煤矿微震监测台网监测能力分析与优化方法

Analysis and optimization method of monitoring capability of coal mine microseismic monitoring network

  • 摘要: 微震监测台网的监测能力取决于多种因素,如台网布置、速度模型、震相读取误差、走时区域异常、定位算法、设备运行状态和环境噪声等,其中台网布置现阶段可以人为优化。为了对微震监测台网的监测能力进行有效评价,并在此基础上对台网布置进行优化,提出了一种煤矿微震监测台网监测能力分析与优化方法。分析了台网布置因素中对微震监测台网监测能力影响最大、最直接的四因素:有效波形数、最大空隙角、近台震中距和台站高差,指出有效波形数、近台震中距和台站高差对震源深度求解误差起决定性作用,有效波形数和最大空隙角对震中定位精度起决定性作用。结合现有台网和工作面情况,得出四因素的分布云图,通过四因素分布云图逐项对微震监测台网监测能力进行评价,根据评价结果进行优化,得出新的台网布置方案;对新方案进行定位误差与灵敏度分析,得出全矿井的震中定位误差、震源定位误差及区域灵敏度,对新方案进行二次评价;若二次评价结果满足要求,则可将新方案作为最佳台网布置方案;若二次评价结果不满足要求,则重新进行四因素分项评价并对方案进行优化,直至满足要求为止。现场试验结果表明,利用提出的方法对唐口煤矿5307工作面的微震监测台网进行优化后,爆破震源定位误差均值由59.2 m降到37.2 m,定位误差最大值降到100 m以下,误差在50 m以下的爆破事件占总数的69.0%,说明提出的方法能够有效提高微震定位精度,优化台网监测能力。

     

    Abstract: The monitoring capability of microseismic monitoring network depends on many factors, such as network layout, velocity model, seismic phase reading error, regional anomaly of travel time, positioning algorithm, equipment running state and environmental noise. Among these factors, the network layout can be artificially optimized at present stage. In order to effectively evaluate the monitoring capacity of microseismic monitoring network and optimize the network layout, the analysis and optimization method of monitoring capacity of coal mine microseismic monitoring network is proposed. This study analyzes four factors which have the greatest and most direct influence on the monitoring capability of the microseismic monitoring network. The four factors are the number of effective waveforms, the maximum gap angle, the near-station epicenter distance and the height difference between stations. It is pointed out that the number of effective waveforms, the near-station epicenter distance and the height difference between stations play a decisive role in the error of hypocenter depth solution. The number of effective waveforms and the maximum gap angle play a decisive role in the precision of epicenter positioning. According to the situation of the existing network and the working face, the distribution cloud pictures of the four factors are obtained. The monitoring capability of the microseismic network is evaluated item by item through the distribution cloud pictures of the four factors. The new network arrangement scheme is obtained through optimization of the evaluation result. The positioning error and sensitivity of the new scheme are analyzed. The epicenter positioning error, hypocenter positioning error and regional sensitivity of the whole mine are obtained. The second evaluation of the new scheme is carried out. If the secondary evaluation results meet the requirements, the new scheme can be regarded as the best network layout scheme. If the secondary evaluation results do not meet the requirements, the four factors sub item evaluation will be carried out again and the scheme will be optimized until the requirements are met. The field test results show that after the proposed method is used to optimize the microseismic monitoring network of 5307 working face in Tangkou Coal Mine, the average value of blasting hypocenter positioning error is reduced from 59.2 m to 37.2 m. The maximum value of positioning error is reduced to less than 100 m, and the blasting events with error less than 50 m account for 69.0% of the total. The results show that the proposed method can effectively improve the microseismic positioning precision and optimize the monitoring capability of the network.

     

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