基于区域平均垂直不确定度的自适应网格水深建模方法

提出一种基于区域平均垂直不确定度的自适应网格数字水深模型构建方法。利用水深不确定度及其传递机制,计算建模区域的水深平均不确定度,自动确定网格剖分层次和组织水深数据;并在公共边界处增加约束条件,解决相邻网格区域的水深模型不连续问题。试验证明:①在水深变化趋势不一致的区域,与等间距法和水深复杂度法相比,不确定度法所构DDM具有更高的质量;②相对于等间距法,不确定度法能自动确定网格剖分层次和网格大小,控制模型质量达到预先设计的指标要求,相对水深复杂度法,具有更强的自适应调控能力;③不确定度法比水深复杂度法具有更高的自动化程度和效率。

A Method for Constructing an Adaptive Grid Digital Depth Model Based on Mean Vertical Uncertainty of Area

A method for constructing an adaptive grid digital depth model based on mean vertical uncertainty of area is proposed.Depth uncertainty and its propagation is used for estimating the mean uncertainty of depths in the modeled area,levels of grid partition are determined automatically and depths are organized effectively;constraint conditions at common nodes of adjacent areas are added for making the depth model continuous.The results show:① in the area where the trend of depth change is not consistent,the method based on mean vertical uncertainty of area is better than the method with equal spacing and the method considering depth complexity;② the method based on mean vertical uncertainty of area is able to determine the grid spacing automatically and make the model quality meet the design requirements while the regular grid method is not,the method based on mean vertical uncertainty of area is more adaptive than the method considering depth complexity;③the method based on mean vertical uncertainty of area is more automatic and efficient than the method considering depth complexity.