Summary Predicting seafloor topography (ST) from altimetry-derived gravity data is an effective method for obtaining ST in sea areas with sparse bathymetry. Classical ST inversion methods primarily utilize gravity anomaly, whereas vertical deflection (VD) —a fundamental product of altimetry that exhibits greater sensitivity to high-frequency ST is infrequently employed. We propose an iterative method for optimization to predict ST using VD in the spatial domain, which addresses the major problem—high nonlinearity between VD and ST. It considers the Airy-isostatic compensation and removes the non-topographic components while preserving short-wavelength signals. Our method predicts the optimal ST by iteratively minimizing the squared 2-norm of the weighted residual vector between the forward-modelled and observed VD. A synthetic test conducted in a part of the South China Sea preliminarily validates the method’s effectiveness. A real-data experiment in the Arctic Ocean shows that the root-mean-square (RMS) of differences between the STVD model constructed using our method and checkpoints is 110. 43 m, representing improvements of 6. 45, 18. 85, and 13. 95 per cent over the topo₂7. 1, ETOPO1, and IBCAO V3, respectively. Accuracy verification in different depth ranges and profile analysis indicate that STVD exhibits significant advantages in shallow depth (≤2, 000 m), while it is relatively inferior in deep depth (2, 000 m). Radial power spectra reveal that STVD possesses higher energy at short wavelengths (less than ∼10 km), and its energy at intermediate-long wavelengths is consistent with the comparison models. The results demonstrate our method can effectively recover detailed ST in shallow areas and enhance the short-wavelength ST.
Pei et al. (Mon,) studied this question.
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