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多通道微震监测系统采用的定位算法一般是基于P波到时与等速度模型,P波到时拾取的准确与否直接影响着定位精度的高低。然而由于地震波传播衰减与应用环境背景噪声的原因,参与定位的一些通道信噪比偏低,P波到时拾取的随意性较大,导致震源定位结果与实际震源位置相差较大,影响了在矿山的实际应用效果。简要介绍了震源定位方法并开发应用了基于小波理论的离散信号滤噪程序,通过对原始信号进行小波分解、给定阀值与重构处理后,提高了原始信号的信噪比,显著地提高了P波到时拾取准确度。结合某矿山特大采空区采场冒顶实例,应用基于小波理论的信号滤噪方法对信噪比偏低的通道进行了滤噪处理,重新优化拾取了P波到时。所得到的定位结果,与处理前相比,定位误差从47~94 m减小到14~23 m,大大提高了微震监测在矿山的实际应用效果。
The positioning algorithm used in multi-channel microseismic monitoring system is generally based on the P-wave arrival time and the constant velocity model. The accuracy of P-wave arrival timing directly affects the positioning accuracy. However, due to the propagation attenuation of seismic waves and the background noise of application environment, the signal-to-noise ratio of some channels involved in the positioning is low and the randomness of P wave arrival time is relatively large, resulting in a large difference between the source positioning results and the actual source location, Practical application of the mine. The method of source localization is briefly introduced and a discrete signal filtering program based on wavelet theory is developed and applied. After the original signal is decomposed by wavelet, the signal-to-noise ratio of the original signal is improved significantly after given threshold and reconstruction processing P wave to pick up the accuracy. Combined with the example of roof falling in the stope of a large gob of a mine, the signal filtering and noise reduction method based on wavelet theory is applied to filter the channel with low signal-to-noise ratio, and the P wave arrival time is optimized again. Compared with the pre-processing, the positioning error is reduced from 47-94 m to 14-23 m, which greatly improves the practical application of microseismic monitoring in mines.