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Technical Paper: 3D general surface multiple prediction: An algorithm for all surveys

Society: SEG
Paper Number:
Presentation Date: 2008

Download: 3D general surface multiple prediction: An algorithm for all surveys (3.41 MB PDF)

3D general surface multiple prediction (GSMP) is a data-driven 3D SRME algorithm that solves the problem of trace sparseness. Rather than overcoming the sparseness problem by changing the data to fit the algorithm — for example, by means of regularization and interpolation — GSMP changes the SRME algorithm to fit the data. This not only makes GSMP a universal compute engine for the 3D prediction of multiples, but also makes it quite versatile. We illustrate this versatility by showing successful applications of GSMP to narrow-azimuth, wide-azimuth, and rich-azimuth seismic surveys.

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