基于实例克隆的ICSMOreg算法及在铀矿床蚀变矿物水云母中的物谱建模研究

摘要

Hydromica is a typical alteration mineral in granite-type uranium deposit, and also an important indication of uranium.The amount of hydromica to some extent reflects the strength of hydromicasization in uranium deposit. Because of the bad performance of the traditional modelling methods in prediction, in the present paper, the authors’ adopt SMOreg in the spectral modelling for hydromica, and validate its effectiveness. The authors' also propose a novel method called ICSMOreg. In this method the authors' employ instance cloned method to learn the samples selected by having a strong affinity with the test sets,and then get the new samples into SMOreg to build the spectral model. Finally, we experimentally compare ICSMOreg with SMOreg, artificial neural network, model tree and the common modelling methods like linear regression, multiple linear regression. The result shows that the new method improves the accuracy of prediction, and also reduces the negative impact of noise.%水云母足花岗岩型铀矿床蚀变带中的一种典型蚀变矿物,它也是铀矿找矿的一个重要标志.水云母含量的大小能在一定程度上体现铀矿床水云母化的强弱.传统建模方法对水云母含量的预测效果较差.文章将回归支持向量机SMOreg应用到水云母物谱关联建模中,并在验证其有效性的基础上提出一种基于实例克隆的ICSMOreg方法,以构建水云母含量与光谱特征参数的关联模型.该方法首先选择与待测样本亲和度较强的部分样本,运用实例克隆的方法对其进行克隆学习,再将得到的新样本数据输入SMOreg,建立水云母的物谱关联模型.最后将文章提出的算法与SMOreg算法、人工神经网络、模型树及常用的高光谱物谱关联模型中的一元线性回归、多元线性回归的预测结果相比较,表明提出的算法预测结果精度高于现有算法,且通过克隆与待测样本亲和度强的样本降低了无用信息在预测过程中所造成的负面影响.