激光诱导击穿光谱在地质录井岩性快速识别中的应用  

Fast Lithology Identification by Laser-Induced Breakdown Spectroscopy

作　　者：陈兴龙[1,2] 董凤忠[2,3] 陶国强[4] 李油建[4] 佘明军[4] 付洪波[2] 倪志波[2] 王静鸽[2] 贺文干 汤玉泉[2] 饶瑞中[1,2] 

机构地区：[1]合肥工业大学仪器科学与光电工程学院,安徽合肥230009 [2]中国科学院安徽光学精密机械研究所,安徽合肥230031 [3]中国科学技术大学环境科学与光电技术学院,安徽合肥230026 [4]中国石油化工集团公司中原石油工程有限公司录井公司,河南濮阳457001

出　　处：《中国激光》2013年第12期237-242,共6页Chinese Journal of Lasers

基　　金：国家自然科学基金(11075184);中国石化集团科技攻关项目(P12084)

摘　　要：激光诱导击穿光谱(LIBS)已经被证明是极具潜力的物质定性、定量分析工具之一。将激光诱导击穿光谱结合自组织映射神经网络技术,引入到石油勘探录井领域,对五类岩心样品(火山灰岩、泥岩、页岩、砂岩、白云岩)进行了岩性自动分类,为以后在录井现场实现岩性在线快速识别奠定基础。使用构造特征变量和主成分分析两种方法对原始光谱进行特征提取,相应的特征参量和主成分分别作为自组织映射神经网络的输入变量。两种输入方式下,神经网络对全部44块岩心样品岩性分类的准确率分别为75%和86%。其中以主成分作为网络输入变量,对火山灰岩、砂岩、白云岩的分类准确率可达100%。实验分析表明:在进一步提高对泥岩和页岩的区分能力后,LIBS有望成为录井领域新的岩性快速识别技术。Laser-induced breakdown spectroscopy （LIBS） has demonstrated its high potential in both qualitative analysis and quantitative analysis. LIBS combined with self-organizing mapping （SOM） neural network is applied in oil prospecting industry. Some rock core samples, including ash rock, mudstone, shale, sandstone and dolostone, are automatically classified to lay a foundation for lithology on-line identification. Characteristic variables and principal components, which are obtained by feature extraction from raw spectra, are used as inputs of the SOM neural network, respectively. Classification accuracy is 75% and 86% for the two kinds of inputs, respectively. Particularly, all of ash rocks, sandstone and dolostone are the input. The experimental results indicate that LIBS classified correctly when principal components are used as will be capable of fast identification of lithology after improving the classification accuracy of mudstone and shale.

关 键 词：激光光学 激光诱导击穿光谱 自组织映射 特征变量 主成分分析 岩性分类 

分 类 号：O433.4[机械工程—光学工程] TE19[理学—光学]