Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for the quantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future. In this study, we explored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrument containing a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra are produced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. We constructed a convolutional neural network (CNN) regression model with unified parameters for all oxides, which is efficient and concise. CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models. Firstly, we explored the effects of four activation functions on the performance of the CNN model. The results show that the CNN model ...

中文翻译：

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基于卷积神经网络的激光诱导击穿光谱的ChemCam光谱数据定量分析模型

激光诱导击穿光谱法（LIBS）已应用于许多领域，用于各种材料的定量分析。提高LIBS回归模型的预测精度对于近期的火星探测仍具有重要意义。在这项研究中，我们探索了对一维ChemCam（包含LIBS光谱仪和Remote Micro-Imager的仪器）的LIBS定量分析的光谱数据，该光谱数据是由ChemCam团队使用LIBS在类似火星的大气条件下产生的。我们针对所有氧化物建立了具有统一参数的卷积神经网络（CNN）回归模型，该模型高效且简洁。具有出色特征提取能力的CNN可以有效克服阻碍回归模型预测准确性的化学基质效应。首先，我们探讨了四个激活函数对CNN模型性能的影响。结果表明，CNN模型...