Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research on 3D density structures. A comprehensive study is still needed to refine the expansion mechanism and tectonic evolution of the South China Sea. In this study, we utilized wavelet multi-scale analysis of gravity data to obtain a 3D density model of the South China Sea and discussed its tectonic evolution from the pattern of density anomalies. The inversion results show that (1) the expansion of the South China Sea caused the typical thin oceanic crust and parts of the continent–ocean transition zone may fracture due to the expansion; (2) the low-density anomaly in the upper mantle of Luzon Island may indicate partial melting or the upwelling of asthenosphere materials; and (3) the expansion of the South China Sea is influenced by multiple plate forces and uneven forces affect the distribution of high-density anomalies in the upper mantle.

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.