Inversion of Gravity Anomalies by a Hybrid Metaheuristic Algorithm

In this work, we introduce application of a hybrid algorithm (DE/PSO) to estimate the model parameters from residual gravity anomalies due to some simple geometrical bodies. This algorithm combines differential evolution (DE) and particle swarm optimization (PSO). To investigate the performance of the hybrid algorithm, test studies were carried out using synthetic and field data sets. The synthetic data sets include noise-free and noisy synthetic anomalies. Two published gravity anomalies from Cuba and Canada were used as the field data. In the hybrid algorithm, DE and PSO yield [premature] solutions separately and share their best solutions during an iterative process. An openly accessible metaheuristics package (NMOF) in R programming environment was used to implement the hybrid algorithm. Through simulations using synthetic anomalies, DE/PSO algorithm was successful to provide improved results. In comparison to the solutions from the single algorithms (DE and PSO), the DE/PSO algorithm shows more effectiveness in terms of accuracy and convergence. The true model parameters of noise-free and noisy synthetic gravity anomalies were recovered well by the hybrid algorithm. The results of inversion for the field examples are characterized by low residual values between the observed gravity anomalies and the calculated ones.