Applying the geology big data and artificial intelligence technology to the quantitative evaluation of mineral resources and metallogenic prediction systems has improved the effective information mining of massive geological data and made up for the deficiencies of traditional methods. Based on the basic geological data and geophysical and geochemical prospecting data of the Baixiangshan mining area, this paper used the three-dimensional geological modeling techniques and three-dimensional spatial analysis techniques to build a 3D geological body model and quantify three-dimensional ore-controlling factors, establishing a three-dimensional mineralization prediction model based on the CART algorithm. The experiments in the Baixiangshan mining area show that the model can better localize known orebodies and predict a higher probability of mineralization in the northern, eastern, northeastern, western, southern and southeastern regions of the known orebodies. Those areas can be used to define the prospecting targets. This model that applying the geology data to prospecting and the exploration work has the advantages of pure data drive, high prediction accuracy, and reliable prediction results. At the same time the study has found that the prediction effect of the model is related to the number of training data sets, extraction of mining control factors, and decision tree depth.
Li Zhihui Zhao Ping Li Xiaohui Yuan Feng Zhou Yuzhang. 3D metallogenic prognosis based on CART algorithm: A case study of Baixiangshan mining area in Anhui Province[J]. Chinese Journal of Geology, 2018, 53(4): 1314-1326.