Numerical Simulations Of PSO And Its Modifications Applications In Microseismic Monitoring Velocity Calibration

In this paper, we applied particle swarm optimization, an artificial intelligence technique, to velocity calibration in microseismic monitoring. We ran simulations with four1-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. Firstly, we proposed PSO modification by introducing Chaos method in boundary condition and Flexible Boundary Shrinkage Strategy to improve basic PSO algorithm reliability in complex models inversion. Secondly, we proposed the Staged Shrinkage Strategy (SSS) for the PSO algorithm to improve PSO algorithm reliability without extra forward function valuations. The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate in all models and initial conditions simulation tests. 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.