Acta Photonica Sinica, Volume. 48, Issue 10, 1030001(2019)

Data Denoising Method for Rock Identification Based on LIBS Technology

WANG Chong1,*... ZHANG Xiao-mo1, ZHU Xiang-ping2,3, LUO Wen-feng1 and SHAN Juan3 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    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

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    Paper Information

    Received: Apr. 30, 2019

    Accepted: --

    Published Online: Nov. 14, 2019

    The Author Email: Chong WANG (cw72@xupt.edu.cn)

    DOI:10.3788/gzxb20194810.1030001

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