激光与光电子学进展, 2017, 54 (5): 053003, 网络出版: 2017-05-03   

激光诱导击穿光谱结合人工神经网络测定地质标样中的铁含量 下载: 551次

Quantitative Measurement of Iron Content in Geological Standard Samples by Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network
作者单位
中国地质大学(武汉)数学与物理学院, 湖北 武汉 430074
摘要
运用激光诱导击穿光谱(LIBS)技术得到了美国地质勘探局(USGS)系列地质标样的等离子发射光谱。使用人工神经网络测定了不同地质标样的铁元素含量, 测得BCR-1G、BHVO-2G、BIR-1G、GSD-1G、GSE-1G标样的铁元素含量与标准含量的相对误差分别为1.86%、5.73%、0.27%、3.86%、2.63%, 表明LIBS结合人工神经网络可以很好地测定USGS系列地质标样的铁元素含量。
Abstract
By using the laser-induced breakdown spectroscopy (LIBS) technique, the plasma emission spectra of the series of geological standard samples from United States Geological Survey (USGS) are obtained. By using the artificial neural network, the content of Fe in different USGS geological standard samples is measured. The relative error between the measured content and standard content of BCR-1G, BHVO-2G, BIR-1G, GSD-1G, and GSE-1G is 1.86%, 5.73%, 0.27%, 3.86% and 2.63%, respectively, which shows that LIBS combined with artificial neural network can measure the Fe content well in the series of geological standard samples from USGS.

胡杨, 李子涵, 吕涛. 激光诱导击穿光谱结合人工神经网络测定地质标样中的铁含量[J]. 激光与光电子学进展, 2017, 54(5): 053003. Hu Yang, Li Zihan, Lü Tao. Quantitative Measurement of Iron Content in Geological Standard Samples by Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network[J]. Laser & Optoelectronics Progress, 2017, 54(5): 053003.

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