利用激光诱导击穿光谱技术进行原岩分类与识别存在可重复性差,数据残差值高等问题,导致其分类识别准确率较低.针对此问题,提出了一种基于格拉布斯准则法的异常值判别方法,该方法可以有效替换残差值较大的数据,从而降低分类识别算法过拟合的概率.使用线性判别分析法、随机森林分类法、支持向量机三种分类识别算法对岩石的LIBS光谱进行识别.在数据降噪前,三种方法的识别准确率为：线性判别分析法79.6%、随机森林分类法75.2%、支持向量机94.5%,而数据降噪后的识别准确率为：线性判别分析法 92%、随机森林分类法 97%、支持向量机99.4%.

There have been confront with a low identification accuracy problem due to the poor repeatability and high data residual value of laser-induced breakdown spectrum. In order to solve such problems, an distinguishing method of abnormal value based on Grubbs criterion (3δ-Grubbs) was proposed. The method can effectively replace the data of large residual values to reduce the probability of over-fitting in the classification recognition algorithm. Finally, by using three classification recognition algorithms: linear discriminant analysis, random forest classification and support vector machine, we identified the LIBS spectrum of rocks. Before the data noise reduces, the recognition accuracy of the three methods were: linear discriminant analysis 79.6%, random forest classification 75.2%, support vector machine 94.5%.After data noise is reduced,the recognition accuracy of the three methods is as follows: linear discriminant analysis 92%, random forest classification 97%, support vector machine 99.4%.