测绘学报 ›› 2023, Vol. 52 ›› Issue (12): 2039-2053.doi: 10.11947/j.AGCS.2023.20220644

• 大地测量学与导航 • 上一篇    下一篇

基于最小二乘配置的陌生海域海底地形反演方法

范雕1, 李姗姗1, 冯进凯1, 黄炎2, 范昊鹏1, 张金辉1, 李新星1   

  1. 1. 信息工程大学, 河南 郑州 450001;
    2. 军事科学院国防科技创新研究院, 北京 100071
  • 收稿日期:2022-11-27 修回日期:2023-08-15 发布日期:2024-01-03
  • 通讯作者: 李新星 E-mail:minibad@126.com
  • 作者简介:范雕(1991-),男,博士,讲师,研究方向为物理大地测量和空间化海洋测绘工程。E-mail:fandiao2311@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金(42204009;42174007;42174008)

Applying least square collocation method to predict seafloor topography in the unknown sea area

FAN Diao1, LI Shanshan1, FENG Jinkai1, HUANG Yan2, FAN Haopeng1, ZHANG Jinhui1, LI Xinxing1   

  1. 1. Information Engineering University, Zhengzhou 450001, China;
    2. National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
  • Received:2022-11-27 Revised:2023-08-15 Published:2024-01-03
  • Supported by:
    The National Natural Science Foundation of China (Nos. 42204009;42174007;42174008)

摘要: 针对目前在无船测水深数据支撑的陌生海域海底地形模型构建困难的现状,提出了可将船测水深数据和重力数据已知海区协方差函数统计结果迁移、推广并应用于与已知数据海区重力异常相似的船测水深数据空白的陌生海区,运用最小二乘配置方法实现陌生海域海底地形模型构建。首先在西太平洋某1°×1°数据已知海区,依据最小二乘配置方法构建了相应的海底地形模型(BAT_LSC_1)。模型评估结果表明,BAT_LSC_1模型与导纳函数构建的海底地形模型检核精度相当,检核相对精度优于4%。海区重力异常作灰度化处理后,应用影像粗匹配和精匹配技术搜寻出与已知海区重力异常特征相似区域,迁移已知海区协方差统计结果应用于陌生海区,运用最小二乘配置方法构建陌生海区海底地形模型(BAT_LSC_2)。试验检核结果表明,BAT_LSC_2整体表现优于ETOPO1模型和应用导纳函数构建的海底地形模型,验证了本文方法思路的可行性和适用性。

关键词: 海底地形, 卫星测高, 重力异常, 最小二乘配置, 协方差函数

Abstract: The least square collocation (LSC) method is introduced to address the problem in seafloor topography (ST) prediction without shipborne bathymetry data. That is, cross covariance function between shipborne bathymetry and gravity in the known sea area can be applied to the unknown sea area with no shipborne bathymetry data if the gravity anomaly in the two areas is similar. The ST model (BAT_LSC_1) is constructed based on LSC in the western Pacific Ocean where shipborne bathymetry and gravity anomaly are known. The evaluation results showed that the checking accuracy of BAT_LSC_1 is equivalent to that of the ST model constructed by admittance function, and the checking relative accuracy is better than 4%. Then, sea surface gravity anomaly in the sea area is grayed out and the unknown area where shipborne bathymetry is missing is identified by gravity anomaly image coarse matching and fine matching. The cross-covariance function in the known area is applied to the unknown area, and the LSC is also used to predict the ST model (BAT_LSC_2). The results show that BAT_LSC_2 is better than ETOPO1 model and the ST model constructed by admittance function, which verifies the feasibility and applicability of the proposed method.

Key words: seafloor topography, satellite altimetry, gravity anomaly, least square collocation, covariance function

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