Acta Photonica Sinica, Volume. 48, Issue 10, 1030001(2019)
Data Denoising Method for Rock Identification Based on LIBS Technology
There have been confront with a low identification accuracy problem due to the poor repeatability and high data residual value of laser-induced breakdown spectrum. In order to solve such problems, an distinguishing method of abnormal value based on Grubbs criterion (3δ-Grubbs) was proposed. The method can effectively replace the data of large residual values to reduce the probability of over-fitting in the classification recognition algorithm. Finally, by using three classification recognition algorithms: linear discriminant analysis, random forest classification and support vector machine, we identified the LIBS spectrum of rocks. Before the data noise reduces, the recognition accuracy of the three methods were: linear discriminant analysis 79.6%, random forest classification 75.2%, support vector machine 94.5%.After data noise is reduced,the recognition accuracy of the three methods is as follows: linear discriminant analysis 92%, random forest classification 97%, support vector machine 99.4%.
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WANG Chong, ZHANG Xiao-mo, ZHU Xiang-ping, LUO Wen-feng, SHAN Juan. Data Denoising Method for Rock Identification Based on LIBS Technology[J]. Acta Photonica Sinica, 2019, 48(10): 1030001
Received: Apr. 30, 2019
Accepted: --
Published Online: Nov. 14, 2019
The Author Email: Chong WANG (cw72@xupt.edu.cn)