长江大学学报（自然科学版）2024，Vol.21Issue(5)：47-55,9.
电成像测井中基于GA-RF的火山岩岩性识别
GA-RF-based lithologic identification of volcanic rocks in electrical imaging logging
摘要
Abstract
Aiming at the problem that it is difficult to accurately identify the lithology of complex volcanic rocks using conventional logging data,this paper proposes a GA-RF(genetic algorithm-random forest)based method for volcanic rock lithology identification using electric imaging logging.Firstly,the four texture features of energy,contrast,correlation and homogeneity of the electric imaging logging image are extracted by grey level co-occurrence matrix(GLCM)method,and the three texture features of roughness,contrast and orientation of the image are extracted by the Tamura method,and the texture feature dataset is established;then,the feature dataset is subjected to feature fusion,dimensionality reduction,and the feature samples are balanced by the ADASYN over-sampling algorithm,which reduces the impact of sample imbalance on the algorithm.imbalance on the algorithm;finally,the parameters of Random Forest algorithm are optimized by genetic algorithm to construction of volcanic rock Lithology identification model based on GA-RF(hereinafter referred to as GA-RF model)and compare it with the three algorithms of Random Forest,GBDT and LightGBM.The results of instance analysis show that the accuracy of GA-RF model can reach about 94％,which is much higher than the three comparison algorithms.The method effectively improves the accuracy and speed of volcanic rock lithology recognition,which can provide a reference for the sample imbalance problem as well as the lithology recognition by logging methods.关键词
电成像测井/火山岩/灰度共生矩阵/岩性识别Key words
electrical imaging logging/volcanic rock/grayscale symbiosis matrix/lithology identification分类
能源科技引用本文复制引用
张翔,曾鑫,肖小玲..电成像测井中基于GA-RF的火山岩岩性识别[J].长江大学学报（自然科学版）,2024,21(5):47-55,9.基金项目
国家自然科学基金项目"复杂地质背景下电成像测井层理面检测与产状快速提取方法研究"(41374148). （41374148）
