Endrias Getachew Asgedom

On Separation of Reflections and Diffractions Using a CRS/MUSIC Approach

Seismic diffracted waves carry high-resolution information about important subsurface structures associated with potential hydrocarbon traps like faults and pinch outs. However, the amplitudes of these waves are in general much weaker than those of the reflected waves and diffractions often fall within the noise level. As a consequence, classical seismic processing schemes mostly regard these waves as noise. Recently, however, different approaches have been proposed to separate diffractions from reflections and to further make use of these contributions within velocity analysis and local imaging of the subsurface (Fomel et al., 2007; Moser and Howard, 2008). In this paper we propose to employ the Common Reflection Surface (CRS) technique (Jager et al., 2001)to enhance the diffractions through optimized stacking. To further ensure a high-resolution separation of diffracted energy we also replace the classical coherency measure of semblance with that of MUSIC (MUltiple SIgnal Classification; Schmidt (1986)). The potential resolving power of this combined technique of CRS and MUSIC has been demonstrated employing a synthetic data set involving two closely separated point diffractors placed in the vicinity of a dipping reflector.

Common-offset Diffraction Separation and Imaging with Interpretation

“DEDICATED - Case Studies in Diffraction Imaging and Interpretation” Diffractions contain key information about small-scale inhomogeneities and discontinuities in the subsurface. In this paper we employed a modified Common-Reflection-Surface (CRS) equation, tailored for common-offset sections, with the aim of separating diffractions from reflections. As a next step, diffraction apex binary mapping and the MUltiple SIgnal Classification (MUSIC) algorithm were employed as a higher-resolution imaging. The feasibility of the technique is demonstrated using a field Ground Penetrating Radar (GPR) data that provides a significant diffraction separation and a fairly good image of the faults that correlate well with an independent interpretation of the time-migrated reflections.

Multi-frequency Phase Coherent Super-resolution Imaging

Super-resolution imaging of point like geological features is of great interest for analyzing the location of faults and resolving finer details like formation pinch outs. For diffractions from point like features, it has been demonstrated that the time-reversal MUSIC (MUltiple SIgnal Classification) algorithm is able to resolve the location of very closely separated scatterers beyond the classical limit (Lehman and Devaney, 2003; Gelius, 2009). Though this algorithm is efficient for a noise free case, it has been found that it is highly sensitive to the noise level in the measurements. In this paper, we present a modified MUSIC type of algorithm that can handle measurement noise and the phase ambiguity introduced due to singular value decomposition (SVD) by utilizing both the source and receiver side singular vectors and superimposing phase-coherent multiple frequency images. The performance of this new algorithm is demonstrated to be excellent when employed to numerical data associated with two point scatterers superimposed Gaussian white noise.