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Automatic rock classification of LIBS 1 combined with 1DCNN based on improved 2 Bayesian optimization

Song, G., Zhu, S., Zhang, W. , Hu, B., Zhu, F., Zhang, H., Sun, T. ORCID: 0000-0003-3861-8933 & Grattan, K. T. V. ORCID: 0000-0003-2250-3832 (2022). Automatic rock classification of LIBS 1 combined with 1DCNN based on improved 2 Bayesian optimization. Applied Optics, 61(35), pp. 10603-10614. doi: 10.1364/AO.472220


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 (1DCNN). As a result, in this paper, an improved Bayesian optimization 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 carried out. Compared to other algorithms, the improved Bayesian optimization method discussed here allows for a reduction of the modelling time by about 65% and can achieve 99.33% and 99.00% for the validation and test sets of 1DCNN.

Publication Type: Article
Additional Information: © the authors, 2022. Optica Publishing Group. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Engineering
SWORD Depositor:
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