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。     

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。     

高地震区公路隧道地震动力响应分析

基于土一结构相互作用理论,对高地震区一实际重大工程的公路隧道洞口段结构进行了抗震计算,得到了衬砌结构各控制点的位移、加速度及内力响应规律。结果表明：在人工合成地震波条件下,衬砌墙脚、拱腰为抗震薄弱位置;结构的加速度波形与输入波形相似;这些结果为抗震设计提供了一些依据。     

地铁隧道结构变形监测信息管理系统的开发

地铁隧道结构变形监测的特殊性、周期性和长期性,使其信息量非常庞大.信息管理是地铁隧道结构变形监测中一项重要的工作,现有的管理方式效率很低.为了高效、准确地管理监测信息,及时分析预报地铁隧道结构的稳定状况,本文结合南京地铁运营期隧道结构变形监测实例,开发了一套具有变形监测资料存储、预处理、管理分析、可视化分析、预测预报及限值预警等功能的信息管理系统,保证了准确及时快速的数据处理和信息反馈,具有良好的运用和推广前景.     

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。     

自动化监测技术在我国首条大型电力隧道中的应用

通过自动化监测在超大工程项目中的应用,利用现有技术手段,对工程进行有效管理,逐渐将对工程的安全、预判起到越来越重要的作用,它可以在为工程节约大量资金情况下,提高预判安全施工的能力,是提高工程管理的重要手段。     

Overbreak and underbreak in underground openings Part 2: causes and implications

Summary The newly developed light sectioning method has been used to investigate some of the causes and costs of overbreak and underbreak. Investigations at the Aquamilpa Hydroelectric Project in Mexico have shown decreased overbreak and increased underbreak as a result of increased rock quality and decreased explosive energy. A new measure of explosive energy, the perimeter powder factor (PPF), has been defined and shown to be useful in the context of tunnel-wall rock damage. Tentative results indicate that explosive energy (PPF) may be a more important factor in producing underbreak, whereas rock quality may be a greater factor in producing overbreak. A site-specific equation is given for predicting overbreak or underbreak as a function of rock quality and explosive energy, with an evaluation of the cost of underbreak and overbreak.     

The origin of accretionary lapilli

Experimental investigations in a recirculating wind tunnel of the mechanisms of formation of accretionary lapilli have demonstrated that growth is controlled by collision of liquid-coated particles, due to differences in fall velocities, and binding as a result of surface tension forces and secondary mineral growth. The liquids present on particle surfaces in eruption plumes are acid solutions stable at 100% relative humidity, from which secondary minerals, e.g. calcium sulphate and sodium chloride, precipitate prior to impact of accretionary lapilli with the ground. Concentric grain-size zones within accretionary lapilli build up due to differences in the supply of particular particle sizes during aggregate growth. Accretionary lapilli do not evolve by scavenging of particles by liquid drops followed by evaporation — a process which, in wind tunnel experiments, generates horizontally layered hemispherical aggregates. Size analysis of particles in the wind tunnel air stream and particles adhering to growing aggregates demonstrate that the aggregation coefficient is highly grain-size dependent. Theoretical simulation of accretionary lapilli growth in eruption plumes predicts maximum sizes in the range 0.7–20 mm for ash cloud thicknesses of 0.5–10 km respectively.     

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.