A Comparative Study of Simulated Annealing and Genetic Algorithm Method in Bayesian Framework to the 2D-Gravity Data Inversion

Abstract

The use of modern optimization method in geophysical inversion has effectively given a robust global solution in its application to solve a complex non-linearity problem. Here, we tested two artificial intelligent-based methods, the simulated annealing and the genetic algorithm to the gravity data. Using predicted anomalies geometries, these methods are addressed to invert a synthetical gravity data extracted from grav2d open source. Differences between these methods are observed in both single parameter inversion and simultaneous multi parameter inversion to evaluate the speed of computing and the use of Space in memory. The result give us an idea that the genetic algorithm are slower than the simulated annealing in solving a simple inversion problem (small data set and less parameter to be inverse) but efficient in a large data set. meanwhile, the simulated annealing faced some problem in locating a global minima of the misfit function for the large data. in the latter case, we simulated the methods in the Bayesian framework to see the distribution of posterior probability of the parameters.