摘要
数字水深模型(Digital Bathymetric Models,简称“DBMs”),是近海工程建设、资源开发、环境保护等领域的重要基础地理信息数据。现有全球公开DBMs产品如GEBCO(The General Bathymetric Chart of the Oceans)、SRTM(The Shuttle Radar Topography Mission)、ETOPO(Earth Topography)等在不同海域的数据类型、数据来源和产品精度均存在差异。为利用全球测深数据和DBMs产品重建中国近海水深模型,本文提出一种基于水深分区的加权融合重建框架。首先,从5个维度(整体精度、不同水深、航线剖面、地理分区、局部细节)对比分析6种常用DBMs产品的可靠性和适用性;然后,顾及水深和地形特征对研究区进行分割和分区,并选取分区内最优DBMs产品,以最小误差为约束进行最优加权融合;最后,对融合结果进行实测值恢复、平滑滤波等后处理,形成中国海岸线周边近海海域15″分辨率高精度无缝水深模型。结果表明,融合结果相比SRTM30_PLUS、GEBCO_2022、SRTM15_V2.5.5和ETOPO_2022均方根误差降低了27%、14%、14%和13%,地形细节也得到保留,证明了该融合框架的可行性,可为多数据集大规模海底地形的融合重建和及时更新提供参考。
Digital bathymetric models(DBMs)are important basic geographic information data in the fields of offshore engineering construction,resource development,environmental protection and so on.The existing global public DBMs products such as GEBCO(The General Bathymetric Chart of the Oceans),SRTM(The Shuttle Radar Topography Mission)and ETOPO(Earth Topography)have different data types,data sources and product accuracy in different sea areas.In order to reconstruct China’s offshore bathymetric model using global bathymetric data and DBMs products,this paper proposed a weighted fusion reconstruction framework based on bathymetric partition.Firstly,the reliability and applicability of six commonly used DBMs products(GEBCO_2022,SRTM30_PLUS,SRTM15_V2.5.5,TOPO_25.1,DTU10,ETOPO_2022)were compared and analyzed in five dimensions(overall accuracy,different water depths,route profiles,geographical partitions,local details).Then,considering the bathymetric and topographic characteristics,the study area was segmented and partitioned,and the optimal DBMs products in the partition were selected,and the optimal weighted fusion was carried out with the minimum error as the constraint.Finally,the fusion results were processed by measured value recovery,smooth filtering and other post-processing to form a high-precision seamless bathymetric model with 15"resolution in offshore waters around China’s coastline.The results showed that the RMSE of the fusion results was reduced by 27%,14%,14%and 13%compared with SRTM30_PLUS,GEBCO_2022,SRTM15_V2.5.5 and ETOPO_2022,and the details of the topograhy were also retained.The feasibility of the fusion framework was proved,which could provide a reference for the fusion reconstruction and timely updating of large-scale seabed topography from multiple datasets.
作者
阮晓光
占赵杰
闫兆进
谈秋英
郭美静
杨阳
Ruan Xiaoguang;Zhan Zhaojie;Yan Zhaojin;Tan Qiuying;Guo Meijing;Yang Yang(College of Geomatics,Zhejiang University of Water Resources and Electric Power,Hangzhou 310018,China;Nanxun Innovation Institute,Zhejiang University of Water Resources and Electric Power,Hangzhou 310018,China;School of Resources and Geosciences,China University of Mining and Technology,Xuzhou 221116,China;Zhejiang Highway Technicians College,Hangzhou 311300,China;The Academy of Digital China,Fuzhou University,Fuzhou 350108,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2024年第7期16-28,共13页
基金
浙江省基础公益研究计划项目(LZJWY22E090002)
国家自然科学基金(42201451)
中国博士后科学基金面上项目(2022M723379)
南浔青年学者项目(RC2024021062)
浙江省社会科学界联合会研究课题(2024N085)
江苏省双创博士项目(JSSCBS20221523)。
关键词
数字水深模型
多源数据融合
水深
海底地形
中国海岸线
digital bathymetric model
multi-source data fusion
bathymetry
seabed topography
China’s coastline
