Main geological and mining factors affecting ground cracks induced by underground coal mining in Shanxi Province, China

As one of the largest coal-rich provinces in China,Shanxi has extensive underground coal-mining operations.These operations have caused numerous ground cracks and substantial environmental damage.To study the main geological and mining factors influencing mining-related ground cracks in Shanxi,a detailed investigation was conducted on 13 mining-induced surface cracks in Shanxi.Based on the results,the degrees of damage at the study sites were empirically classified into serious,moderate,and minor,and the influential geological and mining factors(e.g.,proportions of loess and sandstone in the mining depth,ratio of rock thickness to mining thickness,and ground slope)were discussed.According to the analysis results,three factors(proportion of loess,ratio of rock thickness to mining thickness,and ground slope)play a decisive role in ground cracks and can be respectively considered as the critical material,mechanical,and geometric conditions for the occurrence of mining surface disasters.Together,these three factors have a strong influence on the occurrence of serious discontinuous ground deformation.The results can be applied to help prevent and control ground damage caused by coal mining.The findings also provide a direct reference for predicting and eliminating hidden ground hazards in mining areas.

Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

The influence of mining factors on seismic activity during longwall mining of a coal seam

In this article an attempt to determine the influence of mining factors on the seismic activity during the longwall mining of the upper layer of coal seam no.405/2 in one of the Polish hard coal mines in the Upper Silesian Coal Basin was conducted.Two longwall panels were mined in analogous geological conditions and based on the same mining system and technology.However,there was significant difference with regards to the mining factors,which was reflected in the observed seismic activity.Some tools used in mining seismology were applied to illustrate the aforementioned influence of mining factors,e.g.the frequency-energy distribution,the frequency-magnitude distribution,the 2 D distribution of released seismic energy,the relationship between released seismic energy and the volume of mined coal,the Benioff strain release,and the Gutenberg-Richter(GR)b coefficient distribution(b is the proportion between high and low energy tremors).Concerning the Benioff strain release,a new solution,based on the slope of a fitted line in a moving time window,is proposed.

Detecting Surface Subsidence in Coal Mining Area Based on DInSAR Technique

Coal is the primary energy resource in China. Thousands of underground coal mines are operating in China and cause severe land subsidence, leading to many environmental and engineering problems. Huainan （淮南） coal mine is the largest coal mining area in East China. Surface subsidence associated with Huainan coal mining activities has been monitoring by DInSAR （differential synthetic aperture radar） techniques in this study. Four ASAR （advanced SAR） pairs from 2009 to 2010 are selected to perform 2-pass DInSAR processing with spatial and temporal baselines suitable for subsidence monitoring. The subsidence maps generated from these pairs show that the extension of subsidence is consistent with the field observation. Quantitative measurements indicated that the magnitudes of subsidence are increased with the development of underground coal mining exploitation. This study demonstrates that DInSAR technique is effective for surface subsidence monitoring in coal mining area. Limitations and recommendations both in the adopted method and auxiliary data are also discussed.