CAJ | 학술논문

针对现有条件下的岩屑录井中岩屑识别率低、识别速度慢等问题，从特征提取和分类器方面对岩屑岩性识别进行了分析研究.采用二级分类器的思想，首先通过颜色特征和和差直方图特征采用朴素贝叶斯分类器将岩屑粗分为泥岩和砂岩，然后进一步采用贝叶斯分类器，通过颜色特征和和差直方图特征分别将泥岩和砂岩进行进一步的细分.实验结果表明，粗分的识别率、泥岩细分的识别率和砂岩细分的识别率分别能达到94.79%、97.59%和90.28%.这种识别方法更加符合现实的应用需求，有着更高的识别率，为岩屑岩性分析工作提供了可靠的依据.
침대현유조건하적암설록정중암설식별솔저、식별속도만등문제，종특정제취화분류기방면대암설암성식별진행료분석연구.채용이급분류기적사상，수선통과안색특정화화차직방도특정채용박소패협사분류기장암설조분위니암화사암，연후진일보채용패협사분류기，통과안색특정화화차직방도특정분별장니암화사암진행진일보적세분.실험결과표명，조분적식별솔、니암세분적식별솔화사암세분적식별솔분별능체도94.79%、97.59%화90.28%.저충식별방법경가부합현실적응용수구，유착경고적식별솔，위암설암성분석공작제공료가고적의거.
In current cutting logging,low recognition rate and identifying slow are the main existing problems.In order to solve these problems,some studies about cuttings identification has been made from the feature extraction and classification.Within the idea of the two categories,first via color and the Sum and Difference Histograms feature,the cuttings will be roughly divided into mudstone and sandstone by Native Bayes classifier.Then the mudstone and sandstone independent via color and the Sum and Difference Histogram feature subdivided with classifier.The experimental show that the accu-racy about 94.79% for roughly division and 97.59% for mudstone subdivision,90.28% for sandstone subdivision.This identification method is suitable for the application requirements and has a higher rec-ognition rate,and it will provide a reliable basis for the further cuttings identification.