Improvements on particle swarm optimization algorithm for velocity calibration in microseismic monitoring

In this paper,we apply particle swarm optimization(PSO),an artificial intelligence technique,to velocity calibration in microseismic monitoring.We ran simulations with four 1-D layered velocity models and three different initial model ranges.The results using the basic PSO algorithm were reliable and accurate for simple models,but unsuccessful for complex models.We propose the staged shrinkage strategy(SSS) for the PSO algorithm.The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate.We investigated the effects of PSO's velocity clamping factor in terms of the algorithm reliability and computational efficiency.The velocity clamping factor had little impact on the reliability and efficiency of basic PSO,whereas it had a large effect on the efficiency of SSS-PSO.Reassuringly,SSS-PSO exhibits marginal reliability fluctuations,which suggests that it can be confidently implemented.