Journal of Jilin University(Earth Science Edition) ›› 2023, Vol. 53 ›› Issue (6): 2029-2044.doi: 10.13278/j.cnki.jjuese.20230285

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 Review on Research Progress of Recovering Bathymetry from Satellite Altimetry-Derived Data

Jiang Tao1, Jiang Xiao 1,2, Guo Jinyun2, Zhang Zhonghui3   

  1. 1. Chinese Academy of Surveying and Mapping, Beijing 100830, China
    2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
    3. The First Geodetic Surveying Brigade of MNR, Xi’an 710054, China
  • Online:2023-11-26 Published:2023-12-13
  • Supported by:
    the National Key R&D Program of China (2021YFB3900200,2021YFB3900203)

Abstract:  High-precision, high-resolution global seafloor topography models are critical foundational datasets for studying the Earth’s shape and interior structure. They are significant in multiple domains, including seafloor plate movement research, early tsunami monitoring, deep-sea resource exploration, underwater marine construction projects, ensuring maritime safety, and delineating marine territorial boundaries. The extensive and profound realms of the oceans present a formidable challenge for meticulously mapping the seafloor topography. However, satellite altimetry, with its global coverage, high precision, high resolution, and the ability to conduct regular observations of ocean dynamics, has emerged as the primary method for obtaining global bathymetric data. This paper provides an overview of the current state and recent developments in recovering bathymetry from satellite altimetry-derived data. It encompasses fundamental principles, major modes, and missions in satellite altimetry, marine gravity field inversion, methods for seafloor topography inversion, and seafloor topography model products. Additionally, the paper provides insights into the trends and prospects for constructing high-precision, dynamic global seafloor topography models.

Key words:  , satellite altimetry, marine gravity field, seafloor topography, admittance method, linear regression, gravity-geologic method

CLC Number: 

  • P228
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