中文摘要：

微震源定位是微震监测技术的核心。微震源定位问题中的已知参数几乎都存在误差,现有微震源定位算法通常通过最小化所有检波器的到时（差）值函数,但得到的定位结果往往会偏离震源实际位置。针对现有微震源定位算法的不足,提出一种由两步反演构成的微震源定位新方法;在新方法中,一次反演识别异常检波器,二次反演实现震源空间坐标的精确搜索。基于均匀速度假设,分别建立3参数、4参数和5参数的反演模型。应用多目标遗传算法（Non-dominated Sorting Genetic Algorithm–Ⅱ,简称NSGA–Ⅱ）实现一次反演,为实现震源的精确搜索和减少计算时间,二次反演建议采用单目标优化算法实现。将提出的方法用于某矿井工程震源定位实例中,计算结果表明：提出方法能够有效的识别异常检波器,定位结果也较未剔除异常检波器时有了大幅度提升,且相对而言,4参数反演模型的定位结果优于5参数模型。该文方法可作为微震源定位的一种参考。

英文摘要：

Micro-seismic source location is crucial for the micro-seismic monitoring technology. The existing algorithms usually minimize the function of time arrival values（time difference） from all detectors to obtain the location of micro-seismic source. The positioning results deviate usually from the actual locations of the sources because all the known micro-seismic parameters have errors. To overcome the shortcomings of the present algorithm,a two-step inversion method for the micro-seismic source location is thus proposed. The first inversion is to identify the abnormal detector,and the second inversion is to search the exact location from the space coordinates of sources. The 3–parameter,4–parameter and 5–parameter inversion models on the assumption of uniform velocity are established and compared. A multi-objective genetic algorithm（Non-dominated Sorting Genetic Algorithm–Ⅱ,NSGA–Ⅱ） is used for the first inversion. A single objective optimization algorithm is suggested in the second inversion in order to achieve the accurate source searching and to reduce the computing time. The results from a case study indicate that the proposed method can effectively identify the abnormal detectors and the positioning is greatly improved compared with the results with the abnormal geophone. The 4-parameter inversion model is better than the 5-parameter inversion model.