Non-linear optimization by generalized neighborhood algorithm (GNA) and its application for magnetotellurics (MT) layered-earth modeling

Population-based optimization algorithms are often used to resolve highly non-linear inverse problems by performing iterative and stochastic searches of the model space for solutions. These techniques rely on a combination of global and local searches associated with the exploration and exploitation capabilities, respectively. The iterative process usually converges to models very close to each other, leading to unrealistic solution's uncertainties extracted from the final population. In this paper, we are more interested in the model space exploration leading to more representative uncertainties of the solutions. We divide the search agents into two groups, each randomly distributed in the whole model space and in the vicinity of a promising solution or best model. The additional tuning parameter in the relatively new Generalized Neighborhood Algorithm (GNA) is quite simple to choose, i.e., the extent of search in the neighborhood of the best model. The method was tested to invert magnetotelluric (MT) synthetic data of various representative cases with good results, i.e., recovery of the synthetic models with response that fits the synthetic data. Application of GNA to field data showed that the subsurface resistivity distribution agrees well with the geology of the study area.