|
|
|
| A MINERAL RESOURCE POTENTIAL MAPPING MODEL BESED ON RBF NEURAL NETWORKS |
| DAI Liming 1,2,CHEN Yongliang 3,LIU Xin 1,2,ZHOU Juntai 1,2,ZHAO Fengmei 1,2,SUO Yanhui 1,2,GAO Wubin 4,LOU Da 5 |
| 1. College of Marine Geosciences, Ocean University of China, Qingdao266100, China；2. Key Lab of Submarine Geosciences and Prospecting Techniques，Ministry of Education, Qingdao266100, China; 3. College of Earth Sciences, Jilin University, Changchun130026, China；4. Troops 57015, Chinese People's Liberation Army, Beijing100082, China；5. Dagang OilGas Company, CNPC, Dagang300280, China |
|
|
|
|
Abstract 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 threestep procedure is needed as follows: the first step is to get training samples from the study area; the second step is to abstract the structure of spatial information of training samples and then to construct a RBF networks; the last step is to generate the distributive map of mineral resource potentials. In this paper, the model was employed to predict multimetallogenetic prospecting targets in the area from Duolanasayi to Ashele in northern Xinjiang. The predicted targets by the model were compared with the CF model. The two model results are very similar to each other, suggesting that the new model is effective and practical.
|
|
Received: 09 January 2010
Published: 10 February 2011
|
|
|
|
|
|
|
|
|
