Inversion for magnetotelluric data using the particle swarm optimization and regularized least squares

The major disadvantage of using traditional linear or quasi-linear methods to perform magnetotelluric data inversion is that these methods heavily depend on the initial models. If a selected initial model is not suitable, these inversion algorithms may fall into a local minimum or diverge during the iterations. Meanwhile, in most geophysical inversions, it is difficult to obtain a proper initial model because of the lack of sufficient prior information. To solve this problem, a hybrid inversion algorithm based on the combined particle swarm optimization and regularized least squares is proposed to invert magnetotelluric data. Particle swarm optimization algorithms have good performances in global searching. More importantly, these algorithms are not sensitive to the initial models. Therefore, this approach can be used to preliminary search the model parameter space. Then, the searched results are used as an initial model for iterations of inversion based on the least square regularization. Both synthetic data and field data tests demonstrated that the hybrid inversion has a good performance. The numerical experiments show that by utilizing the reliable initial model generated from the particle swarm optimization, the search results of hybrid inversion are greatly refined. The hybrid algorithm offers a significant improvement in both inversion efficiency and accuracy. In particular, this algorithm is suitable for the situations that lack initial information while inverting the field magnetotelluric data.