Benxi area, Northeastern China, is the main distribution area of Archean BIF-hosted iron deposits in China. In this area, Nanfen iron deposit is well known as the largest open-pit iron deposit not only in China but al...Benxi area, Northeastern China, is the main distribution area of Archean BIF-hosted iron deposits in China. In this area, Nanfen iron deposit is well known as the largest open-pit iron deposit not only in China but also in Asia. So far, the tectonic nature during Archean BIF formation period in Benxi area has been long disputed and the tectonic setting of Nanfen BIF had not been found. In this study, the geochemical characters of chlorite amphibolites closely associated with BIF have been investigated for the tectonic environment of Nanfen BIF. Chlortie amphibolites show the geochemical affinity to the back-arc basin basalt (BABB), indicating that the tectonic environment of Nanfen BIF is the back-arc basin. In conjunction with geological evidence of other BIFs at Benxi area, it is identified that BIF in Benxi area might be formed in the subduction-related back-arc basin, which provides a favorable sedimentary environment of Algoma-type BIF.展开更多
A key stage for Kriging interpolation is the estimating of variogram model, which characterizes the spatial behavior of the variables of interest. But most traditional kriging interpolation has finite types of empiric...A key stage for Kriging interpolation is the estimating of variogram model, which characterizes the spatial behavior of the variables of interest. But most traditional kriging interpolation has finite types of empirical variogram model, and sometimes, the optimal type of variogram model can not be find, which result in decreasing interpolation accuracy. In this paper, we explore the use of Multi-Gene Genetic Programming (MGGP) to automatically find an empirical variogram model that fits on an experimental variogram. Empirical variogram estimation based on MGGP, in contrast with traditional method need not select type of basic variogram model and can directly get both the functional type as well as the coefficients of the optimal variogram. The results of case study show that the proposed method can avoid the subjectivity in choosing the type of variogram models and can adaptively fit variogram according to the real data structure, which improves the interpolation accuracy of kriging significantly.展开更多
文摘Benxi area, Northeastern China, is the main distribution area of Archean BIF-hosted iron deposits in China. In this area, Nanfen iron deposit is well known as the largest open-pit iron deposit not only in China but also in Asia. So far, the tectonic nature during Archean BIF formation period in Benxi area has been long disputed and the tectonic setting of Nanfen BIF had not been found. In this study, the geochemical characters of chlorite amphibolites closely associated with BIF have been investigated for the tectonic environment of Nanfen BIF. Chlortie amphibolites show the geochemical affinity to the back-arc basin basalt (BABB), indicating that the tectonic environment of Nanfen BIF is the back-arc basin. In conjunction with geological evidence of other BIFs at Benxi area, it is identified that BIF in Benxi area might be formed in the subduction-related back-arc basin, which provides a favorable sedimentary environment of Algoma-type BIF.
文摘A key stage for Kriging interpolation is the estimating of variogram model, which characterizes the spatial behavior of the variables of interest. But most traditional kriging interpolation has finite types of empirical variogram model, and sometimes, the optimal type of variogram model can not be find, which result in decreasing interpolation accuracy. In this paper, we explore the use of Multi-Gene Genetic Programming (MGGP) to automatically find an empirical variogram model that fits on an experimental variogram. Empirical variogram estimation based on MGGP, in contrast with traditional method need not select type of basic variogram model and can directly get both the functional type as well as the coefficients of the optimal variogram. The results of case study show that the proposed method can avoid the subjectivity in choosing the type of variogram models and can adaptively fit variogram according to the real data structure, which improves the interpolation accuracy of kriging significantly.
