Lithology identification using borehole images by contrast-limited adaptive histogram equalization and machine learning models

Agile lithology identification can assist mining by providing important information in the exploration and production of mineral resources. This study proposes a new lithology recognition procedure using video-logging of boreholes with an endoscope, applied to six production blocks in a limestone quarry. Images are automatically extracted from the videos and the lithology is classified into three classes based on clay content, i.e. massive limestone, brecciated limestone, and high amount of clay. The image quality is evaluated with a gray pixel intensity threshold and three no-reference image quality metrics, i.e. perception-based image quality evaluator, natural image quality evaluator, and blind/referenceless image spatial quality evaluator. After removing low-quality images, 7583 images are retained and used for developing lithology classification models using six optimized classification techniques. The contrast-limited adaptive histogram equalization (CLAHE) technique is used to improve image quality. Ten color characteristics involving three percentiles of red, green and blue pixel intensities, together with color counting and five texture characteristics – correlation, entropy, homogeneity, contrast and energy – are used as inputs. Bayesian optimized light gradient boosting machine model performs best, with an overall accuracy of 88.04%, and a precision on the classes of massive limestone, brecciated limestone and high amount of clay of 90.72%, 83.52% and 85.29%, respectively, for the testing set. The feature importance scores show that the color counting is the most significant parameter for the development of the classification model. Compared with previous image-based methodologies, this study provides a more flexible and cheaper procedure to identify lithology.