Textural identification of basaltic rock mass using image processing and neural network

Abstract

A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.