- 责任者：Dai Liming (College of Marine Geosciences ; Ocean University of China ; Qingdao 266100 ; China) ; Chen Yongliang
- 刊名：Geophysical and Geochemical Exploration
- 出版年：2011
- 卷期：35(1)
- 关键词：成矿预测 ; metallogenic prediction ; 数字制图 ; digital cartography ; 神经网络系统 ; neural network system
- 页码：p.103-108,112
- 图表：6 illus., 2 tables, 17 refs.
- 分类号：2100
- 出版序号：20120654
- issnNo：ISSN1000-8918
- isbnNo：CN11-1906/P
A new RBF neural networks model for mineral resource potential mapping is proposed in this paper. For the purpose of applying this new model, a three-step procedure is needed as follows: first step is to get training samples from the study area; second step is to abstract the structure of spatial information of training samples and then to construct a RBF networks; and last step is to generate the distributive map of mineral resource potentials. In the paper, the model was employed to predict multi-metallogenetic prospecting targets in the area from Duolanasayi to Ashele in the northern Xinjiang. The predicted targets by the model were compared with the C-F model. The two model results are very similar to each other, suggesting that the new model is effective and practical.