Simultaneous determination of lithology and major elements in rocks using laser-induced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network †
Accurate lithological recognition and quantitative determination of multiple chemical elements have a wide market application prospect in geological and geochemical exploration. In recent years, with the development of machine learning, laser induced breakdown spectroscopy (LIBS) coupled with various chemometric methods has demonstrated great advantages to solve both classification and quantitative problems in rock analysis. However, these methods are usually applied for single-task analysis. To further increase the analysis efficiency, LIBS combined with a deep convolutional neural network with a two-dimensional (2D-CNN) algorithm is proposed here for simultaneous determination of lithology and seven major chemical elements in rock samples from six different types. The structure of our CNN model was designed with two different outputs, which could complete classification and regression tasks at the same time. Based on the prediction results of the test set, the experimental results show that the classification accuracy of the CNN model could reach 99.26% the coefficient of determination (R2) of target elements could reach a good level (RSi2 = 0.9917, RAl2 = 0.9935, RCa2 = 0.9906, RMg2 = 0.9926, RFe2 = 0.9716, RNa2 = 0.9693, RK2 = 0.8781), and both the mean absolute error (MAE) of each test sample and prediction relative error of each element are improved. To verify the superior performance of this CNN model, the CNN algorithm was compared with other models, including k-nearest neighbor (kNN), support vector machine (SVM), partial least squares discrimination analysis (PLS-DA), back propagation artificial neural network (BP-ANN) and partial least squares regression (PLSR). The results indicate that the CNN has great potential in the field of rock quantitative and qualitative analyses and provides a reliable data processing method for LIBS to complete multi-task analysis with high efficiency and good accuracy.