Análise do efeito da discretização do modelo de velocidades nas migrações Kirchhoff e Kirchhoff-Gaussian- Beam 2D pré-empilhamento em profundidade

Abstract

The Gaussian Beam (GB) is an asymptotic solution of the elastodynamic equation in the paraxial vicinity of a central ray, which approaches better the wave field than the standard zero-order ray theory. The GB regularity in the description of the wave field, as well as its high accuracy in some singular regions of the propagation medium, provide a strong alternative to solve seismic modeling and imaging problems. In this dissertation , I present a new procedure for pre-stack depth migration with true-amplitude, combining the flexibility and robustness of Kirchhoff migration type using superposition of Gaussian beams to represent the wave field. The proposed migration algorithm comprises in two stacking process: the first is the beam stack applied to subsets of seismic data multiplied by a weight function defined such that stack operator has the same formulation of the integral of the Gaussian beams superposition; the second is a weighted diffraction stack by means of the Kirchhoff type integral having as input the stacked data. For these reasons it is called Kirchhoff-Gaussian-Beam (KGB) migration. In order to compare the Kirchhoff and KGB methods with respect to the sensibility on relation to the discretization length, we apply them to the well-know 2D Marmousi dataset using four velocity grids, i.e. 60 m, 80 m, 100 m e 150 m. As result we have that both methods present a much better image for smaller discretization interval of the velocity grid. The amplitude spectrum of the migrated sections provide us with the spatial frequency contents of the obtained image sections.