The quantitative classification of granites and their metallogenetic relations in West Qinling,Gansu Province,China

The quantitative classification of granite and their metallogenetic relations have never been discussed.The Q-system clustering analysis and discriminant analysis methods were alternately used to quantitatively analyze the 11 oxide data in granite samples from the West Qinling area of Gansu Province,and then to construct the quantitative classification series models of granite(oxide).The granites samples are divided into three categories and eight subcategories.The classification of granites is biased toward prospecting.According to the spatial correlation between eight types of granites and copper deposits,lead and zinc deposits,gold deposits,etc.(within 3 km of the intrusion)in the West Qinling area in Gansu Province,the“metallogenic related intrusions”are sought,and the prospecting target areas are defined.Furthermore,they provide reliable basis for regional geological prospecting.

Application of self-organizing neural networks to classification of plant communities in Pangquangou Nature Reserve, North China

Vegetation classification is an important topic in plant ecology and many quantitative techniques for classification have been developed in the field.The artificial neural network is a comparatively new tool for data analysis.The self-organizing feature map(SOFM)is powerful tool for clustering analysis.SOFM has been applied to many research fields and it was applied to the classification of plant communities in the Pangquangou Nature Reserve in the present work.Pangquangou Nature Reserve,located at 37°20′–38°20′ N,110°18′–111°18′ E,is a part of the Luliang Mountain range.Eighty-nine samples(quadrats)of 10 m×10 m for forest,4 m×4 m for shrubland and 1 m×1 m for grassland along an elevation gradient,were set up and species data was recorded in each sample.After discussion of the mathematical algorism,clustering technique and the procedure of SOFM,the classification was carried out by using NNTool box in MATLAB(6.5).As a result,the 89 samples were clustered into 13 groups representing 13 types of plant communities.The characteristics of each community were described.The result of SOFM classification was identical to the result of fuzzy c-mean clustering and consistent with the distribution patterns of vegetation in the study area and shows significant ecological meanings.This suggests that SOFM may clearly describe the ecological relationships between plant communities and it is a very effective quantitative technique in plant ecology research.