摘要
Abstract
Because of the complexity and uncertainty of mineral geological information, it is a difficult task to model the distribution of mineral recourses by using parametric model. Non-linear modeling techniques such as artificial neural network (ANN), support vector machine (SVM) etc. provide a promising mean to handle such kind of information, without acquiring exact relationship between mineral geological information and mineral deposits. In this non-linear approach, all mineral deposits and non-deposits are trained/validated/tested in a "black box"-like classifier. Although a high predict accuracy can be reached if the classifier is trained properly, it is still hard to obtain classify rules, which indicate the preferable metallogenic factors from geological information because of the non-linear structural characteristic the classifier. In this paper, a technique called support vector machine based recursive feature elimination, or SVM-RFE is used to rank all input features in SVM. An experiment of SVM-RFE is conducted on gold prospectivity analysis in southeast Yunnan shows this technique could improve the predict accuracy from 68. 42％ to 94.74％ by shrinking 10 input features to 6. The preferable rank of all 10 features calculated by SVMRFE is Au abnormity, As abnormity, intrusive rock, parallel unconformity between Lower and Middle Triassic system, parallel unconformity between Upper Permian and Triassic system, density of faults intersection, parallel unconformity between Carboniferous and Permian system, parallel unconformity between Devonian and Carboniferous system, Sb abnormity, Hg abnormity; and the former 6 features give the best predict accuracy. This rank benefits to selection and understanding metallogenic factors in this study area.关键词
特征选择/支持向量机/迭代特征消去/金矿/滇东南Key words
feature selection/ support vector machine(SVM) / Recursive Feature Elimination(RFE)/ gold deposit/southeast Yunnan分类
天文与地球科学
