Petrographic Thin Section Image Classification Based on Deep Convolutional Network with Hybrid Attention Mechanism

Abstract The classification of petrographic thin section image holds significant importance in the fields of geology and petroleum exploration. In light of the issue that the feature information of current petrographic thin section image recognition algorithms is prone to loss, this paper utilizes deep learning technologies and proposes the adoption of the Inception-ResNet-V2 network as the backbone network. When compared to various classical classification algorithms, the model proposed in this paper demonstrates a considerable advantage in classification accuracy on the petrographic thin section image dataset, achieving an impressive accuracy rate of 95.89%. The classification of each image category was analyzed by confusion matrix, and the model was visualized using Grad-CAM to observe the distribution of classification weights and the changes of classification weights. The results indicate that the model can extract crucial feature information from the petrographic thin section image, effectively enhancing the accuracy of petrographic thin section image recognition.