Classically, local deterministic optimization techniques have been employed to solve such nonlinear gravity inversion problem. Nevertheless, local search methods can also be easily implemented and demonstrate higher rates of convergence; but in highly nonlinear cases such as geophysical problems, they require a reliable initial model which should be adequately close to the true model. Recently, global optimization methods have shown promising results as an alternative to classical inversion methods. Each of the global optimization algorithms has unique benefits and faults; therefore, applying different combinations of them is one of the proposed solutions for overcoming their distinct limitations. In this research, the design and implementation of the hybrid method based on a combination of the imperialist competitive algorithm (ICA) and firefly algorithm (FA) as tools of two-dimensional nonlinear modeling of gravity data and as a substitute for the local optimization methods were investigated. Hybrid of ICA and FA algorithm (known as ICAFA) is a modified form of the ICA algorithm based on the firefly algorithm. This modification results in an increase in the exploratory capability of the algorithm and improvement of its convergence rate. This inversion technique was first successfully tested on a synthetic gravity anomaly originated from a simulated sedimentary basin model both with and without the presence of white Gaussian noise (WGN). At last, the method was applied to the Bouguer anomaly from a real gravity profile in Moghan sedimentary basin (Iran). The results of this modeling were compatible with previously published works which consisted of both seismic analysis and other gravity interpretations. In order to estimate the uncertainty of solutions, several inversion runs were also conducted independently and the results were in line with the final solution.

传统上，局部确定性优化技术已被用来解决这种非线性重力反演问题。尽管如此，局部搜索方法也可以轻松实现并展示更高的收敛速度；但是在地球物理问题等高度非线性的情况下，它们需要一个可靠的初始模型，该模型应该足够接近真实模型。最近，全局优化方法作为经典反演方法的替代方法已经显示出有希望的结果。每种全局优化算法都有其独特的优点和缺点；因此，应用它们的不同组合是克服它们不同局限性的建议解决方案之一。在这项研究中，研究了基于帝国主义竞争算法（ICA）和萤火虫算法（FA）组合作为重力数据二维非线性建模工具和替代局部优化方法的混合方法的设计和实现。ICA 和 FA 算法的混合（称为 ICAFA）是 ICA 算法在萤火虫算法的基础上的改进形式。这种修改导致算法探索能力的增加和收敛速度的提高。这种反演技术首先在合成重力异常上成功测试，该异常源自模拟沉积盆地模型，存在和不存在高斯白噪声 (WGN)。最后将该方法应用于伊朗莫干沉积盆地真实重力剖面的布格异常。这种建模的结果与先前发表的作品兼容，这些作品包括地震分析和其他重力解释。为了估计解的不确定性，还独立进行了几次反演，结果与最终解一致。