Deep ensemble learning for quantitative geological fracture analysis using borehole televiewer images

Abstract

In the geological survey of hydraulic engineering projects, the Borehole televiewer (BHTV) technique is applied widely. The BHTV images are usually interpreted by engineers. To improve the automatic level of geological images analysis and decrease the subjective influence of the engineers, a fracture quantitative approach based on deep ensemble learning is proposed. In the research, the BHTV images' segmentation using the ensemble model is the basis of the fracture quantitative analysis. Therefore, the accuracy of segmentation can not be ignored. In the experimental study, the different deep learning architectures, UNet, SegNet, and FRRN based on VGG and ResNet, are employed. The evaluation result shows that the IoU values of the single models are in the range of 83.96%–84.36%, while the ensemble model outperforms all of them with a mean IoU of 86.36% and ranks top-3 for the all testing images. Through comparison of the segmentation for the single image, it is evident that the ensemble model is effective for noises removal, which improves the accuracy and efficiency of fracture parameter quantitative analysis. Finally, the thinning method and sine function based on Hough Transform (HT) and the generalized least squares (GLS) are adopted to fit the fracture curve. The calculation result of the dip azimuth, dip angle, and mean thickness also validates the effectiveness of the ensemble model.