Meticulous evaluation of rock mass quality in mine engineering based on machine learning of core photos

In mining engineering, the geological drilling boreholes are used to obtain accurate reserves of mineral resources, and many core photos are gathered in this process. It has a practical engineering significance to get the structural information from those core photos in order to evaluate rock mass quality. However, the current manual method for geological borehole logging is inefficient, and the results are usually affected by subjective factors. A method for evaluation of rock mass quality is proposed using the Mask-RCNN deep learning instance segmentation network. Firstly, the core strips are cut from the core photos automatically, and the core segments longer than 10 cm are identified from those core strips, then the rock quality designation RQD is calculated. Finally, using the information of boreholes and the geological model, the ordinary Kriging method is employed to get a heterogenous RQD block model to achieve a meticulous evaluation of rock mass quality. The case study in Wushan Copper and Molybdenum Mine indicates that the machine learning method can accurately calculate the RQD from core photos, and the geostatistical method can effectively evaluate the rock mass quality. The results show that the rock mass quality evaluation based on deep learning is consistent with the actual situation, and the proposed method has a wide range of application prospects in mining engineering.