Intelligent On-Site Lithology Identification Based on Deep Learning of Rock Images and Elemental Data

To reduce the disadvantages of factors such as limited expression of 2-D images, weathering, or man-made pollution on image-based methods and realize rapid and accurate on-site lithology identification, we propose an intelligent lithology identification method based on the deep learning of rock images and element information. The initial lithology probabilities and locations are first determined from the images using an object detection model. Then, the initial lithology probabilities and element data are used as an input to correct the lithology probabilities using self-constructed fusion model. Finally, all lithology detection results are integrated and redundant results are removed. The verification experiment shows that the accuracy of the proposed method is 13.73% higher than that of the image-based method. The proposed method improves the performance of lithology identification by improving the classification of lithology and influencing the choice of bounding box. The proposed fusion identification method is a real-time and accurate method for the identification of complex lithology and provides a new idea for the rapid and intelligent identification of on-site lithology.