华南石炭纪岩关-大塘界线期牙形石地层分带

系统采集不同沉积相区和连续沉积的剖面，即浅海台地相广西柳江龙殿山剖面、台缘缓坡相云南施甸鱼硐剖面和台内盆地相广西柳州碰冲剖面，运用数理统计方法，研究了华南岩关一大塘界线过渡期牙形石的产出丰度和相分布，在恢复生物演化序列的基础上，对各剖面首先详细分带，再进一步综合成系统的5个牙形石带，即Gnathodus typicus—G．cuneiformis带、Gnathodus typicus—Protognathodus cordiformis带、Scaliognathus anchoralis—Gnathodus pseu-dosemiglaber带、Gnathodus praebilineatus带和Paragnathodus horrtopunctatus带。以生物系统发生为标准，严格限定各带的界线，并与北美、西欧同期地层进行精确对比，为建立杜内阶一维宪阶界线的全球层型(GSSP)提供可靠依据。     

基于光谱特征的月球岩性分类方法研究——以Apollo 16登月区域为例

利用NASA行星数据系统提供Apollo计划登月点采样线路影像数据,通过与嫦娥二号CCD数据、印度M~3数据空间校正获得采样路线坐标。开展嫦娥二号CCD数据与印度M~3数据MAP(后验概率)融合并选择Apollo 15、Apollo 16-62231的LSCC测得的标准岩石双向反射率光谱与M~3、嫦娥二号进行交叉定标。本文采用月球岩石光谱谱型全特征分析方法,选取涵盖Apollo计划登月获取的36个基站主要岩性87种、285件岩石样品,利用校正后的M~3数据分析月球典型岩石各阶吸收反射特征,建立月球典型岩石标准遥感影像光谱库,此后应用Apollo 623个岩石样品进行对比得到很好结果,同时完成Apollo 16登月点周围领域岩性分布图,并讨论了研究区的岩石成因,Apollo 16区域形成于高地大撞击,在早期的研究中已经被用于划分月球年代,本文方法对于月球岩石类别研究与理解月球的岩浆演化具有重要的研究价值。     

A Bayesian approach to the detection of gross errors based on posterior probability

Existing methods for gross error detection, based on the mean shift model or the variance inflation model, have hardly considered or taken advantage of the potential prior information on the unknown parameters. This paper puts forward a Bayesian approach for gross error detection when prior information on the unknown parameters is available. Firstly, based on the basic principle of Bayesian statistical inference, the Bayesian method—posterior probability method—for the detection of gross errors is established. Secondly, considering either non-informative priors or normal-gamma priors on the unknown parameters, the computational formula of the posterior probability is given for both the mean shift model and the variance inflation model, respectively, under the condition of unequal weight and independent observations. Finally, as an example, a triangulation network is computed and analyzed, which shows that the method given here is feasible.     

龙门山南段二叠系热液白云岩特征

二叠系白云岩与白云质灰岩、灰岩具有渐变过渡关系。白云岩中斑马状构造发育。白云石多为马鞍状白云石或异形白云石,较围岩显示出低碳、低氧和富锶特征,与典型构造热液成因白云岩一致,强度受构造控制明显,从造山带向盆地方向强度逐渐减弱。     

Bayesian Hierarchical ANOVA of Regional Climate-Change Projections from NARCCAP Phase II

We consider current (1971–2000) and future (2041–2070) average seasonal surface temperature fields from two regional climate models (RCMs) driven by the same atmosphere–ocean general circulation model (GCM) in the North American Regional Climate Change Assessment Program (NARCCAP) Phase II experiment. We analyze the difference between future and current temperature fields for each RCM and include the factor of season, the factor of RCM, and their interaction in a two-way ANOVA model. Noticing that classical ANOVA approaches do not account for spatial dependence, we assume that the main effects and interactions are spatial processes that follow the Spatial Random Effects (SRE) model. This enables us to model the spatial variability through fixed spatial basis functions, and the computations associated with an ANOVA of high-resolution RCM outputs can be carried out without having to resort to approximations. We call the resulting model a spatial two-way ANOVA model. We implement it in a Bayesian framework, and we investigate the variability of climate-change projections over seasons, RCMs, and their interactions. We find that projected temperatures in North America are credibly higher, that the associated warming effects differ in spatial areas and in seasons, and that they are of much larger magnitude than the variability between RCMs.     

抗差贝叶斯估计及应用

当未知参数具有先验期望和方差，且观测值与未知参数先验值均服从正态分布时，最小二乘贝叶斯估计将给出参数的最优解。然而当观测值和参数先验值的实际分布有悖于正态假设时，经典贝叶斯估计使估值偏高。本文基于常用的M估计原理，对三种类型的误差模式，导出了M－LS、LS－M和M－M三种抗差贝叶斯估计解式和影响函数；讨论了相应的计算方法；给出了参数验后方差表达式。     

Statistical fusion of two-scale images of porous media

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.     

思茅民乐宋家坡铜矿地质及找矿标志

宋家坡铜矿属火山-次火山热液交代次生富集型铜矿床。对矿床地质特征、控矿因素等方面的研究，提出岩石、构造、蚀变、物化探等找矿标志。     

赫尔默特方差分量估计在混合水准网平差中的应用

测量中有些时候会碰到定权不统一的问题,导致了平差后的结果不理想.本文主要讨论的是通过赫尔默特方差估计的方法消除由于定权的不合理而产生的精度超限问题.通过实例进行分析表明,利用赫尔默特方差分量估计在进行迭代定权非常实用,大大提高了其精度.     

云南东川岔河铁矿成因探讨

岔河铁矿产于构造破碎带中,似层状产出,围岩和矿体中石英、方解石脉发育,矿床成因属浅海一滨海相沉积变质型褐铁矿床.