1 Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Visco-acoustic transmission waveform inversion of velocity structure in space-frequency domain
1 Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China 2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China
摘要According to the least square criterion of minimizing the misfit between modeled and observed data, this paper provides a preconditioned gradient method to invert the visco-acoustic velocity structure on the basis of using sparse matrix LU factorization technique to directly solve the visco-acoustic wave forward problem in space-frequency domain. Numerical results obtained in an inclusion model inversion and a layered homogeneous model inversion demonstrate that different scale media have their own frequency responses, and the strategy of using low-frequency inverted result as the starting model in the high-frequency inversion can greatly reduce the non-uniqueness of their solutions. It can also be observed in the experiments that the fast convergence of the algorithm can be achieved by using diagonal elements of Hessian matrix as the preconditioned operator, which fully incorporates the advantage of quadratic convergence of Gauss-Newton method.
Abstract：According to the least square criterion of minimizing the misfit between modeled and observed data, this paper provides a preconditioned gradient method to invert the visco-acoustic velocity structure on the basis of using sparse matrix LU factorization technique to directly solve the visco-acoustic wave forward problem in space-frequency domain. Numerical results obtained in an inclusion model inversion and a layered homogeneous model inversion demonstrate that different scale media have their own frequency responses, and the strategy of using low-frequency inverted result as the starting model in the high-frequency inversion can greatly reduce the non-uniqueness of their solutions. It can also be observed in the experiments that the fast convergence of the algorithm can be achieved by using diagonal elements of Hessian matrix as the preconditioned operator, which fully incorporates the advantage of quadratic convergence of Gauss-Newton method.
