GVG采样线代表性检验

通过在实验区内设置不同类型的采样线,对GVG采样线的代表性进行检验.结果表明,高速公路和乡村道路类型的采样线对区域代表性的精度在95%以上;国道为86.726%;省道为65.447%.对于不同的缓冲区,高速公路、省道和乡村道路类型采样线,以200m缓冲区的代表性最好.而国道则以800m缓冲区的代表性最好.对于不同的作物而言,无论何种类型的采样线或者缓冲区,种植面积最大的棉花的精度是最好的.最低的是省道采样线1000m缓冲区,精度是78.146%,最好的是国道采样线800m缓冲区,精度是99.974%.除省道外,其他精度都在94.8%以上.这说明GVG采样线所获得的成数对于区域主要作物的代表性是很好的.     

多道瑞利波在矿井独头巷道超前探中的应用

瑞利波超前探测作为一种新的矿井地球物理探测方法,具有简便、快速、经济、分辨率高、适用场地小、应用范围广等优点。介绍了瑞利波探测技术原理,并将其用于煤矿井下独头巷道大距离超前探测中。实例表明,探测距离达80 m以上,条件允许时可达100 m以上。     

改进的边缘角度直方图在遥感图像检索中的应用

针对基于形状的遥感图像检索中区域分割困难这个问题,本文提出利用边缘角度直方图作为形状特征对遥感图像进行检索;针对遥感影像分辨率和颜色反差对检索性能的影响,本文提出采用多尺度分析的思想,提取图像在多个尺度上的边缘角度直方图特征进行检索。通过单一尺度边缘角度直方图特征和多尺度边缘角度直方图特征检索的对比实验,揭示了本文所提出的改进方案使遥感图像的检索精度提高了约31个百分点。     

一种直线特征标志的子像素定位方法

在近景摄影测量中,常常需要对直线特征的标志进行定位,而标志定位的精度会影响到整个测量的精度。不同特征的控制点标志,对应不同的识别方法,因此控制点标志的选择须根据不同的要求,进行综合研究分析。本文提出一种新的混合方法,首先采用Hough变换对直线特征标志进行直线检测,得到直线初值;然后采用灰度矩边缘直线拟合定位法,对标志点中心进行子像素定位。通过实验证明:经过Hough变换得到粗值的灰度矩边缘直线拟合定位法达到较高的定位精度,证明了该混合方法的有效性。     

石狮市地下管线数据采集与建库

地下管线是城市生存发展的命脉,准确掌握地下各种管线的状况,是搞好城市建设和规划管理工作的基础。本文根据石狮市城市规划建设现状,探讨地下管线普查和采集的方法,提出建立地下管线信息系统等有关问题。     

上海市建设用地勘测定界若干问题探讨

对在上海市建设用地勘测定界过程中涉及的土地权属界线认定、原土地用途划分时所现的若干问题进行了详细探讨,同时从政策角度、技术角度提出了以上问题的相应解决途径。     

提高管线探测水平分辨率的技术方法

在平顶山矿工路路面改造工程中,使用GX-2型及RD-4000型地下管线探测仪探测地下管网布设情况。根据地下管线种类、管线布设方位、埋置深度、探测难度大小进行了多次试验,在此基础上确定了电磁探测技术与方法：利用直连法提高被测管线中的交变电流,压制邻近平行管线和地下介质中的异常反映;在多管并存、且间距较小的情况下,应选择“梯度法”对磁场水平分量垂直梯度ΔHx进行观测,以得到最大清晰异常;另外,还应根据具体情况,合理选择诸如压制旁侧管线法、选择发射法、偏移感应法、动源发射法等发射方式,保证目标管线中有较强异常呈现。在管线密集区,应尽可能地降低工作频率,以减小旁侧管线中产生的二次电流及二次磁场;为防止信噪比下降,可适当减小收发距,以提高接收机的灵敏度。     

暖切变型江淮梅雨锋结构及其形成和维持机制

对在1991年7月上旬特别稳定的梅雨形势下持续10天的特大暴雨期作了合成和典型过程研究,并进一步探讨了暖切变型江淮梅雨锋天气系统模型。它在自由大气层表现为结构竖立的相当正压切变线和一种不同于经典地转动量近似的带状准二维运动场。边界层的浅层锋生倾斜环流以及偏于切变线轴以南的Ekman抽吸与自由大气层中基本上由积云对流加热驱动的竖直环流相互依存和耦合,造成持续的大暴雨。这种系统的初生和中断后复苏的机制可能是由斜压性极弱的副热带高空东移的短波扰动在低层“湿度锋”带紧南侧触发导致的一种带状CISK。     

Estimation of statistical parameters for censored lognormal hydraulic conductivity measurements

Field tests of hydraulic conductivity (e.g., injection test, pumping test, etc.) in low permeability formations are subject to censoring due to the detection limit of the instruments used. An iterative method of estimating the mean and variance of hydraulic conductivity data with a presumed log-normal distribution function is presented. This method accounts for the data that are actually below the lower detection limit (called truncated data) and thus gives distribution parameters that are more representative for the underlying distribution. The proposed method is then tested on two simulated normally distributed random datasets having different variances. The results show that the means and variances estimated by the proposed method are very accurate. Finally, the method is used to estimate the mean and variance of hydraulic conductivity data from single hole water injection tests in a fractured geological formation.     

Estimation of covariance matrix from geochemical data with observations below detection limits

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.