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

doi:10.11947/j.AGCS.2023.20220644

Abstract

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

CLC Number:

P229

FAN Diao, LI Shanshan, FENG Jinkai, HUANG Yan, FAN Haopeng, ZHANG Jinhui, LI Xinxing. Applying least square collocation method to predict seafloor topography in the unknown sea area[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2039-2053.

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