Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change—i.e. no time-lapse—the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e., where the acquisition parameters such as source/receiver coordinates are not precise.


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  1. Baron, D., Duarte, M.F., Wakin, M.B., Sarvotham, S. and Baraniuk, R.G.
    [2009] Distributed Compressive Sensing. CoRR, abs/0901.3403.
    [Google Scholar]
  2. Eiken, O., Haugen, G.U., Schonewille, M. and Duijndam, A.
    [2003] A proven method for acquiring highly repeatable towed streamer seismic data. Geophysics, 68(4), 1303–1309.
    [Google Scholar]
  3. Harris, P. and Veritas, D.
    [2005] Prestack repeatability of time-lapse seismic data. In: SEG Technical Program Expanded Abstracts, 25. 2410–2413.
    [Google Scholar]
  4. Hennenfent, G., Fenelon, L. and Herrmann, F.J.
    [2010] Nonequispaced curvelet transform for seismic data reconstruction: a sparsity-promoting approach. Geophysics, 75(6), WB203–WB210.
    [Google Scholar]
  5. Hennenfent, G. and Herrmann, F.J.
    [2008] Simply denoise: wavefield reconstruction via jittered under-sampling. Geophysics, 73(3), V19–V28.
    [Google Scholar]
  6. Herrmann, F.J.
    [2010] Randomized sampling and sparsity: Getting more information from fewer samples. Geophysics, 75(6), WB173–WB187.
    [Google Scholar]
  7. Houck, R.T.
    [2007] Time-lapse seismic repeatability—How much is enough?The Leading Edge, 26(7), 828–834.
    [Google Scholar]
  8. Kragh, E. and Christie, P.
    [2002] Seismic repeatability, normalized rms, and predictability. The Leading Edge, 21(7), 640–647.
    [Google Scholar]
  9. Lumley, D.E.
    [2001] Time-lapse seismic reservoir monitoring. Geophysics, 66(1), 50–53.
    [Google Scholar]
  10. Mansour, H., Wason, H., Lin, T.T. and Herrmann, F.J.
    [2012] Randomized marine acquisition with compressive sampling matrices. Geophysical Prospecting, 60(4), 648–662.
    [Google Scholar]
  11. Mosher, C., Li, C., Morley, L., Ji, Y., Janiszewski, F., Olson, R. and Brewer, J.
    [2014] Increasing the efficiency of seismic data acquisition via compressive sensing. The Leading Edge, 33(4), 386–391.
    [Google Scholar]
  12. Oghenekohwo, F., Esser, E. and Herrmann, F.J.
    [2014] Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery. EAGE, UBC, UBC.
    [Google Scholar]
  13. Rickett, J. and Lumley, D.
    [2001] Cross-equalization data processing for time-lapse seismic reservoir monitoring: A case study from the Gulf of Mexico. Geophysics, 66(4), 1015–1025.
    [Google Scholar]
  14. Ross, C.P., Altan, S. et al
    . [1997] Time-Lapse Seismic Monitoring: Repeatability Processing Tests. In: Offshore Technology Conference. Offshore Technology Conference.
    [Google Scholar]
  15. Wason, H. and Herrmann, F.J.
    [2013] Time-jittered ocean bottom seismic acquisition. SEG Technical Program Expanded Abstracts, 1–6.
    [Google Scholar]
  16. Wason, H., Oghenekohwo, F. and Herrmann, F.J.
    [2015] Compressed sensing in 4-D marine—recovery of dense time-lapse data from subsampled data without repetition. EAGE Annual Conference Proceedings, UBC, UBC. (EAGE, Madrid).
    [Google Scholar]

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