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<p>Velocity model building for tilted transversely isotropic (TTI) media is crucial for high-precision imaging in seismic exploration of complex structural areas. However, TTI tomographic inversion often faces challenges such as severe multi-parameter coupling, strong non-uniqueness, and difficulties in solving for anisotropic parameters. To address these issues, a multi-parameter tomographic model building method for TTI media using Hamiltonian polynomials and cross-gradient constraints is proposed. First, starting from the eikonal equation, a ray governing equation using a Hamiltonian polynomial approximation is built. Then the fourth-order Runge-Kutta (RK4) algorithm is applied to ray tracing to accurately construct the tomographic kernel. Second, to decouple velocity and Thomsen anisotropic parameters, cross-gradient regularization is introduced into the objective function. This ensures structural consistency when updating multiple parameters, effectively suppresses cross-coupling effects, and reduces inversion non-uniqueness. Tests on both synthetic models and field data show that the inverted multi-parameter models share highly consistent geological structures. The method also significantly reduces well-to-seismic mis-ties and improves imaging accuracy in complex exploration areas, providing reliable technical support for high-precision imaging.</p>

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TTI Anisotropic Tomographic Velocity Model Building Using Hamiltonian Polynomials

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Hao Zheng,
  • Guo-Fa Li,
  • Xu-Hui Xu,
  • Jie-Xiong Cai

摘要

Velocity model building for tilted transversely isotropic (TTI) media is crucial for high-precision imaging in seismic exploration of complex structural areas. However, TTI tomographic inversion often faces challenges such as severe multi-parameter coupling, strong non-uniqueness, and difficulties in solving for anisotropic parameters. To address these issues, a multi-parameter tomographic model building method for TTI media using Hamiltonian polynomials and cross-gradient constraints is proposed. First, starting from the eikonal equation, a ray governing equation using a Hamiltonian polynomial approximation is built. Then the fourth-order Runge-Kutta (RK4) algorithm is applied to ray tracing to accurately construct the tomographic kernel. Second, to decouple velocity and Thomsen anisotropic parameters, cross-gradient regularization is introduced into the objective function. This ensures structural consistency when updating multiple parameters, effectively suppresses cross-coupling effects, and reduces inversion non-uniqueness. Tests on both synthetic models and field data show that the inverted multi-parameter models share highly consistent geological structures. The method also significantly reduces well-to-seismic mis-ties and improves imaging accuracy in complex exploration areas, providing reliable technical support for high-precision imaging.