The reflection waveform inversion (RWI) using the tomographic term of the sensitivity kernel could be an alternative to full waveform inversion (FWI), especially in the deep target area where diving wave is not available. In RWI, the reflection waveform residuals are minimized to invert for the low to intermediate wavenumber components of the subsurface model. Nevertheless, the RWI is a highly nonlinear problem requiring careful preconditioning to avoid the local minima and accelerate the convergence rate. The Hessian operator is the second-order derivative of the objective function, implying the quadratic convergence information for the inverse problem. Making use of the Hessian information through Gauss-Newton or Newton method, is a promising way to improve the accuracy and efficiency of RWI. Besides, using traveltime objective function can make the reflection-based inversion less prone to the cycle-skipping problem. Therefore, to facilitate the practical application of reflection data in velocity reconstruction, we design a two-stage workflow, in which reflection traveltime inversion (RTI) and RWI are cascaded and solved using the truncated Gauss-Newton method to obtain a quadratic convergence. The synthetic data example and the real data application show the effectiveness of the proposed method.
Presentation Date: Tuesday, October 13, 2020
Session Start Time: 1:50 PM
Presentation Time: 3:05 PM
Location: Poster Station 3
Presentation Type: Poster
