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\begin{document}

\title{An iterative algorithm for predicting seafloor topography from gravity
anomalies}



\author[1]{Jinhai Yu}%
\author[1]{Bang An}%
\author[1]{huan Xu}%
\author[2]{Zhongmiao Sun}%
\author[1]{Yuwei Tian}%
\author[1]{Qiuyu Wang}%
\affil[1]{University of Chinese Academy of Sciences}%
\affil[2]{Xi'an Research Institute of Surveying and Mapping}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
As high-resolution global coverage cannot easily be achieved by direct
bathymetry, the use of gravity data is an alternative method to predict
seafloor topography. Currently, the commonly used algorithms for
predicting seafloor topography are mainly based on the approximate
linear relationship between topography and gravity anomaly. In actual
application, it is also necessary to process the corresponding data
according to some empirical methods, which can cause uncertainty in
predicting topography. In this paper, we established analytical
observation equations between the gravity anomaly and topography, and
obtained the corresponding iterative solving method based on the least
square method after linearizing the equations. Furthermore, the
regularization method and piecewise bilinear interpolation function are
introduced into the observation equations to effectively suppress the
high-frequency effect of the boundary sea region and the low-frequency
effect of the far sea region. Finally, the seafloor topography beneath a
sea region (117.25\selectlanguage{ngerman}°-118.25° E, 13.85°-14.85° N) in the South China Sea
is predicted as an actual application, where gravity anomaly data of the
study area with a resolution of 1\selectlanguage{english}'x1\selectlanguage{english}' is from the DTU17 model. Comparing
the prediction results with the data of ship soundings from the National
Geophysical Data Center (NGDC), the root-mean-square (RMS) error and
relative error can be up to 127.4 m and approximately 3.4\%,
respectively.%
\end{abstract}\selectlanguage{ngerman}%



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