3D wavefront attribute determination and conflicting dip processing

Abstract

The knowledge of 3D wavefront attributes allows many important applications, such as stacking, 5D interpolation, 3D diffraction separation and imaging, and 3D wavefront tomography, just to name a few. For the determination of wavefront attributes, we use the common-reflection-surface (CRS) operator. We adopt a simultaneous search for the determination of wavefront attributes and combine it with conflicting dip processing. For the simultaneous search, we compare three heuristic global optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE). For conflicting dip processing, a dip angle decomposition method for the probed sample is introduced and the simultaneous search is independently performed in specified dip ranges to individually obtain attributes and semblance for each range. Results for the laterally heterogeneous 3D SEG C3WA data indicate that DE has superior performance to determine the 3D wavefront attributes when compared with PSO, GA, and the conventional pragmatic approach because a higher semblance and an improved set of wavefront attributes are achieved. A comparison of the data-driven wavefront attributes obtained from the DE with the model-driven wavefront attributes computed by kinematic and dynamic ray tracing reveals the validity of the data-driven wavefront attributes. Combining the simultaneous search with conflicting dip processing for the 3D CRS stack further improved reflected energy and diffraction details when compared with results without simultaneous search and/or conflicting dip processing.