RECOGNITION OF LITHOLOGY WITH BIG DATA OF GEOLOGICAL IMAGES

Abstract

In recent years, with the rapid development of big data, deep learning and other technologies, the development and utilization of big data have brought significant economic and social benefits to various industries. It is of great theoretical and social significances to carry out researches on data mining and application of geological texts, images and sequence data by means of big data technologies. This paper focuses on the geological image data processing, development of network models using the deep learning theory and lithology recognition through performing network-based data acquisition, data preprocessing, network construction, network training and result/evaluations. Results show that the recognition accuracy of lithology images is about 90%. Limited image data employed can be one of the reasons for deviations of recognitions. Positive correlation scores given by machine for some similar characteristics of the rock images, e.g., macroscopic shape and color, etc., also can lead to misjudgments in recognition. Theoretically, adoption of networks like BCNN(Bilinear Convolutional Neural Network) for capturing finer details and solving the problem of fine-grained recognition in computer vision and fundamentally improving the efficiency of image recognition, should be considered in future works.