Metaheuristic multi-objective optimization-based microseismic source location approach with anisotropic P-wave velocity field

Rockburst is a common dynamic geological hazard, frequently occurring in underground engineering (e.g., TBM tunnelling and deep mining). In order to achieve rockburst monitoring and warning, the microseismic monitoring technique has been widely used in the field. However, the microseismic source location has always been a challenge, playing a vital role in the precise prevention and control of rockburst. To this end, this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity. On the one hand, it assigns a unique P-wave velocity to each propagation path, abandoning the assumption of a homogeneous velocity field. On the other hand, it treats the P-wave velocity as a co-inversion parameter along with the source location, avoiding the predetermination of P-wave velocity. To solve this model, three various metaheuristic multi-objective optimization algorithms are integrated with it, including the whale optimization algorithm, the butterfly optimization algorithm, and the sparrow search algorithm. To demonstrate the advantages of the model in terms of localization accuracy, localization efficiency, and solution stability, four blasting cases are collected from a water diversion tunnel project in Xinjiang, China. Finally, the effect of the number of involved sensors on the microseismic source location is discussed. • Proposed a novel microseismic source location model that considers P-wave velocity anisotropy. • Devised a novel solution algorithm based on metaheuristic multi-objective optimization. • Validated the efficacy through multiple blasting cases from practical projects.