021. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 32(2): 604–624, doi: 10.1109/TNNLS.2020.2979670
    Ouyang Mingda, Sun Zhongmiao, Zhai Zhenhe. 2014. Predicting bathymetry in South China Sea using the gravity-geologic method. Chinese Journal of Geophysics (in Chinese), 57(9): 2756–2765, doi: 10.6038/cjg20140903
    Ouyang Mingda, Sun Zhongmiao, Zhai Zhenhe, et al. 2015. Bathymetry prediction based on the admittance theory of gravity anomalies. Acta Geodaetica et Cartographica Sinica (in Chinese), 44(10): 1092–1099, doi: 10.11947/j.AGCS.2015.20140427
    Parker R L. 1973. The rapid calculation of potential anomalies. Geophysical Journal International, 31(4): 447–455, doi: 10.1111/j.1365-246X.1973.tb06513.x
    Reddi S J, Kale S, Kumar S. 2018. On the convergence of Adam and beyond. In: Proceedings of the 6th International Conference on Learning Representations. Vancouver, Canada: OpenReview. net
    Sandwell D T, Goff J A, Gevorgian J, et al. 2022. Improved bathymetric prediction using geological information: SYNBATH. Earth and Space Science, 9(2): e2021EA002069, doi: 10.1029/2021EA002069
    Scharroo R, Visser P. 1998. Precise orbit determination and gravity field improvement for the ERS satellites. Journal of Geophysical Research: Oceans, 103(C4): 8113–8127, doi: 10.1029/97JC03179
    Schulz M A, Yeo B T T, Vogelstein J T, et al. 2020. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nature Communications, 11(1): 4238, doi: 10.1038/s41467-020-18037-z
    Seoane L, Ramillien G, Beirens B, et al. 2022. Regional seafloor topography by extended Kalman filtering of marine gravity data without ship-track information. Remote Sensing, 14(1): 169, doi: 10.3390/rs14010169
    Shi Bo, Lu Xiushan, Yang Fanlin, et al. 2017. Shipborne over- and under-water integrated mobile mapping system and its seamless integration of point clouds. Marine Geodesy, 40(2/3): 104–122,
    Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations. San Diego, USA: Association for Computing Machinery
    Smith W H F, Sandwell D T. 1994. Bathymetric prediction from dense satellite altimetry and sparse shipboard bathymetry. Journal of Geophysical Research: Solid Earth, 99(B11): 21803–21824, doi: 10.1029/94JB00988
    Smith W H F, Sandwell D T. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277(5334): 1956–1962, doi: 10.1126/science.277.5334.1956
    Walcott R I. 1970. Flexural rigidity, thickness, and viscosity of the lithosphere. Journal of Geophysical Research, 75(20): 3941–3954, doi: 10.1029/JB075i020p03941
    Watts A B. 1978. An analysis of isostasy in the world’s oceans 1. Hawaiian-Emperor Seamount Chain. Journal of Geophysical Research: Solid Earth, 83(B12): 5989–6004, doi: 10.1029/JB083iB12p05989
    Watts A B. 2001. Isostasy and Flexure of the Lithosphere. Cambridge, UK: Cambridge University Press
    Watts A B, Sandwell D T, Smith W H F, et al. 2006. Global gravity, bathymetry, and the distribution of submarine volcanism through space and time. Journal of Geophysical Research: Solid Earth, 111(B8): B08408, doi: 10.1029/2005JB004083
    Wei Zhijie, Guo Jinyun, Zhu Chengcheng, et al. 2021. Evaluating accuracy of HY-2A/GM-derived gravity data with the gravity-geologic method to predict bathymetry. Frontiers in Earth Science, 9: 636246, doi: 10.3389/feart.2021.636246
    Wu Meiyin, Chen Li. 2015. Image recognition based on deep learning. In: Proceedings of 2015 Chinese Automation Congress (CAC). Wuhan, China: IEEE, 542–546, doi: 10.1109/CAC.2015.7382560
    Wu Ziyin, Yang Fanlin, Tang Yong, et al. 2020. High-Resolution Seafloor Survey and Applications. Beijing: Science Press
    Yale M M, Sandwell D T, Herring A T. 1998. What are the limitations of satellite altimetry?. The Leading Edge, 17(1): 73–76, doi: 10.1190/1.1437832
    Yuan Qiangqiang, Shen Huanfeng, Li Tongwen, et al. 2020. Deep learning in environmental remote sensing: achievements and challenges. Remote Sensing of Environment, 241: 111716, doi: 10.1016/j.rse.2020.111716
    Zhao Jianhu, Ouyang Yongzhong, Wang Aixue. 2017. Status and development tendency for seafloor terrain measurement technology. Acta Geodaetica et Cartographica Sinica (in Chinese), 46(10): 1786–1794, doi: 10.11947/j.AGCS.2017.20170276
    Zhu Chengcheng, Guo Jinyun, Yuan Jiajia, et al. 2021. Refining altimeter-derived gravity anomaly model from shipborne gravity by multi-layer perceptron neural network: a case in the South China Sea. Remote Sensing, 13(4): 607, doi: 10.3390/rs13040607
    Zwally H J, Schutz B, Abdalati W, et al. 2002. ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. Journal of Geodynamics, 34(3/4): 405–445,
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(5)

    Article Metrics

    Article views (307) PDF downloads(26) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint