广西沿岸海平面变化的关联维数研究

采用相空间重构和关联维数方法，研究了广西沿岸月平均海平面变化的分形特征，结果表明，相空间重构的延迟时间τ为3△t，当关联维数趋于饱和时，石头埠，北海和龙尾的相空间维数为7，而涠洲岛为9，它们的关联维数平均值平均值分别为6.6507,6.5414,6.7059.     

马氏珠母贝生长性状的相关分析

对马氏珠母贝(Pinctadamartensi)不同生长时期的壳长、壳高、壳宽和活体质量进行了一年多的跟踪测量。分析表明,在不同生长时期,其4个性状的两两间的相关系数均达到极显著水平(P<0.01),但其相关系数大小排列顺序存在差异,8次测量的平均相关系数大小依次为壳高-壳长>壳宽-体质量>壳高-体质量>壳长-体质量>壳长-壳宽>壳高-壳宽。在不同生长时期,活体质量对壳高、壳宽的偏回归系数均极显著(P<0.01),而对壳长的偏回归系数在第1,3,7,15个月时不显著(P>0.05)。该研究的结果可为珍珠贝的选育种的重要目标性状的确定提供理论指导。     

正交距离圆曲线拟合方法

在分析圆曲线拟合准则基础上,提出采用正交最小二乘法拟合圆曲线。该方法以圆曲线正交距离残差平方和极小为准则。基于间接平差原理详细推导了相关模型和算法,并以实际算例说明了该方法的效果。     

基于MODIS数据的浒苔信息提取方法研究

浒苔是一种大型绿藻,大量浒苔爆发会严重影响景观,干扰旅游观光和水上运动的进行。本文利用NASA提供的MODIS02的250m分辨率影像,分别采用了比值法、归一化植被指数法和监督分类中的最小距离分类,提取了浒苔的信息,实验结果表明,三者都可以提取浒苔信息,但是比值法提取的结果最好,最小距离分类很难与薄云区分,必须加入人工目视解译,归一化植被指数可以区分出薄云,但是遗漏较多。     

一种新型遥感影像快速匹配算法(英文)

An iterative algorithm to calculate mutual correlation using hierarchical key points and the search space mark principle is proposed. An effective algorithm is designed to improve the matching speed. By hierarchical key point algorithm and mutual correlation coefficients of the matching images, the important points can be iteratively calculated in the images hierarchically, and the correlation coefficient can be obtained with satisfactory precision. Massive spots in the parameter space which are impossible to match can be removed by the search space mark principle. Two approximate continuities in the correlation image matching process, the image gray level distribution continuity and the correlation coefficient value in the parameter space continuity, are considered in the method. The experiments show that the new algorithm can greatly enhance matching speed and achieve accurate matching results.     

GPS在计算导弹脱靶量中的应用研究

将GPS技术应用于导弹靶试脱靶量测量,并对计算过程作了较为详细的分析和探讨。试验证明：利用GPS测定脱靶量,测量精度高,简单易行,可以在靶场逐步试验推广。     

低渗透储层水平井—直井联合布井极限注采井距研究

低渗透储层水平井—直井联合布井技术具有广泛的应用前景,其布井方式至关重要。首先利用劈分流场的方法直观地给出常规储层水平井—直井联合布井的产能评价方法;然后针对低渗透储层的渗流特点,在考虑启动压力梯度条件下建立新的井网渗流物理模型并求解得到产能评价公式;最后经分析计算给出启动压力梯度影响的面积井网极限注采井距关系曲线。研究结果为低渗透储层部署水平井—直井联合井网提供理论依据。     

Origin of Oils in “Subtle pools” in the Dongying Depression, Bohai Bay Basin, China

Subtle traps or oil pools have become an important exploration play in the Dongying Depression, Bohai Bay Basin, east China. Despite recent successes in exploration, the formation mechanisms of subtle traps are still not well understood. The majority of subtle oil pools in the Dongying Depression are developed in the middle interval of the Es3 Member of the Paleogene Shahejie Formation with the subtle traps being primarily of lenticular basin-floor turbidite sands encompassed in mudstones. Oil in the subtle traps was previously thought to have migrated directly from the surrounding source rocks of the same formation （Es3）. Detailed geochemical investigation of 41 oils and 41 rock samples from the depression now indicates that the oils from the subtle traps cannot be correlated well with the surrounding Es3 source rocks, which are characterized by high Pr/ Ph （〉1）, low Gammacerane/C30hopane, representing a freshwater lacustrine setting. In contrast the oils features low Pr/Ph （〈1） and relatively high Gammacerane content, showing a genetic affinity with the underlying Es4 source rocks, which also have the same qualities, indicating a brackish lacustrine setting. Oils in the Es3 subtle traps are probably derived from mixed sources with the contribution from the upper Es4 source rocks predominating. Therefore unconventional oil migration and accumulation mechanisms need to be invoked to explain the pooling of oils from the ES4 source rocks, which probably came through a thick low interval of the Es3 source rocks with no apparent structural or stratigraphic pathways. We suggest that the subtle oil migration pathway probably plays an important role here. This finding may have significant implications for future exploration and the remaining resource evaluation in the Dongying Depression.     

Group K-Nearest Neighbors queries in spatial network databases

This paper addresses the problem of Group K-Nearest Neighbors (GKNN) queries in spatial network databases, and suggests a novel approach based on real network distances. This approach essentially uses network Voronoi diagram properties together with a progressive incremental network expansion for determining the inner network distances that are needed to obtain GKNN queries.