A Matlab-based optimization algorithm is introduced for inverting fault structures from observed gravity anomalies. A convenient graphical user interface is also presented for incorporating the input parameters without any technical complexity to any users. The inversion code uses particle swarm optimization, and all control parameters are tuned initially for faster convergence. There is no requirement of prior choice of an initial model, that is the advantage of using global optimization. The optimization technique is versatile enough to handle any depth-varying density distributions. The maximum number of iterations and stopping criterion is fixed initially for getting the best optimized solution. The inverted model's output in terms of fault structure, observed and inverted gravity anomalies and dip, and vertex location of fault plane can be viewed in the graphical user interface at the end of the optimization process. The optimization algorithm is applied to different synthetic models with fixed and depth-varying density contrasts. All synthetic models are further contaminated with white Gaussian noise for sensitivity analysis, and detailed uncertainty appraisal was also performed for the reliability estimation. Finally, the optimization is implemented for fault structure inversion of the Aswaraopet boundary fault, India, and found that the optimized solution provides a good agreement with the previously published literature. Optimized results indicate that this novel optimization approach demonstrates a robust implementation of fault inversion for any depth-varying density distributions.

中文翻译：

---

使用粒子群优化对具有可变密度对比度的二维断层进行重力反演

引入了一种基于 Matlab 的优化算法，用于从观测到的重力异常中反演断层结构。还提供了一个方便的图形用户界面，用于在没有任何技术复杂性的情况下合并输入参数。反演代码使用粒子群优化，所有控制参数最初都经过调整以加快收敛速度​​。不需要预先选择初始模型，这是使用全局优化的优点。优化技术的通用性足以处理任何深度变化的密度分布。最大迭代次数和停止标准最初是固定的，以获得最佳优化解决方案。反演模型在断层结构、观测和反演重力异常和倾角方面的输出，在优化过程结束时，可以在图形用户界面中查看断层平面的顶点位置。优化算法应用于具有固定和深度变化密度对比的不同合成模型。所有合成模型都进一步受到高斯白噪声的污染以进行敏感性分析，并且还对可靠性估计进行了详细的不确定性评估。最后，对印度 Aswaraopet 边界断层的断层结构反演进行了优化，发现优化后的解与之前发表的文献具有很好的一致性。优化结果表明，这种新颖的优化方法展示了对任何深度变化密度分布的故障反演的稳健实现。