激光诱导击穿光谱的自组织特征映射结合相关判别对天然地质样品分类方法研究

激光诱导击穿光谱技术具有微损、原位、快速分析的特点，在样品分类识别、成分分析等领域有广阔的应用前景。为探索该技术在天然地质样品识别应用的可行性，提出了一种自组织特征映射神经网络结合相关判别对天然地质样品LIBS光谱分类识别的方法。为减小全谱中背景噪声等不相关数据干扰、降低计算量，在元素谱线归属的基础上进行了特征谱线提取，实现了高维光谱数据的降维。以特征谱数据为输入建立网络训练模型，得到具有输入样本特征的权向量，通过权向量与待测样本进行相关分析可以实现样品分类。对16种天然地质样品的分类算法实验证明，在全谱、主成分降维和特征谱段三种数据处理方法中，特征谱的降维和提取LIBS数据主特征效果最优。改进的SOM网络结合相关判别算法比支持向量机方法和直接应用SOM网络方法的分类准确度更高，初步证实了该方法的有效性。

Classification of Geological Samples with Laser-Induced Breakdown Spectroscopy Based on Self-Organizing Feature Map Network and Correlation Discrimination Analysis

Laser-induced breakdown spectroscopy has the characteristics of small-invasive, in situ and rapid analysis. It has wide application prospects in the field of sample identification and component analysis. In order to explore the feasibility of the technology in the automatic identification of natural geological samples, a method of identifying and sorting LIBS spectral of natural geological samples by self-organizing feature map neural network combined with correlation is proposed in this paper. In order to reduce the interference of unrelated data such as background noise in the whole spectrum and the computational complexity, the feature spectral line is extracted on the basis of elemental to achieve the dimensionality reduction of high dimensional spectral data. The network training model is established by using the feature spectrum data as input, then the weight vectors which have the feature of input samples are obtained. Finally the geological sample classification is achieved by the correlation analysis between the weight vectors and the samples to be tested. The classification results of the 16 kinds of natural geological samples prove that the feature spectrum is superior to full spectrum and PCA dimension reduction, especially in the aspects of descending dimension and extracting the main features of data. The algorithm proposed in this paper has a better classification effect on the feature spectrum data of 16 samples than SVM and SOM neural network algorithm. Moreover, the validity of the proposed method is initially verified in this paper.