采矿与岩层控制工程学报2026，Vol.8Issue(4)：232-246,15.DOI:10.13532/j.jmsce.cn10-1638/td.2025-1231
基于多钻进参数综合指标的岩性分类智能识别方法
Rock type identification based on comprehensive identification indexes of multiple drilling parameters
摘要
Abstract
To address the issues of low real-time responsiveness and limited detection range in traditional coal roadway roof type detection,a rock type identification method based on measurement while drilling(MWD)technology was proposed in this study.Firstly,three comprehensive indexes for rock type identification,namely rock-breaking energy,logistic regression analysis,and rock hardness,were established based on drilling parameters.Secondly,a probability classification model and a random forest model were established,and the model parameters were obtained by macro-average ROC analysis.Finally,intelligent rock type identification was achieved through both the probabilistic classification model and the random forest model,which were driven by the multi-parameter comprehensive indexes.With the aid of a self-developed laboratory MWD platform,MWD experiments were conducted on spliced specimens composed of different rock types to validate the reliability of the proposed intelligent identification method.The results demonstrate that the three comprehensive indexes outperform single drilling parameters in rock type identification.The random forest model,utilizing these comprehensive indexes,accurately identifies the splicing interfaces,rock layer thicknesses,and rock types in the spliced specimens,achieving a prediction accuracy of 92.9%.Additionally,the precision,recall,and F1-score of the model all exceed 88.7%.关键词
钻进参数/破岩能量/逻辑回归/岩石硬度/概率分类Key words
drilling parameters/rock-breaking energy/logistic regression/rock hardness/probability classification分类
矿业与冶金引用本文复制引用
周星源,岳中文,马文彪,刘伟,胡昊,龙思晨..基于多钻进参数综合指标的岩性分类智能识别方法[J].采矿与岩层控制工程学报,2026,8(4):232-246,15.基金项目
国家重点研发计划资助项目(2021YFC2902103) （2021YFC2902103）
