Lithology Recognition of Rock Image Based on Convolutional Neural ResNet50 Residual Network

Deep learning convolutional neural network algorithm is widely used in the lithology identification of rock images. A rock image lithology recognition model was constructed by combining the convolutional neural residual ResNet50 network, and the parameters of the network model were optimized and verified according to the defined loss function. At the same time, the lithology of the rock image was predicted by the constructed recognition model, and the error causes were analyzed according to the prediction results. The research showed that based on the deep convolutional neural ResNet50 residual network, the lithology prediction model can be constructed and the parameters can be optimized according to the ratio of the training set, test set, and verification set 8: 1: 1, to realize the lithology prediction of rock image. Combined with the project example, the rock image lithology identification model of four kinds of lithology, such as biotite granodiorite, metamorphic sandstone, quartzite, and biotite granite, is constructed. The recognition accuracy of the model is generally up to 75%~90%, except for the fractured rock mass with structural joints. The accuracy of rock image prediction results is greatly affected by the development of rock mass structural fissures and the quality of rock images. The accuracy of prediction results can be improved by increasing the number of training samples.