共查询到15条相似文献，搜索用时 250 毫秒
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数字水深模型是对海底表面形态的数字化表达,传统的网格数字水深模型存在不能根据海区水深变化情况自动调节内插水深间隔的不足,提出了以深度极限误差作为判断标准,顾及海底地形变化的补深补浅方法,并在此基础上构建了相应的狄洛尼三角网。 实验证明:与传统的最浅点抽稀规则格网方法相比,所提方法更能合理的反映出海底地形的实际变化情况,并明显改善 DDM 精度。 相似文献
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基于1986-2008年的中国近海及邻近海域再分析产品(CORA)气候平均海温资料,分别运用S-T法、垂向梯度法和最大曲率点3种温跃层定义计算了南海温跃层上界深度,揭示了南海温跃层季节变化特征。对3种不同定义确定的温跃层上界深度进行比较发现:采用不同定义计算南海温跃层上界深度存在差异,S-T法确定的温跃层上界深度最浅,垂向梯度法其次,最大曲率点法最深;在深水区(水深200 m)运用S-T法计算的温跃层上界深度与垂向梯度法的结果比较一致,都与实际温跃层深度符合较好;在浅水区(水深200 m),垂向梯度法和最大曲率点法可以准确判定无跃区,但对于温跃层深度计算,3种定义误差均较大。 相似文献
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基于多波束测深的地形定位是水下潜器导航技术研究和发展的重点,多波束测深数据的高精度快速重采样是水下地形匹配定位的前提。传统的实时抽稀方法因对多波束测深数据模型的过分简化而效果欠佳。参考Douglas-Peucker算法和点云数据抽稀方法,采用角度-弦高联合准则对多波束每ping数据进行抽稀处理,参考导航地形图对抽稀后的多ping数据基于点云离散度进行二次抽稀处理,从而实现多波束测深数据的高精度快速抽稀处理。典型的数学仿真地形和实测多波束条带数据实验表明:文中提出的抽稀方法数据抽稀率仿真地形在85%以上,实测地形在90%以上,数据抽稀前后点云构成的曲面DEM误差在3%以内,并且算法实时性较好。 相似文献
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海底沙波特征线的最优方向剖面自动识别方法 总被引：2，自引：2，他引：0
海底沙波是发育在近海陆架上的一种常见海底地貌类型,海底沙波特征与运动规律的研究具有重要的科学意义与工程应用价值,沙波脊线与谷线是表征海底沙波的最基本特征,也是精确描述沙波运动的基本参量。本文提出了一种基于复合数字水深模型的沙波特征线自动识别方法——最优方向剖面法,基于水深曲面归算得到最优剖面方向,再依据最优剖面方向求导并判定极值,自动提取沙波形态特征点,最终形成沙波脊线和谷线。以台湾浅滩复合型沙波为例进行对比实验研究,结果表明,该方法能基于不同分辨率的数字水深模型自动准确地提取海底沙波脊线与谷线,勿需设置阈值,地形自动化识别程度得到进一步提升,具有重要的实际应用价值。 相似文献
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《Estuarine, Coastal and Shelf Science》2003,56(3-4):749-764
A challenge for marine ecologists is to explain distinct and recurrent patterns in the distribution of marine faunas by developing new methods that identify and link environmental processes responsible for these patterns. Methods that describe and predict the distribution of benthic faunas using single factors such as sediment type or water depth are generally inadequate, particularly when applied on a broad scale. When a combination of factors such as near-bed tidal velocity, surface seawater temperature and salinity are evaluated in conjunction with sediment type and depth, however, they more clearly characterise benthic habitats. Using principal component analysis (PCA) patterns in the distribution and abundance of different echinoderm and crustacean species were shown to be predictable and characterised by a suite of physical factors. Characterising benthic habitats using factors from the environment provided a potential mechanism for predicting patterns in their spatial distribution. A new analytical method for characterising a species habitat was constructed using a combination of PCA and a generalised additive model. The method is able to predict the habitat preferences of individual species based on their association with physical factors characterising their habitat. These preferences were then used to describe the probability of a species occurring across a range of different habitats, which is referred to as the habitat-envelope. This method enables one species habitat range to be compared directly to another. The strong correlation between species patchiness and its habitat-envelope was used to develop an index to identify species that are potentially more sensitive to habitat change. Distinct patterns in the habitat preferences of echinoderms were generally stronger than those identified for crustaceans. Thus, crustaceans were found more likely to exploit a wider range of habitats than echinoderms, suggesting that they may be less sensitive to habitat change. 相似文献
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针对当前构建高精度数字水深模型中常用的格网数据索引方法,在海量数据管理中存在因树的规模限制而导致检索效率低的问题,提出了一种格网树与KD树(K-Dimension,KD)组合的水深数据索引方法。首先,利用格网将水深源数据分割为网状的数据块,构建出数据块的格网树;其次,构建各数据块的KD树,实现对数据块中任意数据的快速索引;最后,通过快速定位数据块,查找其所在KD树的位置,实现对海量数据的快速检索。实验结果表明:①与格网树相比,本文所提组合检索方法的检索效率随检索树规模的变化不明显;②在相同的数据量下,组合树的检索效率要普遍高于格网树方法。 相似文献