基于RBF神经网络的矿产资源潜力预测模型

摘要

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: 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 multi-metallogeuetie prospecting targets in the area from Duolanasayi to Ashele in 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.%提出一种基于RBF神经网络的矿产资源潜力制图模型.应用该模型生成矿产资源潜力分布图分三步完成:第一步,以找矿标志的空间分布图和已知矿点空间分布图为依据,提取训练样本;第二步,根据训练样本构建RBF矿产资源潜力制图模型;第三步,生成矿产资源潜力分布图.笔者以新疆北部阿尔泰多金属成矿带为研究区,比较了该模型与合成有矿可信度等模型的找矿靶区圈定结果.两种模型的靶区圈定结果基本相同,证明了RBF矿产资源潜力制图模型的有效性.