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...

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Bibliographic Details
Published in:Applied optics (2004) 2022-12, Vol.61 (35), p.10603
Main Authors: Song, Guangdong, Zhu, Shengen, Zhang, Wenhao, Hu, Binxin, Zhu, Feng, Zhang, Hua, Sun, Tong, Grattan, Kenneth Tv
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Bayesian analysis
Classification
Laser induced breakdown spectroscopy
Optimization
Structural analysis
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Summary: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.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.472220