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激光诱导击穿光谱技术(LIBS)可用来测量固体样品中各类微量元素的组成及含量。使用 532nm 波长、5ns 脉宽、100μm束斑直径、脉冲能量 30m J 的激光对 USGS（United States Geological Surve，美国地质勘探局）系列地质标样进行剥蚀，得到了不同地质标样剥蚀物的等离子发射光谱。通过对光谱图的分析处理，并使用人工神经网络对不同地质标样铁元素含量进行测定，最终神经网络预测得到 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%，实验表明 LIBS 结合人工神经网络可以很好的测定 USGS 系列地质标样的铁元素含量。
Laser Induced Breakdown Spectroscopy (LIBS) can be used to measure the composition and content of various trace elements in solid samples. The USGS geological series samples were denuded using 532nm wavelength, 5ns pulse width, 100μm beam spot diameter and pulse energy of 30mJ. The plasma emission from different geological samples was obtained spectrum. Through the analysis and processing of the spectrogram, the content of iron in different geological samples was determined by using artificial neural network. The final neural network predicted BCR-1G, BHVO-2G, BIR-1G, GSD-1G and GSE- The relative errors of iron content and standard content were 1.86%, 5.73%, 0.27%, 3.86% and 2.63%, respectively. The experimental results showed that the LIBS combined with artificial neural network can be used to determine the iron content of the USGS geological series samples well.