Space-time interaction amongst clusters of mining induced seismicity

Elementary cluster analysis of induced seismicity in a South African gold mine has shown that there is a clear interaction amongst the clusters; and that the level of the interaction is a function of the distance. The clusering algorithm used is an adaptation of the single-link cluster analysis which considers both three-dimensional space and time. A high level of interaction between the clusters is demonstrated from the cross-correlation analysis of seismic activity rates and radiated energy. A distinct decrease in the value of correlation coefficients was detectable as distance increased. This was somewhat surprising, considering the simplicity of the technique used. Since no attempt is made to study the physical mechanisms of interaction, these results are very preliminary, but interesting from an observational point of view.     

海水声速剖面基函数的重构性能分析

海水声速剖面通常使用经验正交函数（Empirical Orthogonal Function,EOF）进行稀疏表示,然而基函数会受到数据完备性和数据测量时间的制约,其代表性误差会导致声速剖面重构精度受限。为了提高声速剖面的重构精度,本文利用模糊C均值聚类对BOA_Argo历史数据集进行聚类分析,探讨不同聚类空间的训练集数据对实测声速剖面重构精度的影响。研究表明,声速剖面具有明显的时空聚集特性,聚类后的历史声速剖面集生成的基函数和平均声速剖面具有最优的重构性能。本文研究结果有助于为历史声速剖面训练集的选取提供实际指导意义,进而提高声速剖面重构精度乃至反演精度。     

Analysis of spatial clustering optimization

Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.     

一种新的近似平行沟渠群的典型化方法

提出并实现了一种针对近似平行沟渠群的典型化方法。首先,建立沟渠的方位关系图,通过生成关系图中各连通分量的极大完全子图,提取近似平行的沟渠;然后,依据视觉认知规律定义沟渠间的认知距离,并基于最小生成树的剪枝对沟渠进行分组;接着,运用K-means ++算法对分组后的沟渠进行重新表达;最后,通过比较综合前后沟渠群的外轮廓相似性以及沟渠分布密度的差异,对典型化结果进行了评价。对广州市数据进行了实验,结果表明该方法能有效进行近似平行的沟渠群的典型化。     

Model generalization of two different drainage patterns by self-organizing maps

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.     

密度聚类算法在连续分布点云去噪中的应用

在原始测量获取的点云数据中,除了目标数据外,还有大量的噪声数据。噪声往往无规律地分布在目标物体周围,难以用统一数学模型区分。基于密度的聚类算法将簇定义为密度相连的点的最大集合,能发现任意形状、大小的类簇,将该算法应用在点云去噪中,能将密度分布连续点进行聚类,从中提取出目标点云。     

空间数据模糊聚类的有效性评价

基于模糊划分中存在的分类不确定性因素和空间数据的空间位置特征,提出了一种新的空间数据模糊聚类有效性函数。实验结果表明,这种新的有效性函数能够对模糊聚类结果的有效性进行正确的评价,特别是对于空间数据模糊聚类有效性评价,其分类效果较理想,同其他有效性指标相比,能得到较优的分类数。     

基于聚类有效性函数的面状地理实体聚类

为解决聚类数未知条件下面状地理实体的聚类问题,文中提出了一种基于聚类有效性函数的聚类方法。给出了适合面状地理实体k-中心点聚类算法的聚类有效性函数;将该有效性函数改写为适应度函数,设计了基于遗传算法的面状地理实体聚类算法。该算法在计算聚类数的同时能得到划分聚类结果。实验结果从一定程度上反映了数据集的结构信息特征。     

空间层次聚类显著性判别的重排检验方法

同时顾及空间邻近与专题属性相似的空间层次聚类是挖掘空间分布模式的一种有效手段。空间层次聚类方法虽然可以获得多层次的聚集结构,但聚类结果显著性的统计判别依然是一个尚未解决的难题。为此,本文提出了一种空间层次聚类结果显著性的统计判别方法,用于确定空间层次聚类的停止准则,减少聚类过程对参数设置的依赖。通过试验分析与比较发现,该方法能够有效判别空间层次聚类结果的显著性和确定层次聚类合并过程的停止条件,同时具有很好的抗噪性,避免随机结构的干扰。     

图象分割中随机噪音的影响

该文在生成的灰度图象上加上随机噪音，用最大后验（ＭＡＰ）估计方法、模糊Ｃ－均值方法和Ｏｔｓｕ阈值方法进行了分割计算，对结果进行了对比分析，绘出了分割随噪音的标准差变化曲线，对噪音图象使用了不同的滤波方法处理之后，并进行计算。