Journal of China University of Geosciences最新文献

Located in the Qinling molybdenum metallogenic belt on the southern margin of North China craton, the Nannihu molybdenum (-tungsten) ore field, consisting of the Nannihu, Sandaozhuang, and Shangfang deposits, represents a superlarge skarn-porphyry molybdenum (-tungsten) accumulation. Outside the ore field, there are some hydrothermal lead-zinc-silver deposits found in recent years, for example, the Lengshuibeigou, Yindonggou, Yangshuwa, and Yinhegou deposits. Ore-forming fluid geochemistry indicates that these deposits belong to the same metallogenic system. The hydrothermal solutions were mainly derived from primary magmatic water in the early stage and from the mixture of the primary magmatic water and meteoric water in the later stage, with an obvious decreasing tendency in temperature, salinity and gas-liquid ratio of fluid inclusions. Sulfur and lead isotope data show that the ore-forming substances and related porphyries were mainly derived from the lower crust, and a hidden magmatic chamber is indicated by aeromagnetic anomaly and drill hole data indicate that the Nannihu granite body extends to being larger and larger with depth increasing. The large-scale mineralization was the consequence of lithospheric extension during the late stage of the tectonic regime when the main compressional stress changed from NS-trending to EW-trending.

Weights of evidence (WofE) is an artificial intelligent method for integration of information from diverse sources for predictive purpose in supporting decision making. This method has been commonly used to predict point events by integrating point training layer and binary or ternary evidential layers (multiclass evidence less commonly used). Omnibus weights of evidence integrates fuzzy training layer and diverse evidential layers. This method provides new features in comparison with the ordinary WofE method. This new method has been implemented in a geographic information system-geophysical data analysis system and the method includes the following contents: (1) dual fuzzy weights of evidence (DFWofE), in which training layer and evidential layers can be treated as fuzzy sets. DFWofE can be used to predict not only point events but also area or line events. In this model a fuzzy training layer can be defined based on point, line, and areas using fuzzy membership function; and (2) degree-of-exploration model for WofE is implemented through building a degree of exploration map. This method can be used to assess possible spatial correlations between the degree of exploration and potential evidential layers. Importantly, it would also make it possible to estimate undiscovered resources, if the degree of exploration map is combined with other models that predict where such resources are most likely to occur. These methods and relevant systems were validated using a case study of mineral potential prediction in Gejiu mineral district, Yunnan, China.

The separation of anomalies from geochemical background is an important part of data analysis because lack of such identifications might have profound influence on or even distort the final analysis results. In this article, 1 672 geochemical analytical data of 11 elements, including Cu, Mo, Ag, Sn, and others, from a region within Tibet, South China, are used as one example. Together with the traditional anomaly recognition method of using the iterative mean ±2σ, local multifractality theory has been utilized to delineate the ranges of geochemical anomalies of the elements. To different degrees, on the basis of original data mapping, C-A fractal analysis and singularity exponents, Sn differs from the other 10 elements. Moreover, geochemical mapping results based on values of the multifractal asymmetry index for all elements delineate the highly anomalous area. Similar to other 10 elements, the anomalous areas of Sn delineated by the asymmetry index distribute along the main structure orientations. According to the asymmetry indexes, the 11 elements could be classified into 3 groups: (1) Ag and Au, (2) As-Sb-Cu-Pb-Zn-Mo, and (3) Sn-Bi-W. This paragenetic association of elements can be used to interpret possible origins of mineralization, which is in agreement with petrological analysis and field survey results.