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GJA van Groenestijn
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In a blended acquisition, source encoding is needed for the separation of the blended source responses. The source ghost introduced by the strong sea surface reflection can be considered as a virtual source located at the mirror position of the actual source. In this abstract, we propose an acquisition concept that includes the source ghost as a natural source encoding such that it can be used for deblending, where the end result is deblended as well as deghosted. This acquisition method is easy to combine with man-made source encoding and also the concept of using the source ghost provides an interesting alternative to deal with the current depth distributed source for the broadband seismic data.
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In a blended acquisition, source encoding is needed for the separation of the blended source responses. The source ghost introduced by the strong sea surface reflection can be considered as a virtual source located at the mirror position of the actual source. In this abstract, we propose an acquisition concept that includes the source ghost as a natural source encoding such that it can be used for deblending, where the end result is deblended as well as deghosted. This acquisition method is easy to combine with man-made source encoding and also the concept of using the source ghost provides an interesting alternative to deal with the current depth distributed source for the broadband seismic data.
The source ghost introduced by the sea surface reflection is usually considered noise which needs to be removed before imaging. We propose to utilize the source ghost in deblending as a natural blending code such that the end result is both deblended and deghosted. This method is easy to combine with other temporal source codes and provides an interesting alternative to deal with the current depth distributed source for a broadband solution. In this abstract, we discuss how to use the source ghosts in the case of lateral blending and vertical blending to deblend and deghost with illustrations of simple synthetic models. We applied the method to field data where two sources are blended in the same lateral position but at different depths. The results obtained show that it is possible to deblend and deghost in one step in the variable depth source setting.
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The source ghost introduced by the sea surface reflection is usually considered noise which needs to be removed before imaging. We propose to utilize the source ghost in deblending as a natural blending code such that the end result is both deblended and deghosted. This method is easy to combine with other temporal source codes and provides an interesting alternative to deal with the current depth distributed source for a broadband solution. In this abstract, we discuss how to use the source ghosts in the case of lateral blending and vertical blending to deblend and deghost with illustrations of simple synthetic models. We applied the method to field data where two sources are blended in the same lateral position but at different depths. The results obtained show that it is possible to deblend and deghost in one step in the variable depth source setting.
Journal article
(2009)
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G. J. A. van Groenestijn, D. J. Verschuur
Accurate removal of surface-related multiples remains a challenge in many cases. To overcome typical inaccuracies in current multiple-removal techniques, we have developed a new primary-estimation method: estimation of primaries by sparse inversion (EPSI). EPSI is based on the same primary-multiple model as surface-related multiple elimination (SRME) and also requires no subsurface model. Unlike SRME, EPSI estimates the primaries as unknowns in a multidimensional inversion process rather than in a subtraction process. Furthermore, it does not depend on interpolated missing near-offset data because it can reconstruct missing data simultaneously. Sparseness plays a key role in the new primary-estimation procedure. The method was tested on 2D synthetic data.
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Accurate removal of surface-related multiples remains a challenge in many cases. To overcome typical inaccuracies in current multiple-removal techniques, we have developed a new primary-estimation method: estimation of primaries by sparse inversion (EPSI). EPSI is based on the same primary-multiple model as surface-related multiple elimination (SRME) and also requires no subsurface model. Unlike SRME, EPSI estimates the primaries as unknowns in a multidimensional inversion process rather than in a subtraction process. Furthermore, it does not depend on interpolated missing near-offset data because it can reconstruct missing data simultaneously. Sparseness plays a key role in the new primary-estimation procedure. The method was tested on 2D synthetic data.