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Abstract
With the increasing mining depth in recent decades, the high in-situ stress and challenging environments in deep underground mines result in multiple mining hazards. Coal bursts and rockbursts are one of the most formidable mining hazards in underground mines, causing the dynamic failure of coal and/or rock mass and violent ejections of material into mine openings. After more than half a century of research, the mechanisms of coal bursts and rockbursts are not yet fully understood because of the large variability and uncertainty in the causal factors.
Seismic monitoring is the most popular technique to help forecast, prevent and control burst hazards. It uses seismic waves generated from coal and rock mass to locate internal damage, which provides a powerful means to detect dynamic rock failure and understand the burst damage mechanism. The dynamic impact from seismic waves is an essential cause of rock failure. However, as dynamic impacts in underground coal mines have been rarely studied, the triggering mechanism of seismic waves for coal bursts is poorly understood. Apart from that, due to the complex underground environment, the recorded seismic data may have high location errors and low data integrity, which significantly limits the accuracy of the seismic methods.
Therefore, this thesis investigated dynamic impacts of mining induced seismicity in underground mines and enhanced the seismic data quality in assessing the associated risks. Based on seismic data in a burst-prone coal mine in China, the research investigated the ground motion characteristics in the target longwall blocks. It is found that coal bursts are usually triggered by the dynamic impacts when the coal and rock mass are already under critical stress levels. The roadway zones that have experienced more intensive ground motions are more susceptible to coal bursts.
The characteristics of location error in the studied longwall were investigated, and a modified seismic clustering method was proposed to assess burst risks. The result revealed that location errors are highly anisotropic and vary along with the geophone movement. The proposed seismic clustering method that considers the influence of location errors had a strong correlation with coal burst damage.
The characteristics of seismic data integrity in the studied longwall were investigated by assessing the detection probabilities of the seismic monitoring system. Geophones had various capabilities to detect seismic events at different locations and energy magnitudes. Based on the detection probability results, a method was proposed to correct the integrity of seismic data, which shows more event counts and seismic energy release in front of the longwall face.
The concept of “reinforced seismic data” was proposed to correct location errors in the raw seismic data and improve data integrity. The relationship between the spatial variation of seismicity and burst risks was also investigated by using reinforced seismic data. It is found that seismic energy has a strong correlation with coal burst damage, which can be used as an essential precursor of impending burst hazards.
The outcome of this thesis can provide insights on the burst damage mechanism and evaluation of seismic data quality in underground coal mines. The proposed seismic methods identify burst risks in terms of ground motions, seismic clusters and variations of seismicity, which can be used individually or together to improve burst hazard forecasting.
