Predicting bathymetry by applying multiple regression analysis in the Southwest Indian Ocean Region

doi:10.11947/j.AGCS.2020.20180526

Abstract

Abstract: Considering the fact that the sea floor topography and gravity anomaly or vertical gravity gradient anomaly show strong linear correlation in the corresponding frequency bands, the method based on using multivariate regression analysis technique to combine multi-gravity data to construct the seafloor model was proposed. Then, the inversion test and analysis were carried out in the part of SWIR(Southwest India Ridge) in the Southwest Indian Ocean. The results showed that the bathymetry model (BDVG model) based on multiple regression analysis has the highest accuracy compared with other models, which is 11.51% and 57.81% higher than the S&S V18.1 model and ETOPO1 model respectively. The accuracy of each bathymetry model is higher, and the relative error fluctuation is small where the water depth is above 2000 m, reflecting the good inversion effect in the deep sea area. In places where the seafloor is fluctuated drastically or in shallow seaarea, BDVG model has less variation in relative error and relative error fluctuation than BDG model and BVGG model established by gravity anomaly and vertical gravity gradient anomaly as a single input source, reflecting the BDVG model has better stability and the necessity and advantage of joint inversion. The only shaft-deficient rift oceanic ridge section (27 oceanic ridge section) on the Indomed FZ-Gallieni FZ is currently in the stage of sufficient magma supply, and the seafloor expansion has less influence on it. At the same time, due to the influence of the symmetric splitting, several rises are symmetrically distributed along the north and south of the axis.

Key words: multiple regression analysis, seafloor topography, gravity anomaly, vertical gravity gradient, coherence

CLC Number:

P229

FAN Diao, LI Shanshan, YANG Junjun, MENG Shuyu, XING Zhibin, ZHANG Chi, FENG Jinkai. Predicting bathymetry by applying multiple regression analysis in the Southwest Indian Ocean Region[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2): 147-161.

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