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<p>As a classical method to solve the ill-posed problem in tomographic inversion, model regularization effectively enhances inversion stability and adaptability to complex geological structures by incorporating prior information. Focusing on depth-domain tomographic velocity modeling, this paper systematically compares the differences in the application between Tikhonov regularization and preconditioned regularization, optimizes the model regularization method suitable for depth-domain model building, and further proposes a preconditioned model regularization algorithm based on the structure tensor. First, the structure tensor is utilized to extract structural prior information, which is then combined with the preconditioned regularization to construct a prior constraint term. Subsequently, the conjugate gradient (CG) method is employed to efficiently solve the tomographic matrix equations. Synthetic data tests demonstrate that the proposed method can accurately locate geological boundaries and fault positions in complex geological models, and precisely invert velocity anomalies in structural zones. In field data applications, the accuracy of the velocity model inverted by this method is significantly improved, leading to a remarkable enhancement in imaging quality for complex structural areas. These results indicate that the structure tensor-guided preconditioned regularization method possesses both high resolution and strong robustness, thereby providing more reliable technical support for depth-domain tomographic velocity model building in complex structural regions.</p>

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Model Regularization Techniques in Depth-Domain Tomographic Velocity model building

  • 期刊论文

  • 发表日期：

    2026年4月24日

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  • Hao Zheng,
  • Guo-Fa Li,
  • Jiang-Hua Zhang,
  • Kai Guo,
  • Jie-xiong Cai

摘要

As a classical method to solve the ill-posed problem in tomographic inversion, model regularization effectively enhances inversion stability and adaptability to complex geological structures by incorporating prior information. Focusing on depth-domain tomographic velocity modeling, this paper systematically compares the differences in the application between Tikhonov regularization and preconditioned regularization, optimizes the model regularization method suitable for depth-domain model building, and further proposes a preconditioned model regularization algorithm based on the structure tensor. First, the structure tensor is utilized to extract structural prior information, which is then combined with the preconditioned regularization to construct a prior constraint term. Subsequently, the conjugate gradient (CG) method is employed to efficiently solve the tomographic matrix equations. Synthetic data tests demonstrate that the proposed method can accurately locate geological boundaries and fault positions in complex geological models, and precisely invert velocity anomalies in structural zones. In field data applications, the accuracy of the velocity model inverted by this method is significantly improved, leading to a remarkable enhancement in imaging quality for complex structural areas. These results indicate that the structure tensor-guided preconditioned regularization method possesses both high resolution and strong robustness, thereby providing more reliable technical support for depth-domain tomographic velocity model building in complex structural regions.