Submarine canyons are the major channels for the migration of terrigenous sediments towards deep seas and an important geomorphological unit in continental shelves/slopes. With the development of multibeam bathymetry technology, a method of quickly identifying and accurately extracting the characteristics of submarine canyons from large datasets is urgently required. In this paper, under manual supervision, based on the principles of hydrological analysis and slope analysis, we established a method to quickly identify and extract the characteristic elements of submarine canyons from digital elevation model (DEM) data via the data modeling tools in ArcGIS, according to the topographic features such as U and V shaped downcutting at the bottom of the submarine canyon and high-relief steep canyon walls. The analytical results of the Shenhu Canyons in the northern slope of the South China Sea show that this method can rapidly determine the development position and characteristic elements of submarine canyons and obtain the canyon head and mouth water depth, canyon thalweg length and canyon range (area of canyon floor). To obtain the optimal parameter set applicable to the study area, the factors affecting canyon identification, such as the canyon morphology, reclassification threshold and data resolution, are discussed and analyzed. The results show that the canyon morphology has little influence on the accuracy of recognition results for straight, parallel or sub-parallel canyons. The reclassification threshold of the zero flow accumulation and the spatial resolution of the DEM data determine the accuracy of canyon thalweg recognition, which are two important factors affecting the accuracy of canyon identification results. For the Shenhu Canyons area, a spatial resolution of 200 m and reclassification threshold of 0.4 are the optimal parameters for identifying and extracting features from submarine canyons, and the identification results of automatic and manual extraction of canyon parameters show that the heads water depth differences all are less than 210 m and the thalwegs length difference of the canyon are within 7 km.

海底峡谷是陆源沉积物向深海迁移的主要渠道，也是大陆架/斜坡的重要地貌单元。随着多波束测深技术的发展，迫切需要一种从大型数据集中快速识别和准确提取海底峡谷特征的方法。本文基于水文分析和边坡分析的原理，在人工监督下，建立了一种通过ArcGIS中的数据建模工具从数字高程模型（DEM）数据中快速识别和提取海底峡谷特征元素的方法，根据地形特征，例如海底峡谷和高浮雕陡峭峡谷壁底部的U型和V型下陷。对南海北坡神湖峡谷的分析结果表明，该方法可以快速确定海底峡谷的发育位置和特征要素，并获得峡谷的头，口水深，峡谷海藻长度和峡谷范围（面积）。峡谷楼）。为了获得适用于研究区域的最佳参数集，讨论并分析了影响峡谷识别的因素，例如峡谷形态，重分类阈值和数据分辨率。结果表明，峡谷形态对直，平行或次平行峡谷的识别结果的准确性影响很小。零流量累积的重新分类阈值和DEM数据的空间分辨率决定了峡谷thalweg识别的准确性，这是影响峡谷识别结果准确性的两个重要因素。对于神湖大峡谷地区，200 m的空间分辨率和0.4的重分类阈值是识别和提取海底峡谷特征的最佳参数，自动和手动提取峡谷参数的识别结果表明，水头水深差均小于210 m，峡谷的thalwegs长度差在7 km以内。