Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested.

Automated summary

Microseismic location systems are known for their high-speed and high precision. However, the requirement of high precision can lead to slower calculation speed. This research focuses on using metaheuristic algorithms to improve the performance of cross-correlation stacking (CCS) for better location accuracy. The study shows that appropriate control parameters can greatly enhance the performance of metaheuristic algorithms, with moth flame optimization (MFO) being the most effective. CCS is found to be superior to traditional methods in terms of location efficiency, and its performance is influenced by the speed model and number of sensors.