圣劳伦斯河河口河床的水深数据整合研究

Juzer Noman, Willian Ney Cassol, S. Daniel, Damien Pham Van Bang
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引用次数: 1

摘要

地层识别在海底形态研究中具有重要作用。这些海底动态结构的存在对航行构成了威胁。它们还影响在沿海洪水等情况下使用的水动力学模拟模型。通常使用多波束回声测深仪(MBES)来测量这些地层。不幸的是,MBES的覆盖范围仅限于每次调查的小区域。因此,对大面积感兴趣区域(如导航通道)的分析需要整合在不同时间在重叠区域上获取的不同数据集。这些数据集之间存在的时空不一致性可能会严重影响对河床的研究,河床受许多自然过程(例如潮汐;流)。本文提出了一种整合多源测深数据集进行地层研究的新方法。提出的方法是基于整合多源数据集并应用经验贝叶斯克里格插值来创建多源数字测深模型(DBM)。它的设计是为了适应具有高度海底活力的河口地区,这是圣劳伦斯河河口的河流-海洋制度的特点。这个地区的特点是高潮汐循环和沙丘的存在。该研究使用了MBES数据,该数据是在4天的时间里每天在该地区的一片沙丘上获取的,目的是监测沙丘的形态和迁移。与现有方法相比,所提出的方法具有良好的效果,所得到的表面相对于原始数据的误差较小,并且尽管河流-海洋环境高度动态,但通过数据集的整合可以保持沙丘形状。
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Bathymetric data integration approach to study bedforms in the estuary of the Saint‐Lawrence River
The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, MultiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g., Tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of 4 days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.
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