混沌退火Hopfield网络及其在储层聚类分析中的应用

将一种基于混沌退火的Hopfield神经网络(ACHN)应用到储层聚类分析中。这里主要阐述了ACHN聚类的基本原理、混沌神经元的标定、ACHN聚类的策略、先验信息的约束以及混沌退火搜索。实际砂砾岩储层聚类分析表明:这种网络能克服普通Hopfield网络陷入局部最优的不足,得到更准确、精细的聚类结果。     

模糊聚类实现岩性自动划分

利用测井数据中的自然伽玛、中子、声波和密度测井曲线所蕴含的岩性信息,用模糊聚类算法实现岩性的自动划分。该方法识别准确度高,可靠性强。通过对川西坳陷十八口井的实际应用,此方法在低孔低渗的致密碎屑岩岩性划分中取得了良好的效果。     

Analysis of Shallow and Deep Earthquake Doublets in the Fiji-Tonga-Kermadec Region

For the Fiji-Tonga-Kermadec area and for the period from January 1977 to July 2003, the Harvard CMT catalogue lists 1022 shallow, 410 intermediate and 633 deep earthquakes of moment magnitude from 4.9 to 8.0. The magnitude threshold, above which the catalogue is complete, is 5.3–5.4, and the number of earthquakes of magnitude above this value is 691 for shallow, 329 for intermediate and 476 for deep events, respectively. The proportion of earthquakes associated with doublets and multiplets against the total number of earthquakes is approximately the same in both data sets and therefore all earthquake pairs were considered regardless of their magnitude. We investigated all the pairs of earthquakes that occurred at a centroid distance of less than 40, 60 or 90 km from each other and within a time interval of 200, 300 or 450 days, depending on their magnitude. We found 208 pairs of shallow, 31 of intermediate and 92 of deep events. To ascertain whether these earthquakes in pairs are not connected by chance, the possibility of their occurrence in an uncorrelated Poissonian catalogue was considered. It was assumed that in such a catalogue the inter-event time is exponentially distributed, the earthquake magnitude follows the Gutenberg-Richter relation, and the distribution of centroid distances between the events in pairs is controlled by its non-parametric kernel estimate. The probability of the appearance of the observed proportion of doublets of shallow earthquakes in the Poissonian catalogue was found to be very low. The low probability of occurrence in a semi-random catalogue, created by randomising centroid locations in the actual data set, also indicates major importance of the distance criterion used for a doublet specification. In general, shallow earthquakes tend to form pairs at shorter distances and within shorter time intervals than deep earthquakes. Both the distance and the time intervals do not depend on the magnitude of involved events. The largest number of pairs of deep earthquakes is observed at a depth of about 600 km, and the proportion of deep events associated with doublets against the number of all events increases with depth. From comparison of the focal mechanism of earthquakes in pairs, measured by the 3-D rotation angle, it follows that deep earthquakes forming pairs have a more diverse focal mechanism than shallow events; the rotation angle for three quarters of shallow pairs and only for about one third of deep pairs is reasonably small. The azimuth between two events forming a doublet is in about 60–65% of cases close to the strike of one of nodal planes of the first or the second event.     

Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm

This study proposes an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem fuzzy c-means (FCM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through an optimization model. The meta-heuristic harmony search (HS) algorithm, which is conceptualized using the musical process of searching for a perfect state of harmony, is used as an optimization technique. The optimum parameter zone structure is identified based on three criteria which are the residual error, parameter uncertainty, and structure discrimination. A numerical example given in the literature is solved to demonstrate the performance of the proposed algorithm. Also, a sensitivity analysis is performed to test the performance of the HS algorithm for different sets of solution parameters. Results indicate that the proposed solution algorithm is an effective way in the simultaneous identification of aquifer parameters and their corresponding zone structures.     

Abell团相关性-富度关系的统计研究

本文对D≤4,R≥0的Abell团样品和整个R≥3的Abell团样品的空间两点相关函数做了统计分析,结果表明R≥0,R=0和R≥3的Abell团的相关函数分别为175r~(-1.8),150r~(-1.8)和1900r~(-1.8)利用已有的R≥1和R≥2团的相关函数的统计结果,我们得到了Abell团的相关性幅度α与富度N之间的关系:α∝N~(1.6)。在相关性幅度的统计误差所允许的范围内,α与N之间的关系可以在宇宙弦理论中得到解释。     

基于自适应膨胀因子的模糊覆盖分级方法

分级界限的确定一直都是学者们关注的焦点,而如何确定分级数则往往被忽略了。根据模糊覆盖和膨胀因子的定义,提出了一种确定1维数据分级数的分级方法:首先通过覆盖半径做一个模糊覆盖,利用自膨胀因子对这个覆盖进行扩张,直到不能再向外扩张为止,这一级就确定了;然后进行下一级的确定。该方法不仅可以自动确定数据的分级数,而且克服了分级结果对参数的敏感性。最后用实验证明了方法的有效性。     

基于MRF随机场和广义混合模型的遥感图像分级聚类

有限混合模型FM的分级聚类已广泛应用于不同领域,然而,它的计算复杂度与观测数据的平方成正比,因此,在海量数据方面的应用就受到了限制。另一方面,多光谱图像数据中同时包含有空间和光谱两类信息,但大多数基于像素的多光谱图像聚类方法,仅使用了其频谱信息而忽视了空间信息。本文提出了一种新的基于广义有限混合模型GFM的分级聚类方法,该算法把MRF随机场和GFM模型结合在一起,分类数可以通过PLIC准则自动确定。算法在执行过程中,采用K均值聚类方式获得过分类图像,分级聚类从过分类图像开始,代替原来从单点类开始的方式,这样可以方便获取GFM模型成分密度的初始参数。最后,采用由Gibbs采样器生成的仿真测试图对算法的精度进行了定量评价,通过与K均值聚类和FM聚类的比较说明了本文算法的优越性,同时用荷兰Flevoland农业地区的极化SAR图像验证了本文算法的有效性。     

DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.     

改进蚁群算法的三维激光点云聚类方法

为了提高点云聚类方法的效率和精度,本文提出了一种蚁群优化投影寻踪算法。试验采用机载LiDAR点云数据,通过构建蚁群算法中信息素系数更新的对数反正切函数模型来减少所需的信息素更新系数的迭代次数,不断优化的投影寻踪方向投影,提高寻找最佳投影方向的连续空间的效率,实现了树木和建筑的分割。试验使用人工方法对比评估树木和建筑物的位置和数量的准确性。     

车载激光扫描数据的结构化道路要素自动提取方法

道路识别是无人驾驶、机器导航等领域的关键问题之一。本文利用局部特征约束方法进行点云的预处理分割,基于多尺度聚类方法实现道路要素的点云提取,并在结构化道路实例的识别中进行应用。结果表明,该方法效果良好,是一种合理有效的道路识别手段。