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Abstract

Summary

Migration Velocity Analysis aims at estimating the background, low frequency velocity model. Depth images at fixed surface locations can be obtained and are called Common Image Gathers. On these images, classical artifacts, such as migration smiles, impact the Differential Semblance functional and its gradient, needed for an iterative update of the velocity model. To limit these artifacts, we derive a quantitative migration as an automatic way to compensate for illumination. We first indicate that the migrated image can be obtained as the first gradient of an objective function defined in the data domain. In quantitative migration, we introduce a weight in this data-domain objective function. Based on a simple synthetic model, we show step by step how Migration Velocity Analysis becomes more robust.

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/content/papers/10.3997/2214-4609.20141496
2014-06-16
2024-04-19

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References

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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141496
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Quantitative Migration for a More Robust Migration Velocity Analysis
Conference Proceedings 2014, 1 (2014); https://doi.org/10.3997/2214-4609.20141496
/content/papers/10.3997/2214-4609.20141496
/content/papers/10.3997/2214-4609.20141496
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