haneet wason, felix oghenekohwo, and felix j. herrmann … · 2019. 9. 6. · haneet wason, felix...
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Haneet Wason, Felix Oghenekohwo, and Felix J. Herrmann Compressed sensing in 4-D marine – recovery of dense
time-lapse data from subsampled data without repetition We P3 12
Conclusions
Acknowledgements
We would like to thank all the sponsors of our SINBAD project for their support.
References
Abma, R., Ford, A., Rose-Innes, N., Mannaerts-Drew, H. and Kommedal, J. [2013] Continued development of simultaneous source acquisition for ocean bottom surveys. 75th EAGE Conference and Exhibition, doi: 10.3997/2214-4609.20130081.
Baron, D., Duarte, M.F., Wakin, M.B., Sarvotham, S. and Baraniuk, R.G. [2009] Distributed compressive sensing. CoRR, abs/0901.3403.
Beasley, C.J. [2008] A new look at marine simultaneous sources. The Leading Edge , 27(7), 914–917, doi: 10.1190/1.2954033.
Berkhout, A.J. [2008] Changing the mindset in seismic data acquisition. The Leading Edge, 27(7), 924–938, doi:10.1190/1.2954035.
Donoho, D.L. [2006] Compressed sensing. Information Theory, IEEE Transactions on, 52(4), 1289–1306.
Hampson, G., Stefani, J. and Herkenhoff, F. [2008] Acquisition using simultaneous sources. The Leading Edge, 27(7), 918–923, doi:10.1190/1.2954034.
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.
Lumley, D. and Behrens, R. [1998] Practical issues of 4d seismic reservoir monitoring: What an engineer needs to know. SPE Reservoir Evaluation & Engineering, 1(6), 528–538.
Oghenekohwo, F., Esser, E. and Herrmann, F.J. [2014] Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery. 76th EAGE Conference and Exhibition.
Wason, H. and Herrmann, F.J. [2013] Time-jittered ocean bottom seismic acquisition. SEG Technical Program Expanded Abstracts, 1–6, doi:10.1190/segam2013-1391.1.
Processing time-lapse data jointly leads to improved recovery of both vintages (baseline and monitor) and time-lapse signal Given the context of randomized subsampling, calibration errors ! deteriorate recovery of the time-lapse signal ! improve recovery of the vintages
Since calibration errors are inevitable in the real world, the insistence on repetition in time-lapse surveys can be relaxed Possible to recover dense time-lapse data from randomized subsampling and joint processing
Source position (m)
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no error [12.2 dB]
error ≈ 1.0 m [8.5 dB]
4-D recovery (Joint recovery method) error ≈ 2.8 m
[3.8 dB] 0% overlap
[2.0 dB]
50% overlap in acquisition matrices
As the calibration error increases, the 4-D signal recovery decreases significantly (b,c). Moreover, the recovery becomes comparable to the recovery with 0% overlap between the surveys, i.e., randomized acquisition without repetition (d).
Monitor recovery (Joint recovery method)
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Residual
no error [13.9 dB]
error ≈ 1.0 m [14.5 dB]
error ≈ 2.8 m [15.5 dB]
0% overlap [18.3 dB]
(a), (b), (c), (e), (f), (g) 50% overlap in acquisition matrices
(a) (b) (c) (d) (e) (f) (g) (h)
0% overlap
Independent vs. Joint recovery method
Randomized sampling in (4-D) marine increasing error increasing SNR
Time-lapse seismic Current acquisition paradigm:
! repeat expensive dense acquisitions & independent processing
! compute differences between baseline & monitor survey(s) ! hampered by practical challenges to ensure repetition Compressive sampling paradigm:
! cheap subsampled acquisition, e.g., via time-jittered marine subsampling
! may offer possibility to relax insistence on repeatability ! exploits insights from distributed compressed sensing
Distributed compressed sensing
! joint recovery model (JRM) ! different vintages share common information
Az }| {A1 A1 0A2 0 A2
�zz }| {2
4z0z1z2
3
5=
bz }| {b1
b2
�
x2 = z0 + z2
x1 = z0 + z1
baseline
monitor
differences vintages
common component
no repetition => 0% overlap in baseline and monitor acquisitions (shot positions)
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independent [11.6 dB]*
4-D signal Monitor recovery and residual
independent residual
joint residual
joint [13.9 dB]
joint [12.2 dB]
independent [-10.6 dB] Processing data jointly (by
exploiting common features of surveys) leads to improved recovery of both vintages and time-lapse signal. Note: all results for 50% overlap in acquisition matrices with no calibration errors. * [SNR dB] : signal-to-noise ratio
Aim: recovery of both vintages & time-lapse signal from incomplete data
Source position (m)200 400 600 800 1000 1200
Reco
rding
time
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BaselineMonitorMonitor w/ error
error ≈ 1.0 m
error ≈ 1.4 m
* Baseline o Monitor x Monitor with error in repeated shot positions
Time-jittered marine acquisition
(for 50% overlap in baseline & monitor acquisitions)
(a)
(a) (b) (c) (d) (e) (f)
(c) (d) (b)
subsampled and blended data (50 seconds of total recording time)
Receiver position (m)
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ordi
ng ti
me
(s)
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70
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120
Released to public domain under Creative Commons license type BY (https://creativecommons.org/licenses/by/4.0).Copyright (c) 2015 SLIM group @ The University of British Columbia.