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Adaptive, variable resolution grids for bathymetric applications using a quadtree approach

机译:使用四叉树方法的水深测量应用的自适应可变分辨率网格

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The spatial sampling often used to process and represent bathymetric data are of fixed grid resolution where the least depth value is stored in each grid cell. This results in Digital Elevation Models (DEMs) that are used to depict the underlying features of the seafloor. With the discretion of the user, the resulting DEMs used may either be of coarse resolution or a very fine resolution surface which provides as many details as possible. However, depending on the resolution of the data collected and the variability of the seafloor, the arbitrary user denned grid resolution is not the best option. Hence we address the problem of finding an optimal grid resolution for representing and processing the bathymetric data for the application of bathymetric risk assessment whilst maintaining computational efficiency. Here we adopt the quadtree decomposition approach. In addition, the research suggests the optimal criteria and standard deviation threshold, oth values for this particular application. These suggestions are still flexible and can be optimized for this application depending on the end user requirements. Previous studies have focused only on the splitting criteria or the constrained criteria to ensure that there is homogeneous accuracy over the entire dataset. However, an investigation into the threshold selection for the standard deviation, ath which describes the variability in the dataset is one of the most important splitting criterion, that is still lacking. Also, a new approach to store the depths in the grid in a time ordered approach for each epoch is shown. By optimizing the criteria for the quadtree decomposition and time series algorithm, the approaches shown in this paper provide the adaptive, accurate DEM which makes optimal use of the available bathymetric data for the Netherlands Continental Shelf (NCS) as the study area. This data preparation step forms the basis for developing a probabilistic approach to assigning hydrographic resurvey frequencies in the NCS.
机译:通常用于处理和表示测深数据的空间采样具有固定的网格分辨率,其中最小深度值存储在每个网格单元中。这样就产生了数字高程模型(DEM),用于描述海底的基本特征。根据用户的判断,最终使用的DEM可能具有较粗糙的分辨率,也可能是非常精细的分辨率表面,它提供了尽可能多的细节。但是,取决于收集到的数据的分辨率和海底的可变性,任意用户定义的网格分辨率不是最佳选择。因此,我们解决了寻找最佳网格分辨率以代表和处理测深数据以应用测深风险评估的同时保持计算效率的问题。在这里,我们采用四叉树分解方法。此外,研究建议了最佳标准和标准偏差阈值,以及针对该特定应用的其他值。这些建议仍然很灵活,可以根据最终用户的要求针对此应用进行优化。先前的研究仅关注于分割标准或约束标准,以确保整个数据集具有均一的准确性。但是,对于描述标准偏差的阈值选择的研究(描述数据集的可变性)是最重要的分割标准之一,目前仍然缺乏。而且,示出了一种针对每个时期以时间排序的方法将深度存储在网格中的新方法。通过优化四叉树分解和时间序列算法的标准,本文显示的方法提供了自适应的,精确的DEM,可以最佳利用荷兰大陆架(NCS)作为研究区域的可用测深数据。此数据准备步骤构成了开发概率方法来分配NCS中水文调查频率的基础。

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