<p>Elastic least-squares reverse time migration improves the resolution and amplitude fidelity of multicomponent seismic imaging through iterative inversion of reflectivity models. However, conventional elastic least-squares reverse time migration still suffers from noise, artifacts, and instability due to the ill-posed nature of the inverse problem, especially in geologically complex settings. To address these challenges, a novel elastic least-squares reverse time migration method is proposed, incorporating structure tensor-guided total variation regularization and curvelet-domain sparsity constraints. The structure tensor extracts locally dominant orientations from image gradients. When combined with total variation regularization, it enables edge-preserving smoothing and enhances structural continuity while suppressing artifacts across structural boundaries. In addition, the curvelet transform provides a multiscale and multidirectional sparse representation of the reflectivity image, and the curvelet-domain constraint effectively reduces migration noise and artifacts. This hybrid regularization strategy restricts model updates to subspaces that are both structurally consistent and sparsely represented. Numerical experiments on a synthetic graben model and the SEG/EAGE overthrust model indicate that, compared with conventional methods, the proposed method effectively suppresses noise and artifacts, producing images with improved structural consistency and lower model error.</p>