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

Area of Science:

• Geotechnical Engineering
• Mining Engineering
• Geological Engineering

Background:

• Discontinuities in coal mine roadway roofs, like interfaces and fractures, cause surrounding rock instability.
• Measurement While Drilling (MWD) is emerging for geological formation identification.
• Current rock structure recognition methods lack accuracy, efficiency, and generalizability.

Purpose of the Study:

• To develop a high-accuracy, efficient, and generalizable rock structure recognition method for coal mine roadways.
• To leverage drilling parameters and advanced algorithms for improved geological perception.

Main Methods:

• Acquired drilling parameters: thrust, torque, modulation specific energy (SEM), and rock drillability assessment (RDA).
• Developed a change point detection model using the Bayes algorithm for drilling parameters.
• Proposed a multi-parameter fusion criterion for rock structure identification.

Main Results:

• Single rock interface identification errors: thrust (13.3 mm), torque (4.6 mm), SEM (4.4 mm), RDA (18.3 mm).
• Multiple rock interface recognition rates: thrust (83.3%), torque (100.0%), SEM (66.7%), RDA (83.3%).
• The signal-to-noise ratio (SLP) magnitude index was highest at interfaces, with an optimal threshold of ±0.2 for multi-change-point detection.

Conclusions:

• The proposed Bayes algorithm-based method effectively identifies rock structures using drilling parameters.
• The multi-parameter fusion criterion enhances recognition accuracy for complex geological conditions.
• This research advances intelligent perception of rock structures, crucial for effective rock mass control in mining.