Abstract: Microseismic locating method is an important part of microseismic monitoring technology, the key of which is to locate the hypocenter. In this study, we analyze the two- and three-dimensional spatial distributions of microseismic locating objective functions through spatial gridding and calculation of the objective function values of grid intersections. Accordingly, we find that the objective function is continuous with a unique minimum value, the convergence range of single axis decreases gradually, and the convergence range of each axis varies. Using the above findings and the advantages and disadvantages of pattern search method and grid search method, we propose a variable step size accelerated search method based on continuous comparison module, the variable step size module and the acceleration module. Through the comparison of four indices between the simulation example and the engineering data, i.e. the convergence stability, the accuracy of the results, the calculation speed, and the degree of influence for initial values of parameters, we can find that in the simulation example, compared with simulated annealing algorithm and genetic algorithm, the standard deviations of objective function value, locating error and wave velocity error of the variable step size accelerated search method are all 0. The average locating error of the proposed search method is 0.7% and 1.9% of that of other two algorithms, respectively. The average calculation time of the proposed search method is 6.9% and 33.2% of that of the other two algorithms, respectively. The influence of merely changing one parameter on the locating error in this method is between 0.005 m and 0.025 m. Reducing the lower limit of the search step size can effectively improve the accuracy of the results but increase calculation time. When the first arrival time, objective function model and coordinates of the geophone position are specified, the search algorithm has negligible impact on the locating accuracy.

Key words: microseismic locating, spatial image, variable step size accelerated search method, simulated annealing algorithm, genetic algorithm