Analysis on advanced transient EM detectability of coal mine roadway

The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to detect water damage in advance of roadway excavation.In this paper,the time-domain finite element algorithm based on unstructured tetrahedron grids is used to accurate-ly simulate the geological body in front of the roadway excavation face and analyze its response.The authors detect the distance between the roadway excavation face and the low-resistivity water-bearing body,the resistivity difference between the low-resistivity body and surrounding rock,and the influence of the size of the low-resistivity body on the transient EM response.Furthermore,the common types of low-resistivity bodies in the roadway drivage process are used for modeling to analyze the attenuation of the detected EM response when there are low-resistivity bodies in front of the roadway.The research in this paper can help effectively detecting the water-bearing low-resistivity body in front of the roadway drivage and lay a foundation for reducing the risk of water seepage accidents.

Numerical simulations of full-wave fi elds and analysis of channel wave characteristics in 3-D coal mine roadway models

Currently, numerical simulations of seismic channel waves for the advance detection of geological structures in coal mine roadways focus mainly on modeling two- dimensional wave fields and therefore cannot accurately simulate three-dimensional （3-D） full-wave fields or seismic records in a full-space observation system. In this study, we use the first-order velocity-stress staggered-grid finite difference algorithm to simulate 3-D full-wave fields with P-wave sources in front of coal mine roadways. We determine the three components of velocity Vx, Vy, and Vz for the same node in 3-D staggered-grid finite difference models by calculating the average value of Vy, and Vz of the nodes around the same node. We ascertain the wave patterns and their propagation characteristics in both symmetrical and asymmetric coal mine roadway models. Our simulation results indicate that the Rayleigh channel wave is stronger than the Love channel wave in front of the roadway face. The reflected Rayleigh waves from the roadway face are concentrated in the coal seam, release less energy to the roof and floor, and propagate for a longer distance. There are surface waves and refraction head waves around the roadway. In the seismic records, the Rayleigh wave energy is stronger than that of the Love channel wave along coal walls of the roadway, and the interference of the head waves and surface waves with the Rayleigh channel wave is weaker than with the Love channel wave. It is thus difficult to identify the Love channel wave in the seismic records. Increasing the depth of the receivers in the coal walls can effectively weaken the interference of surface waves with the Rayleigh channel wave, but cannot weaken the interference of surface waves with the Love channel wave. Our research results also suggest that the Love channel wave, which is often used to detect geological structures in coal mine stopes, is not suitable for detecting geological structures in front of coal mine roadways. Instead, the Rayleigh channel wave can be used for the advance detection of geological structures in coal mine roadways.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.