近几十年我国极端气温变化特征分区方法探讨

采用聚类统计检验分析和旋转主分量分析相结合确定中心站的方法,利用我国多年极端气温资料,对我国最高和最低气温年际变化型态进行区划。结果表明,这两种方法结合可以互相补充,使分区结果更具客观性。中国极端高温和极端低温年际变化分别可划为12和11个不同类型的区域,分别计算了各区域第一主成分的方差贡献率以及各区域之间的两两相关系数,检验证明分区是合理的。     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.