船载回声测深数据误差识别方法研究——以上海石化海床为例

发布时间：2018-05-09 14:15

本文选题：船载回声测深数据 + 聚类分析　；参考：《泥沙研究》2017年06期

【摘要】：基于水下地形自然演变的渐变性和连贯性特征,通过统计学聚类分析方法和地统计分析方法构建水下地形Voronoi地形图,可以有效地识别和剔除船载回声测深数据中的"离散"粗差数据;可视化显示的时空Voronoi高程差分布图可以有效地识别误差高程大于冲淤强度的"条带状"误差数据,对"条带状"误差数据识别的灵敏度受地形坡度、冲淤强度和测点密度的影响。虽然时空Voronoi高程差分布图对冲淤强度较大和坡度较大区域的"条带状"误差数据灵敏度有限,但在地形平缓、冲淤强度较弱且测点密度较大的区域,识别效果较好,可以作为船载回声测深数据预处理的有效方法。
[Abstract]:Based on the gradual change and coherence of the natural evolution of underwater topography, the Voronoi topographic map of underwater terrain is constructed by statistical clustering analysis and geostatistical analysis. It can effectively identify and eliminate the "discrete" gross error data from the echo sounding data on board the ship, and visualize the spatial and temporal Voronoi height difference distribution map, which can effectively identify the "strip" error data whose error elevation is greater than that of scour and silt intensity. The sensitivity of the "strip" error data identification is affected by the terrain slope, scour and silt intensity and the density of the measuring point. Although the sensitivity of the "strip" error data in the area with larger silt intensity and slope is limited by the spatial and temporal Voronoi height difference distribution map, the recognition effect is better in the areas where the topography is smooth, the scouring and silting intensity is weak and the density of measuring points is high. It can be used as an effective method to preprocess the echo sounding data.
【作者单位】：华东师范大学地理信息科学教育部重点实验室;华东师范大学地理科学学院;
【基金】：国家自然科学基金项目(41671095,51761135024);国家自然科学基金人才培养项目(J1310028)
【分类号】：P229

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