Automated Rock Quality Designation Using Convolutional Neural Networks

Abstract Mineral and hydrocarbon exploration relies heavily on geological geotechnical information extracted from drill cores. Traditional drill-core characterization is based purely the subjective expertise of a geologist. New technologies can provide automatic mineral analysis high-resolution core images in non-destructive manner. However, automated rock mass presents significant challenge due to its lack generalization robustness. To date, estimation quality designation (RQD), key parameter for classification, mostly digital image processing techniques with user biases. Alternatively, we propose using computer vision machine learning-based algorithms determine RQD. A convolutional neural network (CNN) used detect classify intact non-intact cores, filter out empty tray areas non-rock objects present trays. The model calculates length detected cores estimates We train CNN thousands sandstone different holes South Australia. proposed method tested 540 rows 90 limestone (~ 1 m each), which produces average error rates 2.58% 3.17%, respectively.