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Abstract

Summary

Seismic processing and imaging requires regular and dense sampling on the surface, which can make the traditional seismic surveys too costly. Finding optimal acquisition geometries offering reduced survey costs without compromising imaging quality is a long-standing challenge in exploration seismology. A popular idea is to sub-sample data in a “random” fashion and then apply interpolation or (sparse) reconstruction to recover the complete data. Although such an approach is theoretically appealing, its actual implementation leads to practical complications. We propose “optimal deterministic” acquisition designs which are based on the so-called “difference sets”. We show that our proposed methodology not only avoids implementation difficulties of random sampling but also guarantees a high quality reconstruction irrespective of the subsurface geology. Our initial results based on synthetic data corroborate the superiority of the proposed methodology compared to random sampling.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201601239
2016-05-30
2024-04-25

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Deterministically Subsampled Acquisition Geometries for Optimal Reconstruction
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201601239
/content/papers/10.3997/2214-4609.201601239
/content/papers/10.3997/2214-4609.201601239
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