Application of PSO and GSA hybrid optimization method for 1-D inversion of magnetotelluric data

The hybrid of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is a population-based algorithm known as PSOGSA. This technique incorporates the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both strength. Synthetic MT data are used to optimized with this hybrid algorithm for obtaining the best solution. The results show that hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the PSO and GSA. Initially, the PSOGSA is demonstrated on synthetic 1-D MT data generated by the forward modelling. This algorithm is also tested on synthetic 1-D MT incorporated with 10% and 20% random noise and finally applied to field data obtained from Puga geothermal field in NW Himalaya, Ladakh district, Jammu and Kashmir, India. The analyzed results confirm the presence of a shallow conductive region in the area that is well correlated with available results.