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<p>Discontinuous structures in coal mine roadway roofs, such as rock interfaces and joint fractures, are critical factors leading to surrounding rock instability. The use of Measurement While Drilling (MWD) technology to identify geological formations has become a growing trend. However, there is still a lack of rock structure recognition methods that offer high accuracy, efficiency, and strong generalizability. Therefore, this study acquired four drilling parameters including thrust, torque, modulation specific energy (SEM), and rock drillability assessment (RDA) through drilling experiments. By leveraging the Bayes algorithm, which has high precision, efficiency, and low cost, a change point detection model for drilling parameters was established, and a multi-parameter fusion criterion was proposed for identifying rock structures. The results show that for single rock interface identification, the errors of thrust, torque, SEM, and RDA were 13.3&#xa0;mm, 4.6&#xa0;mm, 4.4&#xa0;mm, and 18.3&#xa0;mm, respectively. For multiple rock interface identification, the recognition rates were 83.3%, 100.0%, 66.7%, and 83.3%, respectively. Moreover, the absolute value of the magnitude index (SLP) at the interface location was generally the highest among all change points. In multi-change-point detection, the SLP threshold should be set at ± 0.2. It is worth noting that the SLP value is correlated with data fluctuation intensity; greater fluctuation leads to higher SLP values at change points. This study contributes significantly to enabling intelligent perception of rock structures and improving the quality of rock mass control.</p>

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A mutation detection method of discontinuous structures in rock strata based on drilling parameters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Xin Sun,
  • Jingyi Cheng,
  • Zhijun Wan,
  • Zhiqing Guo,
  • Shuruo Zhang,
  • Shuheng Wang

摘要

Discontinuous structures in coal mine roadway roofs, such as rock interfaces and joint fractures, are critical factors leading to surrounding rock instability. The use of Measurement While Drilling (MWD) technology to identify geological formations has become a growing trend. However, there is still a lack of rock structure recognition methods that offer high accuracy, efficiency, and strong generalizability. Therefore, this study acquired four drilling parameters including thrust, torque, modulation specific energy (SEM), and rock drillability assessment (RDA) through drilling experiments. By leveraging the Bayes algorithm, which has high precision, efficiency, and low cost, a change point detection model for drilling parameters was established, and a multi-parameter fusion criterion was proposed for identifying rock structures. The results show that for single rock interface identification, the errors of thrust, torque, SEM, and RDA were 13.3 mm, 4.6 mm, 4.4 mm, and 18.3 mm, respectively. For multiple rock interface identification, the recognition rates were 83.3%, 100.0%, 66.7%, and 83.3%, respectively. Moreover, the absolute value of the magnitude index (SLP) at the interface location was generally the highest among all change points. In multi-change-point detection, the SLP threshold should be set at ± 0.2. It is worth noting that the SLP value is correlated with data fluctuation intensity; greater fluctuation leads to higher SLP values at change points. This study contributes significantly to enabling intelligent perception of rock structures and improving the quality of rock mass control.