综合垂直重力异常梯度和海底地形模型的海山自动探测方法

doi:10.11947/j.AGCS.2026.20250319

测绘学报 ›› 2026, Vol. 55 ›› Issue (4): 647-657.doi: 10.11947/j.AGCS.2026.20250319

• 海岸带与海洋测绘遥感 • 上一篇

综合垂直重力异常梯度和海底地形模型的海山自动探测方法

高屹¹(), 刘新¹(), 于道成², 押少帅¹, 边少锋³, 孙和平⁴, 郭金运¹

^1.山东科技大学测绘与空间信息学院，山东　青岛　266590
^2.辽宁工程技术大学测绘与地理科学学院，辽宁　阜新　123000
^3.中国地质大学地理与信息工程学院，湖北　武汉　430074
^4.中国科学院精密测量科学与技术创新研究院精密大地测量与定位全国重点实验室，湖北　武汉　430077

收稿日期:2025-08-11 修回日期:2026-03-12 发布日期:2026-05-11
通讯作者: 刘新 E-mail:2250291272@qq.com;xinliu1969@126.com
作者简介:高屹（2001—），男，硕士生，主要从事卫星测高数据处理和应用。　E-mail：2250291272@qq.com
基金资助:
国家自然科学基金(42430101; 42274006; 42192535)

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

Yi GAO¹(), Xin LIU¹(), Daocheng YU², Shaoshuai YA¹, Shaofeng BIAN³, Heping SUN⁴, Jinyun GUO¹

^1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
^2.School of Geomatics, Liaoning Technical University, Fuxin 123000, China
^3.School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
^4.State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China

Received:2025-08-11 Revised:2026-03-12 Published:2026-05-11
Contact: Xin LIU E-mail:2250291272@qq.com;xinliu1969@126.com
About author:GAO Yi (2001—), male, postgraduate, majors in satellite altimetry data processing and application. E-mail: 2250291272@qq.com
Supported by:
The National Natural Science Foundation of China(42430101; 42274006; 42192535)

摘要/Abstract

摘要：

精确探测全球海山对认识地球科学具有重要意义。传统船载测深方法成本较高、覆盖范围有限，而卫星测高技术为全球尺度的海山探测提供了有效手段。本文提出了一种综合垂直重力异常梯度（VGGA）和海底地形模型的海山自动探测方法。首先，对垂直重力异常梯度数据进行高斯滤波预处理，并搜索局部极大值作为潜在海山中心。然后，生成一系列闭合的VGGA等值线，并逐一对每个等值线应用面积（>50 km²）、VGGA振幅（>12 E）、形状拟合精度（圆形或椭圆形拟合误差分别小于10%、15%，Jaccard相似系数>0.6）和空间位置（排除大陆坡及其20 km缓冲区）等多项约束条件进行严格筛选。在地形复杂的区域，引入标记控制的分水岭算法对VGGA信号进行分割，对分割后的每个子区域单独进行上述筛选。最后，对于通过VGGA数据筛选的候选海山区域，通过对海底地形数据剖面的一阶导数和二阶导数的分析，在GEBCO_2024数据中自动识别海山坡脚点以确定基底深度，进而精确估算海山高度。将本文方法应用于中国南海及其周边海域，共探测出高度超过100 m的海山278座，其中与SIO 2023年全球海山目录空间匹配的识别率为98%。此外，本文探测出109座未被现有目录记录的潜在海山。通过与船测数据的对比，本文方法估算高度的均方根误差为354.4 m，验证了本文方法的有效性和可靠性。本文方法可为完善全球海山数据库及相关海洋研究提供有效技术支持。

关键词: 海山, 垂直重力异常梯度, SWOT测高卫星, 海底地形, 中国南海

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

中图分类号:

P229

高屹, 刘新, 于道成, 押少帅, 边少锋, 孙和平, 郭金运. 综合垂直重力异常梯度和海底地形模型的海山自动探测方法[J]. 测绘学报, 2026, 55(4): 647-657.

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

综合垂直重力异常梯度和海底地形模型的海山自动探测方法

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

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