An improved 1-D crustal velocity model for the Central Alborz (Iran) using Particle Swarm Optimization algorithm

We present an improved 1-D velocity model of the crust in the Central Alborz region using one of the powerful methods in global optimization techniques named Fuzzy Self-Tuning Particle Swarm Optimization (hereafter called FST-PSO). The FST-PSO generates random particles (velocity models) in a pre-defined solution space in which after number of iterations they lead to a model that yields best fits to the data. The ability of using any error statistics equally leads the algorithm to be independent of the velocity model form. As the PSO family members use only random processes to generate new models, they are inherently stable and avoid all numerical problems encountered in deterministic methods due to matrix inversion. Taking advantage of fuzzy logics implemented in FST-PSO, no parameters are needed to be adjusted including social, cognitive and inertia for running the program and can be used “out-of-the-box”. The proficiency of this method first is checked on both synthetic and real datasets. Then it is applied on a unique large amount of local earthquakes to calculated 1-D velocity model of the central Alborz, Iran.Relocation of events using our new velocity model and station corrections results to considerable reduction in RMS, horizontal and depth errors which obviously lead to a more precise events catalog. The spatial distribution of station corrections correlates well geological features. The positive delays in this area are consistent with Pliocene to Quaternary sed...
Source: Physics of the Earth and Planetary Interiors - Category: Physics Source Type: research