极地研究

极地研究 ›› 2026, Vol. 38 ›› Issue (1): 41-51.DOI: 10.13679/j.jdyj.20240077

所属学科：极地遥感

• 研究论文 • 上一篇    下一篇

基于卫星测高重力数据提升北极海底地形模型精度方法的研究

吴浠瑗, 范雕, 刘城涛, 谢东兴   

  1. 信息工程大学地理空间信息学院, 河南 郑州 450001
  • 收稿日期:2024-08-27 修回日期:2024-12-09 出版日期:2026-03-31 发布日期:2026-04-27
  • 通讯作者: 范雕
  • 基金资助:
    国家自然科学基金项目资助

Enhancing the accuracy of an Arctic seafloor topographic model based on satellite altimetry gravity data

WU Xiyuan, FAN Diao, LIU Chengtao, XIE Dongxing   

  1. College of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
  • Received:2024-08-27 Revised:2024-12-09 Online:2026-03-31 Published:2026-04-27

摘要：

提升北极海底地形模型精度, 对于开展北极航道研究具有重要意义。IBCAO V4.2模型因融合多源声呐数据而在北极相关研究领域中占据重要地位, 而联合水深数据和重力数据构建海底地形模型的方法相较于仅依靠水深数据构建模型具有明显的优势, 其中重力地质方法(gravity-geologic method, GGM)正是一种能有效联合水深数据和重力数据构建海底地形模型的方法。本文针对GGM在北极海域海底地形模型构建中地理网格的变形问题及其关键参数密度差异的求解难题, 提出了一种在极球面投影条件下, 利用回归分析解算密度差异常数, 进而使用GGM反演海底地形的途径和方法。该方法以格陵兰海东侧海域为试验区, 结合SDUST_GA_2022卫星数据及部分船载实测水深数据, 构建了覆盖面积约7.6×104 km2的海底地形模型, 称之为AO1-GGM模型。该模型利用船载实测水深数据进行外部验证, 与IBCAO V4.2模型及仅依靠船载实测水深数据构建的AO1-Grid模型进行对比, 结果表明: (1)在船载实测水深数据缺乏区域, AO1-GGM模型可借助极区海洋重力异常信息刻画地形细节, 其表现优于AO1-Grid模型; (2)对比IBCAO V4.2模型, AO1-GGM与实际情况贴合度更高, 在均方误差、均方根误差等评估指标上检核结果更优, 精度提升约35.01%, 且检核误差离散程度更小。综上可知, 通过融合多源声呐和卫星测高重力数据, 本研究可显著提升北极海底地形模型的精度。

关键词: 卫星测高, 重力异常, 海底地形, 极球面投影, 模型精度, 北极

Abstract: Abstract Improving the accuracy of Arctic seafloor topographic model is of great significance for the research of Arctic shipping lanes, and the IBCAO V4.2 model occupies an important position in Arctic related research fields with its fusion of multi-source sonar data, and the construction of seafloor topographic model by combining bathymetry data and gravity data has obvious advantages in relying on the construction of the model by relying on the bathymetry data, and the Gravity- Geologic Method (GGM) has a significant advantage in this regard. Geologic Method (GGM) can effectively combine bathymetric and gravity data to construct seafloor topographic models. Aiming at the deformation problem of geographic grid and the key parameter density difference in the construction of seafloor topographic model in the Arctic sea by GGM, this paper proposes a way and method to solve the density difference constant by regression analysis under the polar spherical projection condition, and then invert the seafloor topography by GGM. The method takes the eastern part of the Greenland Sea as the test area, and combines the SDUST_GA_2022 satellite data and part of the shipborne measured bathymetry data to construct a seafloor topographic model covering about 7.6×104km2, which is called the AO1-GGM model. The model was externally validated using shipboard measured bathymetry data, and compared with the IBCAO V4.2 model and the AO1-GGM model constructed by relying only on shipboard measured bathymetry data. The results of the study show that 1) in the region where shipboard measured bathymetry data are lacking, the AO1-GGM model can draw topographic details with the help of ocean gravity anomalies in the polar region, and its performance is better than that of the AO1-Grid model. 2) Compared with the IBCAO V4.2 model, the AO1-GGM is more compatible with the actual situation, and it has better results in the checking of the assessment indexes, such as the MSE, RMSE, etc., with an improvement of the accuracy of about 35.01%, and the degree of dispersion of the checking error is smaller. The above results also show that the accuracy of the Arctic seafloor topography model can be significantly improved by integrating multi-source sonar and satellite altimetry and gravity data.

Key words: satellite altimetry, gravity anomaly, seafloor topography, polar spherical projection, model accuracy, Arctic