Home

Quick Links

Search

 
A Robust Differential Semblance Optimization by Quantitative MigrationNormal access

Authors: Z. Yu and Y. Liu
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Velocity Model Estimation A
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201900647
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 637.32Kb )
Price: € 20

Summary:
Wave equation migration velocity analysis is an image domain method which can be implemented to estimate the large-scale model. For classic migration and velocity analysis, the inversion results or stacked image sections often suffer from kinematic artefacts due to the limit of the acquisition geometry and uneven illumination or mathematically due to non-unitary of the wavefield extrapolation operator. In this paper, the modified differential semblance optimization (DSO) objective function with root mean squared image amplitudes, which can reduce the amplitudes sensitivity of DSO, is adopted to evaluate the inverted images quality. To obtain more stable results, we introduce a weight to the traditional migration and velocity inversion without ray tracing calculation. To some extent, the weighting function can automatically compensate for uneven illumination and remove migration artefacts. Thus the gradient of MVA has a smoother behavior and gives an optimal convergence. The results of numerical examples illustrate the robustness of the weighting function.


Back to the article list