1-D Magnetotelluric Inversion using Particle Swarm Optimization Algorithm

Abstract:

Inversion is a process to know the earth model from measured data. In Magnetotelluric case, linearized non-linear approach is often used for inversion. While this approach is producing earth models that fit the measured data, the results are influenced by the starting model. This may causing the model produced from inversion doesn't reflect the true earth layer and a problem arise when bad starting model were used, the inversion may not produce the best result. The developments of technologies, especially in computers, allow us to do a faster computation, allowing more exploration of models. Many new algorithms is created to take advantage of this, Particle Swarm Optimization (PSO) algorithm is one of them. Particle swarm optimization (PSO) is a population based stochastic inspired by social behavior of bird flocking or fish schooling. Since it doesn't require any starting model and explore more solution, the use of this algorithm can solve the problem in linearized approach inversion that has been stated before.