Particle composition analysis based on rock slice orthogonal polarization sequence diagram

Abstract:

Recognition of quartz and feldspar is meaningful for reservoir research. Traditional mineral composition analysis mainly relies on the man-machine interactive identification, which brings enormous work as well as low efficiency. Considering above problems, an effective dividing method is proposed in this paper based on rock particles' texture characteristics under orthogonal polarizer. The gradient information of sample images is firstly extracted using Sobel operator. And Gray Level Co-occurrence Matrix(GLCM) energy and relevance of each gradient image are calculated as the characteristic parameters sample library. Then apply Artificial Neural Network(ANN) classification methods to training. Based on the training data，the particles is identified according to the characteristic parameters. At last, polarized sequence diagram is used to decide the final recognition result. The experimental results indicate that the method of recognizing quartz and feldspar achieves good effect.