水平门限同积模型参数的拟极大似然估计

提出了用于非平稳非线性时间序列建模的水平门限同积模型,给出了模型参数的拟极大似然估计,由于对门限参数和同积向量似然函数既不可微也不光滑,不能直接运用传统的极大似然估计．因此首先利用遗传模拟退火算法估计门限参数和同积向量,然后用极大似然估计计算其余的参数,仿真结果表明,拟极大似然估计不受模型维数限制具有有效性和可行性,此外,数值计算结果的比较分析表明遗传模拟退火优于传统的遗传算法、模拟退火和随机搜索等优化算法．

Field analysis of metallogenic information and its application

Focusing on the issue to deal with inadequate extraction of metallogenic information especially geological information,a new method of extracting metallogenic information based on field model,i.e.the field analysis method of metallogenic information,was proposed.In addition,a case study by using the method of the extraction of metallogenic information from the west Guangxi and southeast Yunnan district as an example was performed.The representation method for the field models of metallogenic information,including the metallogenic influence field model and the metallogenic distance field model,was discussed by introducing the concept of the field theory,based on the characteristic analysis of the distance gradualness and the influence superposition of metallogenic information.According to the field theory superposition principle and the spatial distance analysis method,the mathematical models for the metallogenic influence field and the metallogenic distance field of point,line and area geological bodies were derived out by using parameter equation and calculus.Based on the metallogenic background analysis,the metallogenic information field models of synsedimentary faults and manganese sedimentary basins were built.The relationship between the metallogenic information fields and the manganese mineralization distribution was also investigated by using the method of metallogenic information field analysis.The instance study indicates that the proposed method of metallogenic information field analysis is valid and useful for extracting the ore-controlling information of synsedimentary faults and manganese sedimentary basins in the study area,with which the extraction results are significant both statistically and geologically.

Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging

In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.