Bathymetry Inversion Using Vertical Deflections: a Case Study in South China Sea

Author(s):  
Xiaoyun Wan ◽  
Bo Liu ◽  
Xiaohong Sui ◽  
Richar Fiifi Annan ◽  
Yijun Min

Abstract As an alternative method, an algorithm for bathymetry inversion using vertical deflections is proposed. Firstly, the formulas for the bathymetry inversion from north and east components of vertical deflections are derived and the data processing is introduced. Then a local area in the South China Sea is selected as an example to experiment the method. The bathymetry inversion based on gravity anomaly was also conducted for a comparison. The results show that the bathymetry derived from the north component of the vertical deflections have almost the same accuracy as that derived from gravity anomalies and the results derived from the east component have the poorest accuracy. The experiment’s results also show that accuracy of the derived bathymetry can be improved if the fitting parameters are adjusted according to the water depths. In summary, among the gravity field products used in this study, although the gravity anomaly yielded the best performance in the bathymetry inversion, the vertical defections can still be used as supplements, especially in areas where accurate vertical deflections exist. This is because deriving gravity anomaly from altimetry observations needs additional data and calculation efforts.

2021 ◽  
Vol 13 (4) ◽  
pp. 607
Author(s):  
Chengcheng Zhu ◽  
Jinyun Guo ◽  
Jiajia Yuan ◽  
Xin Jin ◽  
Jinyao Gao ◽  
...  

Shipborne gravity can be used to refine altimeter-derived gravity whose accuracy is low in shallow waters and areas with complex submarine topography. As altimeter-derived gravity only within a small radius around the shipborne data can be corrected by traditional methods, a new method based on multi-layer perceptron (MLP) neural network is proposed to refine the altimeter-derived gravity. Input variables of MLP include the positional information at observation points and geophysical information (from our own South China Sea gravity anomaly model (SCSGA) V1.0 and bathymetry model ETOPO1) at grid points around observation points. Output variables of MLP are the refined residual gravity anomalies at observation points. Training shipborne data are classified into four cases to train four MLP models, which are used to predict the refined gravity anomaly model SCSGA V1.1. Then all of the training shipborne data are used for training an MLP model to predict the refined gravity anomaly model SCSGA V1.2. Assessed by testing shipborne data, the accuracy of SCSGA V1.2 is 0.14 mGal higher than that of SCSGA V1.0, and similar to that of SCSGA V1.1. Compared with the original gravity anomaly model (SCSGA V1.0), the accuracy of the refined gravity anomaly model (SCSGA V1.2) by MLP is improved by 4.4% in areas where the training data are concentrated, and also improved by 2.2% in other areas. Therefore, the method of MLP can be used to refine the altimeter-derived gravity model by shipborne gravity, overcoming the problem of limited correction radius for traditional methods.


2013 ◽  
Vol 32 (4) ◽  
pp. 41-48 ◽  
Author(s):  
Xiangtao ZHANG ◽  
Liang CHEN ◽  
Qinghua SHE ◽  
Sufang ZHANG ◽  
Peijun QIAO ◽  
...  

2010 ◽  
Author(s):  
Yuhong Xie ◽  
Jun Cai ◽  
Ling Xia Zhen ◽  
Hong Tian ◽  
Yan Hua Li ◽  
...  

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