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Becoming effective velocity-model builders and depth imagers, Part 2—The basics of velocity-model building, examples and discussions

GeoTech Groups, Houston, Texas, U.S.

Stuart Fagin

Geolmage Resources, Kingwood, Texas, U.S.

Corresponding author: NingGuo@GeoTechGroups.com

The first 20% of the full text of this article appears below.

In the previous discussion of migration algorithms (Part 1), their shortcomings and strengths were presented in the context of their implementation with a known velocity field. It is easy to imagine that if the velocity model is inaccurate, then a reliable portrait of the subsurface cannot be developed. In Part 2, we will touch the key element of successful imaging, particularly for prestack depth migration (PSDM), the accuracy of the velocity field.

	Velocities in the seismic inverse problem

 
The velocity field for imaging is developed from the seismic data itself. This should come as no surprise when we consider that the task of imaging is essentially removal of wave propagation effects from seismic data. Such effects are expressed in the moveout patterns of events in the seismic gathers. Indeed, the elimination of reflection moveout, expressed as flatness of events in gathers, constitutes evidence of the accuracy of the derived velocity field.

Gather moveout is used in deriving the stacking velocity field to generate a stack section, or deriving the rms velocity field to apply to a time migration. Moveout observations are also the basis for deriving interval velocities for use in PSDM. Indeed, because the interval velocity estimation techniques discussed below consider the raypath bending effects on moveout, the resulting analysis is far more accurate than conventional stacking velocity or rms velocity analysis. In particular, as events become more nonhyperbolic, the hyperbolic curve fitting of standard stacking velocity analysis loses meaning as a measure of the subsurface velocity field.

As an inverse problem, velocity field estimation suffers from a number of difficulties including nonuniqueness, instability, and convergence. Therefore, several iterations are normally required. The procedure begins with an estimation of an initial field prior to the first imaging iteration. The subsequent model refinement iterations involve three steps:

1. imaging with the existing velocity field
   
2. measuring the imaging . . . [Full Text of this Article]

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