Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

Abstract

The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.

Keywords:

• Bathymetric model
• CNNGWD method
• marine gravity anomaly
• satellite altimetry
• vertical gravity gradient

Acknowledgments

The authors are thankful to the National Geophysical Data Centre (https://www.ngdc.noaa.gov/mgg/global/global.html) for providing the depth data of the shipborne measurements (Most of the data in the region were found to be fused into the ETOPO1 model, with the exception of JR366, RR1204, RR1205, RR1104 to RR1112. All the shipborne sounding data are applied to construct the GEBCO_21 model.) and the ETOPO1 bathymetric model, and thankful to the Scripps Institution of Oceanography website (https://topex.ucsd.edu/marine_grav/mar_grav.html) for providing the V29.1 gravity anomaly and vertical gravity gradient data. We are grateful the British Oceanographic Data Centre (https://www.gebco.net/) for the GEBCO_2021 bathymetric model. We sincerely appreciate the suggestions of the reviewers and editors for their great help in improving the quality of this article. Yongjin Sun, Wei Zheng and Zhaowei Li contributed equally to this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (under grants 42274119), in part by the Liaoning Revitalization Talents Program (under grants XLYC2002082), in part by National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration (2022YFF1400500), and in part by the Key Project of Science and Technology Commission of the Central Military Commission.