矿业科学学报2025，Vol.10Issue(1)：95-104,10.DOI:10.19606/j.cnki.jmst.2024932
基于随钻参数响应特征的巷道顶板岩层界面识别方法研究
Identification method of roof rock interface based on response characteristics of drilling parameters
摘要
Abstract
This paper investigates measurement while drilling for rock strata interface to achieve geolog-ical transparency,promote intelligence development,and address the limitations of traditional rock de-tection methods in coal mines,including slow speed,high costs,and poor accuracy.Firstly,this study proposes a laboratory-level drilling device for real-time data acquisition of displacement,revolutions per minute,torque,and sound pressure level during drilling the formation model poured in the laboratory.Secondly,an exponentially weighted loss function was used for automatic screening of anomalies in dis-placement data and obtained a penetration rate that better reflect variations in drilling.Then,the accu-racy of rock interface identification was analyzed using parameters such as penetration rate,revolution per minute,sound pressure level,and torque using the application of the change point detection algo-rithm,the strucchange model in RStudio software,and the decision tree algorithm.Finally,the per-formance of rock interface identification during drilling was evaluated under 2 typical geological condi-tions.Rresults indicate that the decision tree algorithm is the most effective method for quick and accu-rate identification of rock interfaces with penetration rate as the input parameter.On-site tests yielded 0.04m of average error in predicting the position of the coal rock interface.Yet,the method produces relatively low accuracy in rock interface identification of composite roof rock strata.关键词
巷道顶板/钻进参数/岩层界面随钻探测/煤岩界面/钻孔Key words
roadway roof/drilling parameters/rock strata interface/measurement while drilling/coal rock interface/borehole分类
矿业与冶金引用本文复制引用
李点尚,刘灿灿,王传兵,任波,任帅,康志鹏..基于随钻参数响应特征的巷道顶板岩层界面识别方法研究[J].矿业科学学报,2025,10(1):95-104,10.基金项目
深部煤炭安全开采与环境保护全国重点实验室开放基金(HNKY2024YB102) （HNKY2024YB102）
国家自然科学基金(52304107) （52304107）
中国博士后科学基金(2023M733761) （2023M733761）
江苏省卓越博士后项目(2023ZB191) （2023ZB191）
