عنوان مقاله [English]
Inversion of basement relief of sedimentary basins is an important application among the non-linear modeling techniques. Particularly in sedimentary basins with hydrocarbon source potential, the thickness of sediments is one of the primary factors in determining the thermal maturation of these basins. Gravity methods have been vastly used to estimate the base of sedimentary basins. The aim of this research is two-dimensional modeling of the basement geometry of a sedimentary basin using the inversion of the gravimetry data. A common way to approach this problem is discretizing the basin using polygons (or other geometries), and solving the non-linear inverse problem by local optimization iteratively. This procedure provides a solution which highly depends on the initial model and the used prior information. Besides, due to the non-linearity of this inverse problem, local optimization methods will fail whenever there is no reliable initial model. The global optimization method is a promising alternative to classical inversion methods because the quality of their solutions does not depend on the initial model. Also, they do not use the derivatives of the objective function.
Ant colony algorithm (ACO) is one of the kinds of important swarm intelligence algorithms which have been successfully applied in many fields such as inversion of geophysical data. This research, in two separate stages, investigates the design and implementation of the ACO as a powerful tool for two-dimensional non-linear modeling of gravity data. ACO can be a substitution for the local response methods such as Marquardt-Levenberg and Gauss-Newton. To apply this algorithm in the problem under consideration, it was validated with the data obtained from a synthetic model and then, reverse modeling of the real data was performed. For evaluating the validation of this developed algorithm, it was tested by the synthetic model. Data from the synthetic models were modeled by using the developed algorithm, and acceptable results were obtained. By using this approach, the topography of the basement in the synthetic model was obtained with acceptable accuracy.
In this study, the effect of ACO algorithm on different values of probable noises was investigated. The results indicate that this algorithm is suitably stable against the Gaussian white noise with relatively high amplitudes. In modeling for high noise percentage, the root mean square error of the data calculated with the original data didn't exceed 1.64 mGal and that obtained with the original model at most was 131.4 m. The results of modeling show acceptable agreement with the original model even in the case of data contaminated with 10% Gaussian white noise.
The reliability of the proposed method to the inversion of a real gravity data was confirmed by applying it on a real gravity profile in the Moghan sedimentary basin. The results of this modeling are compatible with previously published works in this area.