基于ConvNext V2-VMamba模型的岩石薄片岩性识别方法OA

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.