微震源定位的两步反演方法研究
发布时间：2018-11-14 17:25
【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。
[Abstract]:Microsource location is the core of microseismic monitoring technology. Almost all known parameters in the problem of micro-source location have errors. The existing micro-source localization algorithms usually minimize the arrival (difference) function of all geophone, but the location results often deviate from the actual location of the source. In view of the shortcomings of the existing micro-source localization algorithms, a new micro-source location method consisting of two-step inversion is proposed. In the new method, the anomaly geophone is identified by the first inversion, and the precise search of the focal space coordinates is realized by the secondary inversion. Based on the assumption of uniform velocity, the inversion models with 3 parameters, 4 parameters and 5 parameters are established respectively. The multiobjective genetic algorithm (Non-dominated Sorting Genetic Algorithm 鈪，
本文编号：2331822
[Abstract]:Microsource location is the core of microseismic monitoring technology. Almost all known parameters in the problem of micro-source location have errors. The existing micro-source localization algorithms usually minimize the arrival (difference) function of all geophone, but the location results often deviate from the actual location of the source. In view of the shortcomings of the existing micro-source localization algorithms, a new micro-source location method consisting of two-step inversion is proposed. In the new method, the anomaly geophone is identified by the first inversion, and the precise search of the focal space coordinates is realized by the secondary inversion. Based on the assumption of uniform velocity, the inversion models with 3 parameters, 4 parameters and 5 parameters are established respectively. The multiobjective genetic algorithm (Non-dominated Sorting Genetic Algorithm 鈪，
本文编号：2331822
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