石油地球物理勘探2025，Vol.60Issue(6)：1376-1385,10.DOI:10.13810/j.cnki.issn.1000-7210.20240519
基于半监督的SEM图像孔隙分割网络
Semi-supervised pore segmentation network for SEM images
摘要
Abstract
Accurately segmenting pores in scanning electron microscope(SEM)images can provide a scientific basis for oil and gas exploration and development,and more.At present,pore segmentation methods mainly rely on data-driven approaches,requiring a large amount of manual annotation of data,which is time-consuming and costly.To this end,this paper proposes the semi-supervised pore segmentation network PoreSeg for SEM images.Firstly,a semi-supervised framework is constructed based on consistency regularization and pseudo la-beling.Secondly,a high-intensity combined perturbation strategy is introduced to enhance data diversity.Fi-nally,combined with the pore aware fusion(Pore-CutMix)method,the sparse pore information is fully utilized to improve the segmentation ability of the model for pores.Experimental results show that under the condition of equal labeled samples,PoreSeg improves the pore intersection over union(IoU)by 15.10%compared with the fully supervised network.At the same time,compared with existing semi-supervised methods,PoreSeg is more sensitive to pores and has higher segmentation accuracy.PoreSeg significantly reduces dependence on an-notated data while maintaining high accuracy,and has huge application potential.关键词
半监督学习/孔隙分割/深度学习/扫描电镜图像/油气勘探Key words
semi-supervised learning/pore segmentation/deep learning/scanning electron microscope images/oil and gas exploration分类
天文与地球科学引用本文复制引用
LIU Peigang,DONG Honghao,YANG Chaozhi,MA Jing,WANG Peijie,LI Zongmin..基于半监督的SEM图像孔隙分割网络[J].石油地球物理勘探,2025,60(6):1376-1385,10.基金项目
本项研究受国家自然科学基金项目"基于多视角联合的海底油气管道掩埋状态水声探测智能识别关键技术研究"(62471494)、国家重点研发计划项目"典型冰上项目多源数据智能分析系统"(2019YFF0301800)联合资助. （62471494）
