我国深部煤矿高强度开采和复杂地质环境造成冲击地压灾害频发，对矿井安全高效生产造成严重威胁。目前，我国冲击地压矿井配备的微震监测系统在震源定位精度上仍存在不足，冲击地压灾害依然存在“灾源找不准、灾害控不住”的难题。为准确描述采场微震活动空间演化特征，降低震源定位误差对微震预警造成的不利影响，基于冲击地压矿井现有微震监测条件，采用仿真正演试验法探究了煤矿微震台网定位误差矢量分布特征，提出了一种考虑微震定位精度影响的微震裂隙贯通表征方法，并在某矿井回采工作面进行了冲击地压预警实践。研究结果表明：采场不同区域震源定位精度的矢量性差异是造成冲击地压微震预警偏差的重要原因；提出的微震破裂贯通可能性指数$ F{_{{mathrm{sum}}}} $采用与震源能级相关的近场区域半径表征震源破裂尺度，并考虑定位误差对不同距离震源间破裂贯通的影响，最大程度还原了采场煤岩体裂隙扩展贯通可能性的分布规律，与冲击地压危险具有良好对应关系；$ F{_{{mathrm{sum}}}} $在冲击地压危险相关性上显著优于微震频次，同时危险区域识别效率又强于微震能量，可兼顾预警精准率和召回率，是理想的冲击地压周期性预警评价指标。研究成果将为评价微震台网监测能力、提高冲击地压微震预警能力与防治效率提供参考。

Due to high-intensity mining and complex geological conditions, frequent occurrence of rock burst disasters in deep coal mines in China is posing serious threats to mine safety and efficiency. The insufficient locating accuracy of the seismic monitoring system in burst-prone coal mines is still presented, which leads to difficulties in accurately identifying and controlling coal burst hazards. To precisely characterize spatial evolution of seismic activities and reduce the impact of location error on the seismic pre-warning, this paper used the current seismic networks in burst-prone coal mines to conduct forward modeling experiments to explore the distribution characteristics of source location error vectors in the mining field. A new method for characterizing seismic fracture connectivity that considers location accuracy impacts was proposed and applied for coal burst pre-warning in a longwall face. The results show that significant vectorial differences of seismic source location accuracy at different area of the mining field is a key factor in false identifying coal burst risks. The proposed Fracture-connectivity-probability Index ($ F{_{{mathrm{sum}}}} $) characterizes the rupture scale by using the near-field radius that relates to the seismic energy level, which considers the locating error impacts on fracture connectivity between seismic sources at different distances. $ F{_{{mathrm{sum}}}} $ can restore the distribution law of the fracture extension and connectivity probabilities of the coal-rock mass in the greatest extent, and it also corresponds well to the coal burst risks. $ F{_{{mathrm{sum}}}} $ can balance forecast accuracy and recall rate and have better correlation with coal burst, which can be an ideal indicator for periodical coal burst risk assessment. The outcome of this research can provide references for evaluating the monitoring capability of seismic networks and improving the ability and efficiency of coal burst pre-warning and prevention.