Convolutional neural network as a novel classification approach for laser-induced breakdown spectroscopy applications in lithological recognition

Abstract Rapid and accurate identification of multiple types of rocks using spectroscopic techniques has a wide market application prospect and is always challenging due to similar chemical composition and complex matrix effects. In recent years, laser induced breakdown spectroscopy (LIBS) coupled with supervised machine learning and chemometrics methods (e.g. k-nearest neighbor (kNN), support vector machine (SVM), partial least squares (PLS), artificial neural network (ANN)) and combined with feature engineering techniques (e.g. principal component analysis (PCA)), has demonstrated great capabilities for efficient identification of materials with similar chemical composition. To further increase the classification accuracy, LIBS coupled with a convolutional neural network with two-dimensional input (2D CNN) is here investigated for the identification of rock samples, including dolomites, granites, limestones, mudstones and shales. A regularized network structure was first designed, according to the performance of validation dataset, to enable the most reliable discrimination of the rock specimens. The accuracy of test dataset was then evaluated by the determined model. Results indicated that validation and test set of the 2D CNN was able to reach an accuracy of 0.9877 and 1, respectively. Finally, the performance was compared with other identification methods, including: one-dimensional convolutional neural network (1D CNN), kNN, PCA-kNN, SVM, PCA-SVM, PLS-DA, and Human-Assisted ANN (HA-ANN). The proposed approach has demonstrated that CNN has a great potential for the lithological identification and could be a feasible and useful tool for LIBS spectral data processing.