石油地球物理勘探2026，Vol.61Issue(3)：595-606,12.DOI:10.13810/j.cnki.issn.1000-7210.20250273
基于ConvNext V2-VMamba模型的岩石薄片岩性识别方法
Lithology identification method for rock thin sections based on ConvNext V2-VMamba
摘要
Abstract
Rock fabric contains abundant geological information,and lithology identification from rock thin sec-tions is of great importance for oil and gas exploration,mineral extraction,and related fields.To address issues such as low accuracy and high labor costs in rock image classification,this paper proposes a ConvNext V2-VMamba-based method for lithology identification in rock thin sections.First,based on the ConvNext V2 model,grouped convolution and channel shuffle strategies are adopted to combine the ConvNext module with the VSS module.Then,a Conv-VMamba module is proposed to replace the original ConvNext module,enabling the model to possess both a global receptive field and excellent local feature extraction capabilities.Finally,a spatial attention EMA module is integrated into the model to enhance cross-channel spatial information aggrega-tion and improve the capture of texture information in different rock images.Experimental results show that the model achieves an accuracy of 81.5%,precision of 81.1%,recall of 81.4%,specificity of 96.3%,and F1-score of 81.2%on the test set.Compared with the original model,the accuracy is improved by 5%.This method dem-onstrates the highest algorithmic accuracy and best classification performance,providing a new approach and method for the field of lithology identification.关键词
岩性识别/岩石图像分类/ConvNext V2/VMamaba/通道混洗/空间注意力EMA模块Key words
lithology identification/rock image classification/ConvNext V2/VMamba/channel shuffle/spatial attention EMA module分类
天文与地球科学引用本文复制引用
王婷婷,熊东宇,赵万春,蔡萌,史晓东..基于ConvNext V2-VMamba模型的岩石薄片岩性识别方法[J].石油地球物理勘探,2026,61(3):595-606,12.基金项目
本项研究受国家自然科学基金项目"页岩层系组构单元划分与力学耦合竞争控制水力压裂造缝行为研究"(52474036)、"页岩油藏脉动水力压裂缝网实时扩展AE演化机制分数阶方法研究"(52174022)、"页岩油层交替混合压裂诱导及竞争耦合造缝与参数调控机制研究"(52074088)和黑龙江省自然科学基金联合基金重点项目"古龙页岩油SC-CO2立体井网压裂造缝与排采参数调控机制研究"(ZL2024E008)、黑龙江省"揭榜挂帅"科技攻关项目"古龙页岩油油藏工程理论、CO2提产机理及合理排采制度研究"(DQYT-2022-JS-758)、2024年度科技创新领军人才团队——页岩油地质工程一体化压裂与防控技术创新团队(CYCX24015)联合资助. （52474036）
