中国天山-北山地区晚古生代岩石地层系统厘定及沉积环境分析

通过编制天山一北山地区地质图对该区晚古生代岩石地层系统进行了重新厘定，并初步分析了这一地区晚古生代不同阶段的沉积环境，论述了晚古生代时天山与北山应属于同一沉积构造区的地层结构与沉积特征证据。     

浙江黄岩富山山崩地貌景观及其旅游开发

浙江黄岩富山地区受新构造运动的影响，经历了节理发育、流水切割与节理扩张，最终发育成山崩地貌景观。该景观资源丰富，具有组合好、规模大、分布广、科考价值高等特点，是迄今发现的华东地区规模最大的山崩地区。根据《旅游资源分类、调查与评价》的国家标准，对该类景观资源单体进行评价与分级以及开发条件分析，认为黄岩富山山崩地貌观资源具有很大的旅游开发价值。旅游开发时应以保护山崩地质遗迹为前提，以凸显山崩地貌景观的主题与特色为关键，以环境的安全性为根本，实施综合开发。     

深埋隧洞围岩分类方法--JPQ系统

本文以某深埋隧洞围岩分类为例、以著名的巴顿岩体质量分类Q系统为基础,建立了高地应力、高外水压力条件下的围岩分类方法--JPQ系统,经验证该方法在某深埋隧洞围岩分类中适用性好,并可推广使用.     

高边坡岩体卸荷带划分的量化研究

边坡卸荷及其卸荷带是西南地区高边坡一种常见的现象，对岩石边坡稳定性及其它岩石工程问题有重要的影响。在分析目前卸荷带划分方法的基础上，根据卸荷带的形成机理及地质表现，提出了用裂隙率、张开裂隙率和“隙宽和”3个量化指标进行卸荷带的划分。结合实际工程，进行了大量的现场裂隙测量，分析结果表明，3个量化指标对卸荷带的强弱有较好的响应关系，作为卸荷带划分的量化指标具有较好的可操作性。进一步，结合卸荷带的地质特点，提出了所研究的具体工程其卸荷带划分的建议方案。     

Study on strength reduction factors considering the effect of classification of design earthquake

Introduction The strength reduction factor is defined as the lateral yielding strength required to avoid yielding in the system when subjected to a given ground motion, to the lateral yielding strength required to maintain the displacement ductility ratio demand equal to a pre-determined target duc-tility ratio under the same ground motion. The strength reduction factors are not only the key fac-tors in determining seismic action for force-based seismic design, but also one of the key parame-t…     

用雷达反射率作对流性降水和层状云降水自动分类

为提高雷达定量测量降水的精度,利用武汉CINRAD/SA雷达反射率数据,研究提出了对流性降水和层状云降水自动分类算法(ACSS)。该算法在二维反射率结构场初步分类降水的基础上,识别亮带并从体扫描数据中提取降水的三维结构特征,然后对初步分类结果进行订正。试验表明,ACSS能较准确地实现对流性降水和层状云降水的自动分类,相对于只根据二维结构分类降水性能上有较大提高,主要表现在能正确识别出亮带特征明显的强层状云和对流核外沿的对流弱回波区。     

Visualization of Volcanic Rock Geochemical Data and Classification with Artificial Neural Networks

An unsupervised neural network technique, Growing Cell Structures (GCS) was used to visualize geochemical differences between four different island arc volcanic rock types: basalts, andesites, dacites and rhyolites. The output of the method shows the cluster structure of the dataset clearly, and the relevant geochemical patterns and relationships between its variables. The data can be separated into four clusters, each associated with a specific volcanic rock type (basalt, andesite, dacite and rhyolite), according to a unique combination of major element concentrations. Following clustering, performance of the trained GCS network as a classifier of volcanic rock type was tested using two test datasets with major element concentration data for 312 and 496 island arc volcanic rock samples of known volcanic type. Preliminary classification results are promising. In the first test dataset 94% of basalts, 76% of andesites, 83% of dacites and 100% of the rhyolites were classified correctly. Successful classification rates in the second dataset were 100%, 80%, 77%, and 98% respectively. The success of the analysis suggests that neural networks analysis constitutes a useful analytical tool for identification of natural clusters and examination of the relationships between numeric variables in large datasets, and that can be used for automatic classification of new data.     

Factors influencing determination of shale classification indices and their correlation to mechanical properties

The influence of sample preparation procedure on the measured classification indices such as clay-size fraction, liquid limit, and plastic limit of shales was investigated. Shale samples covering a wide range of composition and plasticity were tested using standard and modified preparation procedures to yield quantitative data required for this investigation. The study specifies the range of shale plasticity in which the measured values of clay-size fraction and liquid limit are sensitive to hydrometer specimen size and rehydration time, respectively. The study also revealed that the degree of shale particle disaggregation has an important influence on the measured clay-size fraction and liquid limit. This influence is maximized for shales with intermediate plasticity and can be interpreted in terms of particle size and shape. Correlations were established between classification indices derived from standard and modified sample preparation procedures. It is recommended to use these correlations to adjust shale classification indices derived from standard sample preparation procedures usually utilized by commercial laboratories. Such correlations help in creating a universally followed procedure for better estimation of shale engineering properties and reliable interpretation of case histories involving shale formations.     

利用机载LIDAR双次回波高程之差分类激光脚点

机载LIDAR技术已经引起了测绘界的浓厚兴趣,有可能给测绘领域带来一场新的技术革命。机载LI-DAR技术的硬件设备在国外已相对成熟,而机载LIDAR的数据后处理算法仍然处于研究发展阶段,还有诸多问题没有得到解决,其关键之一就是机载LIDAR数据的滤波与分类。本文首先对已有的滤波分类方法进行了综合评价,并指出了各自的局限。然后提出利用两次回波信号的高程数据来实现对机载LIDAR数据的分类。首次分类后得到植被激光脚点点集和地面及房屋激光脚点点集。而房屋上的激光脚点要高出地面上的激光脚点数米之多,简单利用阈值法就可以进一步分类出房屋激光脚点和地面激光脚点。也可以先经过滤波处理将地面激光脚点去掉,然后利用两次回波信号的高程数据来分类自然植被激光脚点和人工地物激光脚点。实验证明所提方法简单有效,算法简单实用,特别适用于分类植被激光脚点。     

基于RBF神经网络的遥感影像分类器设计

设计了一种运用径向基函数神经网络进行遥感影像分类的监督分类器,以实际的遥感光谱影像分类为例,将分类效果同传统的最小欧氏距离法分类进行比较,探讨了RBF分类的优越性,结果表明RBF神经网络是一种更为有效的图像分类器。