微地震事件的空间分布可以用来监测水力压裂过程中裂缝的发育情况。因此，震源定位是微震监测中重要的环节。震源定位依赖准确的速度模型，而震源位置和速度模型的耦合易导致线性迭代的同时反演方法陷入局部极小值。邻近算法作为一种非线性全局优化算法，能够最大程度地避免陷入局部最优解。本文将邻近算法应用于单井监测的微震定位和一维速度模型同时反演，首先利用邻近算法搜索一维速度模型，再使用网格搜索方法进行震源定位，并根据定位的走时残差产生新的速度模型，最后通过若干次迭代使其收敛到最优解。理论和实际数据结果均表明该方法能够避免局部最优解，得到较为可靠的震源位置和一维速度模型。

The distribution of the microseismic events induced by hydraulic fracturing is used to study the geometry of the fracture growth. Therefore,it is important to obtain accurate locations of microseismic events. Detecting the accurate locations of microseismic events relies on an accurate velocity model. Because of the trade-off between the event locations and velocity model,the solution of simultaneous inversion method is easily trapped in the local minima. Neighbourhood algorithm(NA)is a global optimization algorithm and has more power to escape from local minima. This study applies the NA to the simultaneous inversion of source location and 1D velocity model for single well monitoring. The NA is used to search 1D velocity model firstly,and then locating the source parameters with the grid search method. Finally,the travel time residuals of the location are used to evaluate the 1D velocity model derived by the NA. Both numerical tests and field data are applied to this method in this paper. Results indicate that the joint NA and grid search method can avoid local solutions and obtain reasonable event locations and 1D velocity model.

P315

国家自然科学基金（42004040、U1901602、41704040、41904044）、江西省防震减灾与工程地质灾害探测工程研究中心开放基金（SDGD202002、SDGD202007）共同资助