With increasing demands for coal resources, coal has been gradually mined in deep coal seams. Due to high gas content, pressure and in situ stress, deep coal seams show great risks of coal and gas outburst. Protective coal seam mining, as a safe and effective method for gas control, has been widely used in major coal-producing countries in the world. However, at present, the relevant problems, such as gas seepage characteristics and optimization of gas drainage borehole layout in protective coal seam mining have been rarely studied. Firstly, by combining with formulas for measuring and testing permeability of coal and rock mass in different stress regimes and failure modes in the laboratory, this study investigated stress–seepage coupling laws by using built-in language Fish of numerical simulation software FLAC3D. In addition, this research analyzed distribution characteristics of permeability in a protected coal seam in the process of protective coal seam mining. Secondly, the protected coal seam was divided into a zone with initial permeability, a zone with decreasing permeability, and permeability increasing zones 1 and 2 according to the changes of permeability. In these zones, permeability rises the most in the permeability increasing zone 2. Moreover, by taking Shaqu Coal Mine, Shanxi Province, China as an example, layout of gas drainage boreholes in the protected coal seam was optimized based on the above permeability-based zoning. Finally, numerical simulation and field application showed that gas drainage volume and concentration rise significantly after optimizing borehole layout. Therefore, when gas is drained through boreholes crossing coal seams during the protective coal seam mining in other coal mines, optimization of borehole layout in Shaqu Coal Mine has certain reference values.
Mining-induced tremors are indispensable events that gestate and trigger coal bursts. The radiated energy is usually considered a key index to assess coal burst risk of seismic events. This paper presents a model to assess coal burst risk of seismic events based on multiple seismic source parameters. By considering the distribution and relation laws of the seismic source parameters of coal bursts, the model aims to identify dangerous seismic events that more closely match the characteristics of multiple seismic source parameters of coal bursts. The new coal burst risk index T is proposed. It consists of the similarity index SI (representing the similarity degree of relations between seismic events and coal burst events based on seismic source parameters) and the strength index ST (representing the burst strength of seismic events). We studied 79 coal burst events that occurred during extraction in LW250105 of the Huating coal mine in Gansu Province, China. We obtained the distribution and relation laws of multiple seismic source parameters of coal burst events to establish SI and ST. Two groups of seismic events with different energy distributions were examined to compare the assessment results based on the new model and energy criteria. The results show that 80% and 89% of seismic events with strong coal burst risk in Groups A and B, respectively, were coincident, and the seismic events with medium coal burst risk were slightly less compared to those based on radiated energy. The results indicate that the assessment based on the T value is a modification and optimization of that based on radiated energy. This model is conducive to improving the efficiency of monitoring and early warning of coal burst risk.
Research on the diffusion characteristics of swells contributes positively to wave energy forecasting, swell monitoring, and early warning. In this work, the South Indian Ocean westerly index(SIWI) and Indian Ocean swell diffusion effect index(IOSDEI) are defined on the basis of the 45-year(September 1957–August 2002) ERA-40 wave reanalysis data from the European Centre for Medium-Range Weather Forecasts(ECMWF) to analyze the impact of the South Indian Ocean westerlies on the propagation of swell acreage. The following results were obtained: 1) The South Indian Ocean swell mainly propagates from southwest to northeast. The swell also spreads to the Arabian Sea upon reaching low-latitude waters. The 2.0-meter contour of the swell can reach northward to Sri Lankan waters. 2) The size of the IOSDEI is determined by the SIWI strength. The IOSDEI requires approximately 2–3.5 days to fully respond to the SIWI. The correlations between SIWI and IOSDEI show obvious seasonal differences, with the highest correlations found in December–January–February(DJF) and the lowest correlations observed in June–July–August(JJA). 3) The SIWI and IOSDEI have a common period of approximately 1 week in JJA and DJF. The SIWI leads by approximately 2–3 days in this common period. 相似文献
