An automated seamount detection method integrating vertical gravity gradient anomaly and seafloor topographic models

doi:10.11947/j.AGCS.2026.20250319

Abstract

Abstract:

The accurate, global-scale detection of seamounts is of significant importance to the Earth sciences. While traditional shipborne bathymetry methods are costly and have limited coverage, satellite altimetry provides an effective means for identifying seamounts on a global scale. This study proposes an automated seamount detection method that integrates vertical gravity gradient anomalies (VGGAs) and bathymetric models. The method begins by preprocessing the VGGA data with a Gaussian filter to identify local maxima, which are treated as potential seamount centers. Subsequently, closed VGGA contours generated around these centers are subjected to a rigorous screening process based on multiple constraints: area (>50 km²), VGGA amplitude (>12 E), shape fidelity (circular/elliptical fitting errors <10%/15% and a Jaccard index >0.6), and spatial location (excluding continental slopes and their 20 km buffer zones). For regions with complex topography, a marker-controlled watershed algorithm is employed to segment the VGGA signal, after which each subregion is screened independently using the same criteria. Finally, for the candidate regions that pass the screening, the seamount's basal depth is determined by automatically identifying the foot of the slope within the GEBCO_2024 data. This identification is achieved by analyzing the first and second derivatives of bathymetric profiles, enabling an accurate estimation of the seamount's height. Applied to the South China Sea and its surrounding regions, the method identified 278 seamounts taller than 100 m, achieving a 98% spatial match with the SIO 2023 global seamount catalog. Furthermore, our study identified 109potential, previously uncatalogued seamounts. Comparisons with ship-based data demonstrated a root mean square error (RMSE) of 354.4 m in height estimates, validating the method's effectiveness and reliability. This method offers robust technical support for enhancing the global seamount database and advancing related marine research.

Key words: seamount, vertical gravity gradient anomaly, SWOT altimetry satellite, topography seafloor, South China Sea

CLC Number:

P229

Yi GAO, Xin LIU, Daocheng YU, Shaoshuai YA, Shaofeng BIAN, Heping SUN, Jinyun GUO. An automated seamount detection method integrating vertical gravity gradient anomaly and seafloor topographic models[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(4): 647-657.

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海山高度/m	数量
小型海山（100<h<1000）	139
中型海山（1000<h<3000）	126
大型海山（h>3000）	13

算法	MAX	MIN	平均值	STD	RMSE
ISDHEB	668.5	－912	－155.6	318.4	354.4
SIO	1260	－1467	2.13	684.6	684.6