Gravity Inversion and Modeling Using Multi and Single objective Genetic Algorithms

Abstract

Abstract Studying the bedrock geometry in exploration operations to obtain its 2D pattern requires nonlinear inverse computations. The algorithms used in the present work included non-dominated sorting genetic algorithm (NSGA-II) and single-objective genetic algorithm (GA), which were used to estimate the depth. One of the most important advantages of NSGA-II and GA methods are that it works independently of the regularization coefficient and initial mode.in the NSGA-II method, owing to the direct use of the regularization term as a separate objective function, which makes the results more acceptable and easier to interpret. In the present study, both algorithms were verified and validated using the data produced by synthetic model. In order for a more precise examination of the performance of both algorithms, the synthetic data were used both without noise and with up to 10% Gaussian white noise (GWN). Accordingly, the modeling results indicated a good consistence between the algorithms and the primary model; so that, the root mean square error parameter for the data obtained from the initial data of the synthetic model ranged from 0.05 to 0.35mGal for the NSGA-II and from 0.07 to 0.52mGal for the GA. Also, this parameter didn't exceed 72.4meter in the NSGA-II and didn't exceed 93.8meter in the GA. Based on the gravimetric data of the Atacama desert (Chile) and Western Anatolia (Turkey) the results obtained from both algorithms under similar conditions in terms of parameter settings and number of algorithm executions indicated good performance of the NSGA-II algorithm compared to the single-objective (GA) algorithm and the cost of calculations in the NSGA-II method is much lower than the GA method.