FITS、BMP和SCR图象格式及相互转换

本文详细介绍图象的ＦＩＴＳ格式、ＢＭＰ格式和ＳＣＲ格式。ＦＩＴＳ格式已经是天文界的通用格式，几乎所有的天文软件包都支持这一格式，而ＢＭＰ格式在ＰＣ计算机上有广泛的运用，有大量ＰＣ软件支持ＢＭＰ图象的显示，处理和打印。云南天文台的ＳＣＲ格式是ＰＣ机采集ＣＣＤ图象所使用的格式。实现这三种格式的相互转换，就可以自由的将ＣＣＤ图象在ＰＣ机和工作站上进行各种处理。转换的包括图象头和图象数据的转换，其关键在于交换数据的高低字节。     

满足给定数字特征和谱的随机信号的产生

本文给出了满足给定数字特征和谱的随机序列的产生方法，即用无记忆非线性变换实现非正态随机信号的模拟．该法与Ｇｕｊａｒ法相比较，放宽了变量概率分布的限制，只要求变量矩的存在。同时考虑无记忆非线性变换对谱的影响，用异于Ｌｉｕ的方法在频域上求解输入正态分布随机序列的谱．     

任意曲线曲面上重磁位场转换的B样条函数法

提出用B样条函数求解曲线、曲面上重磁位场的向上延拓,水平、垂向导数计算,磁异常分量互换的方法。该方法的特点是:原理简明,程序通用性强,计算精度高。     

对提高导航型GPS定位精度之工作方法的探讨

论文主要介绍了导航型GPS坐标转换参数的计算方法和测量误差的改正方法。该方法的要点是通过实测多个公共点来计算坐标转换参数,以此获得较好的转换精度;并进一步对观测结果加以误差改正,从而提高其定位精度。     

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。     

保持主分量变换结果信息的量化方法

从主分量变换后图像信息保持的角度出发，充分分析了变换结果存储导致信息丢失的原因；同时以直接线性变换为基础，提出了另外两种新的对主分量变换结果进行量化的方法；并对这3种方法进行了充分的比较，文中最后以信息熵为指标评价了3种方法的优劣。     

Lateral tidal mixing in the Antarctic marginal seas

Tidal mixing plays an important role in the modification of dense water masses around the Antarctic continent. In addition to the vertical (diapycnal) mixing in the near-bottom layers, lateral mixing can also be of relevance in some areas. A numerical tide simulation shows that lateral tidal mixing is not uniformly distributed along the shelf break. In particular, strong mixing occurs all along the Ross Sea and Southern Weddell Sea shelf breaks, while other regions (e.g., the western Weddell Sea) are relatively quiet. The latter regions correspond surprisingly well to areas where indications for cross-shelf exchange of dense water masses have been found. The results suggest that lateral tidal mixing may account for the relatively small contribution of Ross Sea dense water masses to Antarctic Bottom Water.     

Stepwise Conditional Transformation for Simulation of Multiple Variables

Most geostatistical studies consider multiple-related variables. These relationships often show complex features such as nonlinearity, heteroscedasticity, and mineralogical or other constraints. These features are not handled by the well-established Gaussian simulation techniques. Earth science variables are rarely Gaussian. Transformation or anamorphosis techniques make each variable univariate Gaussian, but do not enforce bivariate or higher order Gaussianity. The stepwise conditional transformation technique is proposed to transform multiple variables to be univariate Gaussian and multivariate Gaussian with no cross correlation. This makes it remarkably easy to simulate multiple variables with arbitrarily complex relationships: (1) transform the multiple variables, (2) perform independent Gaussian simulation on the transformed variables, and (3) back transform to the original variables. The back transformation enforces reproduction of the original complex features. The methodology and underlying assumptions are explained. Several petroleum and mining examples are used to show features of the transformation and implementation details.