.png){kind=icon}
.png){kind=icon}
Return
Iterative Least-Squares Collocation Bathymetry Inversion With Optimized Gravity Covariance
.png){kind=icon}
OA
AI
DOI：10.1109/jstars.2026.3712609.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Bathymetry inversion using least-squares collocation (LSC) from satellite altimetry gravity data often relies on empirical covariance parameters or prior topography information, which is physically inconsistent with actual inversion conditions. This article presents an iterative LSC method for bathymetry estimation that strictly uses only observed gravity anomalies to construct the covariance model. We determine the optimal parameters A and B of the Tscherning/Rapp gravity anomaly covariance function directly from the gravity field model, rather than using empirical values. The topography covariance is derived via covariance propagation, and its conversion coefficients from gravity covariance are iteratively optimized for improved consistency and numerical stability. Compared with conventional linear regression, the proposed LSC framework further considers nonlinear terms of bathymetry. In addition to conventional shipborne check points, the residual terrain model is introduced as an independent supplementary validation to assess geophysical consistency. Experiments in the South China Sea show that the method provides physically reasonable and consistent bathymetry results with good coherence with existing models.
Keywords:
Bathymetry
covariance propagation
gravity anomaly
least-squares collocation (LSC)
residual terrain model (RTM)
Journal
IF:
5.3
Papers:
1.3K
Citations:
3.0W