عنوان مقاله [English]
Gravity surveying is applied for studying geological structures, for example, basement topography underneath sediment loads. In potential areas for hydrocarbon and groundwater resources, depth of basement can be estimated using different optimization methods, including stochastic global optimization algorithms. These methods include many functions call of the forward function, so usual forward approaches that discrete the sediment volume into a set of right rectangular prisms need too much computational time. This can be controversial issue while implementing three-dimensional stochastic inversion. In this study, 3D Cauchy-type integral as a fast forward function is applied to accelerate the gravity inversion for 3D determination of the depth to basement. Cai and Zhdanov (2015a,b) has introduced this effective approach for potential fields modeling. This method in modeling sediment-basement interface not only replaced all prisms of conventional volume approach with a gridded basement but also simple mathematical terms are used in comparison with customary prismatic methods which includes trigonometric and logarithmic expressions. Synthetic forward modeling of both of our realistic basin models assesses the validity of the forward operator. Evaluation time for one of the model basins based on the Cauchy-type integral in comparison with the prismatic method which was carried out by two different techniques of forward modeling, is 15 and 50 order lower. Implementing genetic algorithm on the gravity data, the depth of the basement has been recovered. The misfit of our data achieved by the algorithm with initial population equal to 10 times of total number of parameters and carrying 700 generations, were lower than 2 mGal. Optimal values were obtained as 80% and 20% for crossover and mutation, respectively. In addition, due to the non-uniqueness of the gravity problem the genetic algorithm uses a smoothing constraint. By fixing the optimal parameters of genetic algorithm, the optimization process is repeated to find the optimal value for the smoothing factor yielding the most accurate model based on the RMS of the reconstructed model. Results show that a smoothing factor between 0.005-0.015 reconstruct stable solutions. Also applying a Gaussian filter, a smoothing filter with the kernel size equal to 11×11, to the calculated depths achieves more stable evaluations. Noisy synthetic and noise-free gravity data were inverted for one symmetric basin and the algorithm has been able to successfully reconstruct the basement. The case study area is the Aman-Abad alluvial plain(Iran) which main parts are located in the Sanandaj-Sirjan zone in the Zagros Mountains of Iran. The suitable parameters of the genetic algorithm are found by synthetic tests to invert real gravity data to image the interface of the impermeable layer groundwater. The most common polynomial regression, i.e., degree 1 is applied to calculate residual gravity anomaly. Reconstructed depths from residual gravity anomaly match properly with gravity anomaly trend. Deep parts of basement (as impermeable surface) have estimated about 150m which look promising for groundwater resources. Although, previous gravity studies have calculated maximum thickness of sediment lower than 200m also well data specified depth of basement 140m.