1887

Abstract

Summary

We study the optimized shot point and receiver placement problem for hybrid (cross-spread source and receiver) gathers by using the spectral characteristics of the surface waves. This is an acquisition geometry design problem where the objective is to find an optimized layout for a limited number of shot points and receivers such that the sampled data provide maximum information about the continuous-domain data. By using the SEG Advanced Modeling II Arid Model data, we show that surface waves have a unique structure that allows us to represent them with only a small number of both sensors and shot points. After reviewing the conditions for exact recovery of a continuous-domain signal from its sparse samples, we propose a method to design optimized geometries based on the compressed sensing principles. We expect that placing the shot points and receivers at the most informative locations will be beneficial to existing noise attenuation and interpolation algorithms.

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/content/papers/10.3997/2214-4609.201901402
2019-06-03
2024-04-23
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References

  1. Baraniuk, R.G.
    [2007] Compressive sensing [Lecture Notes]. IEEE Signal Processing Magazine. 24, 118–121.
    [Google Scholar]
  2. Candès, E.J., Romberg, J.K. and Tao, T.
    [2006] Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207–1223.
    [Google Scholar]
  3. Herrmann, F.J.
    [2010] Randomized sampling and sparsity: Getting more information from fewer samples. Geophysics, 75(6), WB173–WB187.
    [Google Scholar]
  4. Meunier, J.
    [1999] 3D Geometry, Velocity Filtering and Scattered Noise. 69th Annual International Meeting, SEG, Expanded Abstracts, 1216–1219.
    [Google Scholar]
  5. Mosher, C., Kaplan, S.T. and Janiszewski, F.D.
    [2012] Non-uniform Optimal Sampling for Seismic Survey Design. 74th EAGE Conference and Exhibition, EAGE, Extended Abstracts, 3953–3957.
    [Google Scholar]
  6. Regone, C., Stefani, J., Wang, P., Gerea, C., Gonzalez, G. and Oristaglio, M.
    [2017] Geologic model building in Seam phase II land seismic challenges. The Leading Edge, 36, 738–749.
    [Google Scholar]
  7. Shannon, C.E.
    [1949] Communication in the Presence of Noise. Proceedings of the IRE, 37(1), 10–21.
    [Google Scholar]
  8. Schonewille, M.A., Romijn, R., Duijndam, A.J.W. and Ongkiehong, L.
    [2003] A general reconstruction scheme for dominant azimuth 3D seismic data. Geophysics, 68, 2092–2105.
    [Google Scholar]
  9. Strobbia, C., Zarkhidze, A., May, R., QuigleyJ. and BilsbyP.
    [2011] Attenuation of aliased coherent noise: modelbased attenuation for complex dispersive waves. First Break, 29(8), 93–100.
    [Google Scholar]
  10. Tibshirani, R.
    [1996] Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, 58, 267–288.
    [Google Scholar]
  11. Vermeer, G.J.O.
    [2012] 3D Seismic Survey Design, Second Edition. Society of Exploration Geophysicists, Geophysical References Series.
    [Google Scholar]
  12. Xu, S., Zhang, Y. Pham, D. and Lambare, G.
    [2005] Antileakage Fourier transform for seismic data regularization. Geophysics, 70(4), V87–V95.
    [Google Scholar]
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