A novel geophysical method for fractures mapping and risk zones identification in a coalmine, Northeast, China

Abstract

A novel approach for mapping induced fractures and associated lineaments has been introduced based on microseismic data, and thus their contribution towards identification, and prediction of the risk zones which may lead to a collapse or rockburst in a coal mine. By taking Tashan Coal Mine (TCM) in Datong coalfield of China as an example, the real-time microseismic data was acquired for a period of 7 months. The microseismic data was processed using standard processing schemes followed by source location and parameters estimation. The proposed approach utilizes microseismic events, trace them to map the induced fractures, and based on their network to interpret associated lineaments. Based on the configuration, geometry, and network, thousands of fractures were identified which vary in length. Regional lineaments have been interpreted based on the orientation of these fractures. The fractures and lineaments distribution were further correlated with computed microseismic energy distribution. The spatial distribution of microseismic energy helped in identification of a geological discontinuity (fault) that clearly separates the study area into northern and southern parts. The reactivation of this fault may pose significant threats to mass stability, life, and property. The northern half is characterized by more and short length fractures while southern by prolonged and fewer fractures. The upper northern half is dominated by high energy (≥50, 000 J), while the lower part is characterized by variable energy pattern (22,000 to 40, 000 J). It is inferred from the energy-lineaments correlation figure, the northern half describes relatively stronger rock type in comparison to the southern part suggesting different rock deformational behavior. The fractures and lineaments converging towards a single point define risk zones where future collapse or rockburst can be expected. This identification of risk zones provides a strong basis for the real-time prediction of future catastrophic hazards in the coal mines. This study may benefit global underground engineering researchers for hazards prediction and establishing of the early warning systems.