Shale porosity and pyrite intelligent identification based on improved Unet neural network

Currently, AI for data image processing has been widely studied in various fields of geology; such research has been greatly improved in terms of timeliness and accuracy, but no AI application with good results has been proposed for the recognition of pores and pyrite in shale images imaged by electron microscopy. To address the problem of intelligent recognition of pores and pyrite in shale images imaged by scanning electron microscopy, this paper proposes to apply Resunet, a novel convolutional neural network combining Resnet and Unet, to the intelligent recognition of pores and pyrite in shale images, and at the same time train the Resunet model through the dataset, evaluate and test the model, and obtain the resU-net semantic segmentation model The correct rate of recognizing pyrite and shale pores reaches 96.42%; the experimental results show that resU-net has the highest recognition accuracy compared to other models in the task of recognizing pyrite and shale pores on scanning electron microscope images, and this study effectively improves the problem of intelligent recognition of pores and pyrite in shale images imaged by electron microscope.