人工神经网络在成矿预测中的应用

Application of artificial neural network to ore-forming prediction

成矿预测正从定性描述性预测向定量成矿预测转变,数理统计方法和技术逐渐引入地学研究。传统统计方法多假想包含地学现象的空间为均质,假定在一个尺度上的地学关系在另一个尺度上也是相同的,而在实际应用中这样的地质条件是不可能存在的。而非线性科学正具有不满足线性叠加原理的性质,因此将非线性科学如人工神经网络与成矿预测相结合是未来矿产资源预测的发展方向。采用Kohonen聚类模型和BP预测模型相结合的方法,对包古图金矿区1444个矿点的地球化学数据进行聚类分析并建立成矿预测模型,预测正确率为85.2%。该方法性能良好,具有一定的实际意义,为解决成矿预测提供了一种新的手段。

With the transformation of ore-forming prediction from qualitative to quantitative,mathematical statistical methods and techniques are introduced into this field in recent years.Traditional statistical methods are applied under the assumption that the geological phenomena are homogeneous and a geo-scale relationship is the same with the others.But those geological conditions don’t exist in the practical application.Therefore,applying the non-linear science,which is not satisfied with the nature of the principle of linear superposition,such as neural network to metallogenic prognosis becomes the development direction.This paper combines the Kohonen clustering model and the BP prediction model,performs cluster analysis of the geochemical data of 1,444 mineral points in BaoGutu,and establishes the ore-forming prediction model.The prediction accuracy is 85.2%.The result shows that the proposed method is effective and feasible,which provides a new means for solving the ore-forming prediction.