关键词: 微震定位, 空间图像, 变步长加速搜索法, 模拟退火算法, 遗传算法

Abstract: Microseismic locating method is an important part of microseismic monitoring technology, the key of which is to locate the hypocenter. We analyze the two-dimensional and three-dimensional spatial distributions of microseismic locating objective functions by using spatial gridding and calculating the objective function values of grid intersections. Accordingly, we find that the objective function is continuous and the minimum value is unique, 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 the 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 indexes between the simulation example and the engineering data: the convergence stability, the accuracy of the results, the speed of calculation and the degree of influence of initial values of parameters, we get the results that: in the simulation example, compared with that of simulated annealing algorithm and genetic algorithm, 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 variable step size accelerated search method is 0.7% and 1.9% of that of other two algorithm, respectively; the average calculation time of this algorithm is 6.9% and 33.2% of that of the other two algorithm, respectively. The influence of the algorithm changing each parameter individually on the locating error is between 0.005 m and 0.025 m; reducing the lower limit of the step size for search can effectively improve the accuracy of the results but increase calculation time. When the initial arrival time, objective function model and coordinates of the geophone position are specified, the search algorithm has no substantial impact on the locating accuracy.

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