摘要
为提高测井岩性分类的精度和效率,基于不同岩性在测井响应特征中的差异,笔者利用随机森林算法对测井数据进行岩性自动分类识别,并对不同变量在岩性分类中的重要性予以分析。以松辽盆地大庆长垣南端某铀矿矿区钻孔数据为例,识别研究区钻孔数据的岩性类别,随机森林算法的预测准确率达88.67%,电阻率和自然电位在岩性分类中的重要性较高。结果表明,相较于支持向量机方法,随机森林算法的预测准确率更高,是一种高效且准确的岩性分类方法。
In order to improve the precision and efficiency of logging lithology classification,based on the difference of lithology in log response and using random forest algorithm,the authors automatically classify and identify the lithology from the logging data.In addition,the importance of different variables from the lithology classification is analyzed.Taking the logging data of a uranium mining area at the southern end of Daqing placanticline in Songliao Basin as an example,the lithology of the logging data in the study area is identified.The prediction accuracy of random forest algorithm is 88.67%.Resistivity and spontaneous potential data show more importance in lithology classification.The results indicate that random forest algorithm is an efficient and accurate lithology classification method with higher prediction accuracy,compared with support vector machine method.
作者
康乾坤
路来君
KANG Qian-kun;LU Lai-jun(College of Earth Sciences,Jilin University,Changchun 130061,China)
出处
《世界地质》
CAS
2020年第2期398-405,共8页
World Geology
基金
中国地质科学院项目(3S2170034422)。
关键词
岩性分类
随机森林算法
测井数据
lithology classification
random forest algorithm
logging data
