| 基于水文分析法的海底峡谷特征要素自动提取方法研究及应用 |
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| 引用本文： | 周庆杰,高珊,刘乐军,李西双,周航,徐元芹,栾坤祥.基于水文分析法的海底峡谷特征要素自动提取方法研究及应用[J].海洋技术,2020,39(1):25-31. |
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| 作者姓名： | 周庆杰 高珊 刘乐军 李西双 周航 徐元芹 栾坤祥 |
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| 作者单位： | 自然资源部第一海洋研究所海洋沉积与环境地质国家海洋局重点实验室,山东 青岛 266061;中国科学院海洋研究所中国科学院海洋地质与环境重点实验室,山东 青岛 266071;青岛海洋科学与技术试点国家实验室海洋地质过程与环境功能实验室,山东 青岛 266061;自然资源部第一海洋研究所海洋沉积与环境地质国家海洋局重点实验室,山东 青岛 266061;青岛海洋科学与技术试点国家实验室海洋地质过程与环境功能实验室,山东 青岛 266061;自然资源部第一海洋研究所海洋沉积与环境地质国家海洋局重点实验室,山东 青岛 266061;自然资源部第一海洋研究所海洋沉积与环境地质国家海洋局重点实验室,山东 青岛 266061;青岛海洋科学与技术试点国家实验室海洋矿产资源评价与探测技术功能实验室,山东 青岛 266071;中国冶金地质总局青岛地质勘查院,山东 青岛 266061 |
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| 基金项目： | 中国科学院重点实验室开放基金;全球变化与海气相互作用专项;国家自然科学基金 |
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| 摘 要： | 海底峡谷是陆源沉积物向深海运移的主要通道,也是陆架/陆坡区重要的地貌单元。随着多波束测深技术的发展,如何快速而准确地从海量数据中识别并提取海底峡谷的特征要素,是一个亟待解决的重要热点问题。文中根据海底峡谷谷底下切、谷壁高而陡等地形特征,基于水文分析法和坡度分析等原理,通过ArcGIS中的数据建模工具建立了一种从数字高程模型(DEM)数据快速识别和提取海底峡谷特征要素的方法。以南海北部陆坡神狐峡谷区为例进行算例分析,结果表明,该方法在快速了解海底峡谷的发育位置和特征要素等方面是可行的,并可以获得峡谷头尾部水深、轴线长度、峡谷范围等特征信息。为获得该方法适用于研究区的最优参数组,文中讨论分析了峡谷形态、重分类阈值及数据分辨率等影响峡谷识别的因素。结果分析表明,峡谷形态会在一定程度上影响识别结果的准确性,但不影响对峡谷的总体了解;零值汇流累积量重分类阈值和DEM数据的空间分辨率是影响峡谷识别结果准确度的两个重要因素,在神狐峡谷群区,空间分辨率200 m且重分类阈值0.4时,海底峡谷识别和特征要素提取效果最佳。
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| 关 键 词： | 海底峡谷 水文分析法 特征要素提取 数字高程模型 |
Study and Application of the Automatic Extraction Method of Submarine Canyon Characteristics Based on Hydrologic Analysis |
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| ZHOU Qing-jie,GAO Shan,LIU Le-jun,LI Xi-shuang,ZHOU Hang,XU Yuan-qin,LUAN Kun-xiang.Study and Application of the Automatic Extraction Method of Submarine Canyon Characteristics Based on Hydrologic Analysis[J].Ocean Technology,2020,39(1):25-31. |
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| Authors: | ZHOU Qing-jie GAO Shan LIU Le-jun LI Xi-shuang ZHOU Hang XU Yuan-qin LUAN Kun-xiang |
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| Institution: | (First Institute of Oceanography,Ministry of Natural Resources,Key Laboratory of Marine Sedimentology and Environmental Geology,Qingdao 266061,Shandong Province,China;Institute of Oceanology,Chinese Academy of Sciences,Key Laboratory of Marine Geology and Environment,Qingdao 266071,ShandongProvince,China;Pilot National Laboratory for Marine Science and Technology,Function Laboratory for Marine Geology,Qingdao 266061,ShandongProvince,China;Pilot National Laboratory for Marine Science and Technology,Laboratory for Marine Mineral Resources,Qingdao 266071,ShandongProvince,China;Shandong Bureau of China Metallurgical Geology Bureau,Qingdao 266061,Shandong Province China) |
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| Abstract: | Submarine canyons are the main channel for the migration of continental sediments to the deep sea.Submarine canyon is also the important geomorphologic unit in the continental shelf/slope area.How to quickly identify and accurately extract the characteristic information of submarine canyons from massive data is a research hotspot,combined with the development of multi-beam sounding technology.In this paper,a method is established to quickly identify and extract the features of submarine canyons according to the topographic features of down-cutting canyon bottom and high-steep canyon wall from the digital elevation model(DEM)data through the data modeling tool in ArcGIS and based on the principles of hydrologic analysis and slope analysis.Taking the Shenhu canyon area on the Northern Slope of the South China Sea as an example,the results show that this method is feasible to quickly understand the development position and characteristic features of the submarine canyons,as well as the water depth of the canyon head and tail,axial length and canyon range.In order to obtain the optimal parameter group applicable to the research area,the factors affecting canyon recognition,such as canyon morphology,reclassification threshold and data resolution,are discussed and analyzed in detail.The results show that the canyon morphology can affect the accuracy of the recognition results to some extent,but it does not affect the overall understanding of the canyon.The reclassification threshold of zero-value confluence cumulant and the spatial resolution of DEM data are two important factors that affect the accuracy of canyon recognition results.In the Shenhu canyon group area,when the spatial resolution is 200 m and the reclassification threshold is 0.4,the submarine canyon recognition and feature element extraction have the optimal effect. |
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| Keywords: | Submarine canyon Hydrologic analysis Feature extraction Digital elevation model |
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