泌阳断陷下第三系核三上段高频层序中米兰柯维奇天文旋回信息

古气候是影响陆相层序发育的一个重要因素,其周期性变化是源于天文旋回的驱动力（即米兰柯维奇假说）。米兰柯维奇天文旋回包括偏心率旋回、倾斜率旋回和岁差旋回,这几个轨道参数所驱动的古气候变迁,分别形成了周期为１０万年（或４０万年）、４万年及２万年的高频层序,即小层序组、小层序及小层单元,研究结果表明,泌阳断陷湖盆的高频层序是由地球轨道旋回所驱动的古气候周期性变迁所形成的。     

基于地质记录用时域组合模型预测气候变化趋势的初步研究

用时域分析组合模型建立了１００万年来６０°Ｎ６月份太阳辐射量时间序列、宝鸡黄土粒度时间序列、渭南夏季风指数时间序列的动态模型．研究结果表明,时域分析组合模型较好地提取了时间序列的信息,得到的几个显著周期Ｔ＝１３３,１００,８９,４１,２３,１９ｋａ,与地球轨道三要素的变化周期接近．用组合模型拟合实测数据,精度是高的;用其预测未来气候替代性指标时间序列的变化情况,发现未来气候有向干冷方向发展的趋势．时域分析组合模型为研究气候变化趋势提供了一种定量分析、预测的方法．     

MIXTURE ANGLYSIS USING FACTOR ANGLYSIS I: CALIBRATION AND QUANTITATION

One of the major application areas of factor analysis, multivariate calibration and quantitation, is coveredin this review. The algorithms, methodologies and applications covered include principal componentregression, target transformation factor analysis, singular value decomposition and rank annihilationfactor analysis. Many important areas of research having relevance to multivariate calibration andquantitation problems are also covered in this review, including background correction, measurementerror, rank determination, cross-validation, figures of merit, detection of invalid samples, experimentaldesign, sample selection, statistical inference and wavelength selection.     

基于灰关联分析的GPS高程拟合加权平均函数模型权函数优选

基于灰色系统理论的灰关联分析方法,提出了权函数的优选方法,实例分析表明其具有较好的实用价值.     

GPS网平差观测量选取的理论分析

由多台GPS接收机在不同时段进行静态观测而组成的观测网形,当采用高精度的科研软件与常用商用软件解算基线时,其基线向量的方差阵不同,前者的方差阵结构是完全的、严密的。同时,在进行GPS网平差时,选取GPS基线的方式及数量的不同,构造的网形也就不同,采用不同的数学模型其得到的平差结果和精度指标也不相同。文章先从理论上进行分析,后用示例进行比较论证。     

工程建筑沉降预测的非等间距灰色建模

介绍了传统灰色GM(1,1)模型建立的基本思路及其精度评定方法,着重探讨了非等间距数列的灰色预测建模问题,给出了详细的建模过程。并结合高层建筑沉降监测实例进行分析,取得了较好效果。     

论《GIS空间分析》课程建设

综述了GIS空间分析理论的相关知识面和知识点,分析了课程的教学适应性,提出了课程建设的几点建议.     

小波分析若干应用模型及在测绘中的应用和展望＊

小波分析是８０年代中期发展起来的应用数学理论,在众多学科领域取得了广泛的应用。文中首先对小波分析的发展作了简要概述,然后介绍了小波分析在测绘中的应用,对小波分析若干与测绘相关的应用模型进行了分析和讨论,并对小波分析在测绘中的应用前景作了展望。     

空间直角坐标和大地坐标的转换

目前国内在空间直角坐标和大地坐标转换公式使用问题上 ,还明显缺乏必要的统一性。针对这个问题 ,本文作者在全面分析和比较国内外现有各种转换方法的基础上 ,重点推荐和介绍根据 Bowring研究思路导出的转换公式 ,这组公式既简单又具有很高的计算精度 ,能够满足各个部门的使用要求     

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R² of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R² of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.