关键词: 微地震监测, 震源定位, 蜣螂算法, 群智能算法, 水力压裂

Abstract: To improve the positioning accuracy under unstable first arrival time conditions of microseismic events in hydraulic fracturing, a Dung Beetle Optimizer with Differential Evolution (DE-DBO) algorithm was established by combining the Dung Beetle Optimizer (DBO) and the Time Difference of Arrival (TDOA) velocity model. This algorithm initializes the initial positions of dung beetles through Bernoulli chaotic mapping on the basis of the standard DBO to improve population diversity, uses differential evolution algorithm to enhance the global search ability of DBO, and uses the Levy flight strategy to improve the diversity of group search and guide the search algorithm to jump out of local optima. The simulation results show that when the wave velocity fluctuates within the range of ±1%, ±3%, and ±5%, DE-DBO has smaller root mean square error and absolute error. Its positioning accuracy, convergence speed, and algorithm stability are better than those of the traditional standard DBO, particle swarm optimization, and genetic algorithm. The research results not only improve the accuracy and stability of microseismic positioning under uncertain velocity models but also have great significance for fracture dynamic monitoring and fracturing effect evaluation at hydraulic fracturing sites.

Key words: microseismic monitoring, seismic source location, dung beetle algorithm, swarm intelligence algorithm, hydraulic fracturing