Intelligent prediction of lithology and uniaxial compressive strength of rock based on multi-directional while-drilling vibration signals
LUAN Hengjie1, LIU Junchi1, JIANG Yujing1, 2*, LIU Jiankang1, LIU Shangming3, LIU Ziyi1, YANG Yuqing1, GONG Zhibo1
(1. State Key Laboratory of Disaster Prevention and Ecology Protection in Open-pit Coal Mines, Shandong University of Science and Technology, Qingdao, Shandong 266590, China; 2. School of Engineering, Nagasaki University, Nagasaki 8528521, Japan; 3. Xin'an Coal Mine of Zaozhuang Mining Group Co., Ltd., Jining, Shandong 272000, China)
Abstract：To improve the accuracy of lithology identification and uniaxial compressive strength (UCS) prediction based on drilling vibration signals and to elucidate the effects of multidirectional vibration signals and lithological differences, this study investigates coal, sandstone, granite, and marble. Triaxial vibration and acoustic pressure signals generated during core drilling were acquired using a core drilling machine and a dynamic signal acquisition system, from which time- and frequency-domain features were extracted. A backpropagation (BP) neural network was then developed for lithology identification and UCS prediction. The results show that the combined X-, Y-, and Z-axis vibration and acoustic pressure signals provide the best overall predictive performance. Specifically, the maximum accuracy of lithology identification on the test set reaches 93%, while UCS prediction yields a coefficient of determination of 0.76, a root mean square error of 7.61 MPa, and a mean absolute percentage error of 15.56%. Vibration signals perpendicular to the drilling direction provide significantly higher prediction accuracy than those parallel to the drilling direction, while multidirectional signals further improve predictive performance through information complementarity. Among the individual lithologies, granite exhibits the highest UCS prediction accuracy. In addition, the prediction accuracy for soft-hard rock combinations, such as coal-granite and coal-marble, is significantly improved. These findings reveal the relationship between multidirectional signals and lithological combinations, thereby providing technical support for the engineering application of measurement-while-drilling technology.
栾恒杰1，刘骏驰1，蒋宇静1，2*，刘建康1，刘尚明3，刘子怡1，杨玉晴1，宫智博1. 基于多向随钻振动信号的岩石特性智能预测研究[J]. 岩石力学与工程学报, 2026, 45(11): 3218-3235.
LUAN Hengjie1, LIU Junchi1, JIANG Yujing1, 2*, LIU Jiankang1, LIU Shangming3, LIU Ziyi1, YANG Yuqing1, GONG Zhibo1. Intelligent prediction of lithology and uniaxial compressive strength of rock based on multi-directional while-drilling vibration signals. , 2026, 45(11): 3218-3235.
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2026, Vol. 45 
