地质通报2024，Vol.43Issue(6)：938-946,9.DOI:10.12097/gbc.2022.10.002
基于MobileViT的岩石薄片图像岩性识别方法研究
Lithology identification method of rock thin section images based on MobileViT
摘要
Abstract
The rock thin-section images contain a large amount of geological feature information that cannot be observed with the naked eye.The lithology identification of rock thin-section images lays the foundation for subsequent oil exploration and production.Aiming at the problems of unbalanced lithology identification data set and many identification model parameters,an improved lightweight MobileViT model is proposed to model and analyze the rock slice images covering more than 90%of common lithology.First,to enable the model to better learn the unique features contained in each type of rock slice image,adding numbers of the dataset set is performed on the image.Secondly,use GELU to replace the ReLU6 of the MV2 module in MobileViT as the activation function of the module,which effectively solves the problem of neuron death and improves the convergence speed of the model.Finally,the training set and the test set are divided,the cosine annealing algorithm is used to automatically update the learning rate,and the transfer learning is used to speed up the training process,so as to realize the automatic identification of rock slice images.The experimental results show that the accuracy of the improved MobileViT for lithology identification is 82.8%,and the model parameters are only 7.66M,which has good robustness.关键词
岩石薄片/岩性识别/MobileViT/余弦退火/轻量化Key words
rock thin section/MobileViT/lithology identification/cosine annealing/light weight分类
天文与地球科学引用本文复制引用
王琼,杨杰,霍凤财,董宏丽,任伟建,于涛..基于MobileViT的岩石薄片图像岩性识别方法研究[J].地质通报,2024,43(6):938-946,9.基金项目
黑龙江省自然科学基金项目《基于分布式算法和多源异构井筒数据驱动的页岩储层有利区评价研究》(编号:LH2023F007) （编号:LH2023F007）
