Distributions of various compositions in granite specimen using fully convolutional network

The distributions of various compositions are the fundamental issues in studying the physical and mechanical properties of rock material. In this study, fully convolutional network (FCN) and the video images photographed during the uniaxial compression test were used to study the distribution of various compositions (including crack, quartz, feldspar and biotite) in the granite specimen. After converting into the grayscale image and cropping the specimen in each frame extracted from the video image, the compositions are labeled to make a ground data set using the naked eye judgment method. A corresponding FCN is then established and trained. The distributions of these compositions are furthermore examined by visualizing various convolutional layers. The evolution features and influencing factors (including network depth, initial learning rate, and iterations) of the recognitions during the whole deformation/failure process were investigated. It shows that during the total deformation/failure process of the granite specimen the crack initiated in the central region and penetrated through the surface longitudinally, the biotite dispersedly distributed and moved continuously to the upper left or upper right parts, the quartz concentrated mainly in the sides, while the feldspar concentrated mainly in the middle and upper left parts; the recognition accuracy of various compositions decreased slightly with a descending order of crack > biotite > feldspar > quartz; the recognition was better with a deeper FCN and a greater initial learning rate, the recognition was good if the iteration is set at 5000. The above results may be referable in studying the distribution features of various compositions in rock by using of artificial intelligence.