基于激光诱导击穿光谱技术的岩石表面指纹图谱分析及分类方法

岩石岩性识别在油气田探测开发、研究地球成因及演化发展、地质灾害分析预测等众多方面起着不可替代的导向作用，因此岩石的识别分类对于地质勘探分析来说至关重要。为了提高岩石的分类准确率，提出了一种基于激光诱导击穿光谱技术(LIBS)的岩石表面指纹图谱分析及分类方法。通过LIBS对岩石表面不同位置进行激发，获取原始光谱数据。对收集到的光谱数据进行去除异常点、归一化等预处理操作，根据岩石矿物成分确定五种含量差异较大元素(硅、铝、钾、钠、镁)的特征谱线并得到元素指纹图谱。然后选择支持向量机(SVM)作为分类器进行分类，分别建立利用光谱均值的分类模型和多维指纹图谱融合的分类模型，并对两种分类结果进行比较。利用光谱均值的分类模型准确率为59. 4%，多维指纹图谱融合的模型分类准确率为96. 5%。实验结果表明，元素指纹图谱展示了岩石表面元素分布，可以充分利用不同种类岩石本身的不均匀性结构信息，极大地提高了岩石的分类准确率。

Fingerprint analysis and classification of rock surface based on laser- induced breakdown spectroscopy

Rock lithology identification plays an irreplaceable guiding role in many aspects, such as exploration and development of oil and gas fields, study of the origin and evolution of the earth, analysis and prediction of geological hazards etc. Therefore, rock identification and classification are very important for geological exploration and analysis. In order to improve the classification accuracy of rocks, a method of rock surface fingerprint analysis and classification based on laser- induced breakdown spectroscopy (LIBS) was proposed. In the experiment, six rock samples were placed on a three- dimensional displacement platform, and different positions of the rock surface were excited by LIBS to obtain the original spectral data. After removing abnormal points, normalization and other pretreatment operations on the collected spectral data, the characteristic spectral lines of five elements (silicon, aluminum, potassium, sodium and magnesium) with large content differences were determined according to the rock mineral composition, and the element fingerprint was obtained. Then, the support vector machine (SVM) was selected as the classifier for classification. The classification model using the spectral mean and the classification model of multi- dimensional fingerprint fusion were established respectively, and the two classification results were compared. The accuracy of traditional classification model based on spectral mean is 59. 4%, while that of multi- dimensional fingerprint fusion model can reach 96.5%. The experimental results show that the element fingerprint shows the element distribution on the rock surface, which can make full use of the heterogeneous structure information of different kinds of rocks, thus greatly improving the classification accuracy of rocks.