Constrained Adaptive Weighted Particle Swarm Optimization (C-AWPSO) Algorithm for Dipping Fault Parameter Inversion

To overcome the limitations of gravity inversion methods in fault inversion, this paper proposed a constrained adaptive weighted particle swarm optimization algorithm. Simulation experiments demonstrate that the proposed method exhibits stronger noise resistance compared to traditional optimization methods. In practical cases, the inversion accuracy of this method is improved by at least 64.4%, and the predicted gravity anomaly curve is closer to the observed data. The research findings are as follows: (1) The linearly decreasing inertia weight strategy performs best in terms of convergence efficiency and global search capability; (2) among the fault parameters, the top-layer center depth z and bottom-layer center depth w show higher sensitivity, and the inversion results for these parameters are more stable, which is beneficial for determining the depth information of faults; (3) introducing L2 regularization and penalty terms as constraints significantly improves the inversion stability, and among these, z and w have a particularly notable impact on the error.