Automatic rock classification of LIBS combined with 1DCNN based on an improved Bayesian optimization

To achieve automated rock classification and improve classification accuracy, this work discusses an investigation of the combination of laser-induced breakdown spectroscopy (LIBS) and the use of one-dimensional convolutional neural networks (1DCNNs). As a result, in this paper, an improved Bayesian optimization (BO) algorithm has been proposed where the algorithm has been applied to automatic rock classification, using LIBS and 1DCNN to improve the efficiency of rock structure analysis being carried out. Compared to other algorithms, the improved BO method discussed here allows for a reduction of the modeling time by about 65% and can achieve 99.33% and 99.00% for the validation and test sets of 1DCNN.(c) 2022 Optica Publishing Group

Automated summary

In this work, an investigation of the combination of laser-induced breakdown spectroscopy (LIBS) and one-dimensional convolutional neural networks (1DCNNs) is presented for automated rock classification and improved classification accuracy. An improved Bayesian optimization (BO) algorithm is proposed and applied to automatic rock classification using LIBS and 1DCNN, leading to a reduction in modeling time by about 65%, and achieving 99.33% and 99.00% accuracy for the validation and test sets of 1DCNN.