AI Summary & Key Takeaways
AI InsightAfter demonstrate thereliability of the technique, proposed method is applied to Sivas-Gurun basin and the anomaly of the model has only0.1 mGal ME.
📌 3 Key Takeaways
Abstract
In this paper, new modeling technique is introduced using Genetic Algorithm (GA). In this method, Geneticalgorithm (GA) is modified and used as an optimization method in geophysics applications. The problem at hand is tofind the best modelfor the cross-section, which its anomalies fit the observed ones. Optimization methods are generallyused in inversion problems to find some parameters, which describe underground structures. The model shape islimited with the chosen parameters. In our modeling technique, firstly, cross section is divided into prismatic pieces.Then, genetic algorithm finds models in each generation, which have geometries constitute ofprismatic structures, andobtains the best model thatfits its anomaly to the observed one. In this process, the density diflerences of each prismaticpiece are assumed to be constant. Therefore, the model obtained by using GA, the structure is shape independent.Moreover, any kind of structure can be modeled with the proposed method using constant density difference forunderground structure. We give two synthetic examples; thefirst one is type and the other one is basin model. Basedon the anomalies with acceptable errors, GA finds the models satisfactory. Mean Errors WES) of these two syntheticmodes are 0.087 mGal and 0.073 mGal respectively. Then, Godavari Valley models of [1], [2] are considered tocompare our proposed modeling technique. Our model is found to be similar to these models. After demonstrate thereliability of the technique, proposed method is applied to Sivas-Gurun basin and the anomaly of the model has only0.1 mGal ME.