长江大学学报（自然科学版）2026，Vol.23Issue(1)：22-30,9.
基于LGWO-LightGBM的混合模型在火山岩测井岩性识别中的应用
Application of hybrid model based on LGWO-LightGBM in lithology identification of volcanic rock logging
摘要
Abstract
With the Bohai Sea's complex and varied volcanic rock types and absence of specialised logging data like imaging,the machine learning method in the context of big data offers a fresh solution to the logging lithology identification challenge.In order to improve the accuracy of lithology identification,a hybrid model of lithology identification of volcanic reservoirs embedded in the LightGBM algorithm of Lévy flight strategy(LFS)grew wolf optimizer(GWO)was proposed.With LightGBM serving as the foundation algorithm,the LGWO-LightGBM hybrid model employs LGWO to optimise the hyper-parameter optimisation procedure inside the LightGBM framework,finally producing the ideal hyper-parameter combinations that yield a better accuracy rate.The experimental results demonstrate that the model performs best when the following parameters are set:180,0.2,501,460 and 30 for the number of leaf nodes,maximum depth,number of leaf nodes,and minimum number of samples of leaf nodes,respectively.This results in a lithology identification accuracy of 93.9%,which is 7.2%higher than that of the LightGBM model(86.7%),and a shorter relative calculation run time.LightGBM solves the problem of covering a variety of hyperparameters that are difficult to determine,with the advantage of better recognition accuracy and operational efficiency when dealing with a large amount of data.The model not only improves the productivity of oil and gas exploration and development of volcanic reservoirs in the Bohai Sea waters,but it also serves as a reference for identifying the volcanic lithology of reservoirs under similar types of geological conditions.关键词
莱维飞行策略/灰狼算法/LightGBM/火山岩/岩性识别Key words
Lévy flight strategy/grey wolf optimizer/LightGBM/volcanic rock/lithological identification分类
能源科技引用本文复制引用
刘迪仁,张苗,张国强,曹军,任宏,李鸿儒,王志豪..基于LGWO-LightGBM的混合模型在火山岩测井岩性识别中的应用[J].长江大学学报（自然科学版）,2026,23(1):22-30,9.基金项目
国家重点研发计划项目"地下及井中地球物理勘探技术与装备"(2018YFC060330502). （2018YFC060330502）
