3D generalized spherical multifocusing seismic imaging

Abstract

We introduce a three-dimensional (3D) generalized spherical multifocusing (GSMF) algorithm to generate a high-resolution 3D stacked volume that is equivalent to a synthesized 3D zero-offset wavefield for crooked-line/3D seismic data. The proposed algorithm can be applied to arbitrary recording geometry from areas with irregular topography, complex near-surface, and complex subsurface. The 3D GSMF method simultaneously corrects for elevation statics, nonhyperbolic moveout associated with reflections beneath complex overburden structures, and azimuth-dependent dip-moveout effects. Also, the formulation is dually generalized for both the optical domain and the effective medium. The optical domain and effective medium parameterizations account for heterogeneity either by shifting the reference time to project the problem into the optical image space or by adjusting the velocity of an effective overburden, respectively. We test the performance of the proposed method using 3D synthetic data with both 3D and crooked-line surveys. The numerical tests have shown that the new approximation is significantly accurate for gently to highly curved interfaces beneath low to relatively high heterogeneous overburden with rugged topography, even at large offsets and midpoint separations. Additionally, we rigorously evaluate the method using 3D real seismic data acquired over a complex thrust-belt area with rugged terrain. Compared to conventional 3D stacking, the new formulation yields high-resolution and accurate seismic stacked volume from land seismic data collected with arbitrary 3D geometries.

High‐resolution 2.5D multifocusing imaging of a crooked seismic profile in a crystalline rock environment: Results from the Larder Lake area, Ontario, Canada

“…The adjustment can be performed based on prior geological knowledge and legacy data. The globally optimal wavefield attributes are estimated (see section Wavefront Parameter Analysis for details) using global optimization algorithms such as the differential evolution method (see the Appendix) or the very‐fast simulated annealing method (Jodeiri Akbari Fam & Naghizadeh, 2019). This step requires several iterations to adjust the constraints and fine‐tune searching parameters to achieve accurate solutions effectively.…”