Classification of 13 original rock samples by laser induced breakdown spectroscopy

Rock classification plays a very important role in geological research. In order to simulate rock classification under unmanned conditions, we selected 13 kinds of rock samples and obtained their classification from spectral information, without any pretreatment, by using laser-induced breakdown spectroscopy (LIBS). Firstly, we collected all the characteristic regions of each rock and used principal component analysis to reduce the dimension of each LIBS spectral signal, to improve the accuracy and speed of the classification algorithm. Secondly, three classification algorithms were used to classify dimension-reduced spectral data, namely linear discriminant analysis, random forest classification and support vector machine (SVM). At the same time, the classification results were evaluated by confusion matrix. The final average classification accuracy was 27%, 91% and 100%, respectively, showing that the SVM algorithm can be applied to the LIBS classification of rocks.