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Tytuł artykułu

Compressive sensing aided seismic geometry design for offshore acquisition

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Seismic acquisition guided by the compressive sensing theory can significantly improve seismic data acquisition efficiency and reduce the cost. After reviewing the basic principles of compressive sensing, we propose an optimized random sampling method that can control the maximum sampling interval and improve the design flexibility. We analyze several factors that can introduce reconstruction errors from compressive sensed data and learn that besides sampling method, reconstruction errors increase with decimation degree and the complexity of structures and also depend on the reconstruction workflow. In addition, we provide a basic workflow of the geometry design of compressive sensing acquisition. We analyze the feasibility of the three types of receiving equipment that are widely used in marine environment and discuss the potential cost reduction and efficiency gain. Our field example demonstrates the detailed working process and the feasibility of the combination of random sailing line intervals and random shot intervals and verifies the effect of cost saving and efficiency increasing.
Czasopismo
Rocznik
Strony
547--562
Opis fizyczny
Bibliogr. 33 poz.
Twórcy
  • CNOOC Research Institute Ltd., Beijing 100028, China
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
autor
  • CNOOC Research Institute Ltd., Beijing 100028, China
  • Tianjin Branch, CNOOC China Limited, Tianjin 300450, China
Bibliografia
  • 1. Abma R, Kabir N (2006) 3D interpolation of irregular data with a POCS algorithm. Geophysics 71(6):E91–E97
  • 2. Baraniuk RG (2007) Compressive sensing. IEEE Signal Process Magzine 24(4):118–124
  • 3. Candès E, Romberg J, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math 59(8):1207–1223
  • 4. Chen SC, Wang HC, Chen GX (2012) The preliminary study on high efficient acquisition of geophysical data based on compressive sensing. CGS, Expand Abstr 387, pp 377–378
  • 5. Chen SC, Chen GX, Wang HC (2015) The preliminary study on high efficient acquisition of geophysical data with sparsity constrains. Geophys Prospect for Pet 54(1):24–35
  • 6. Diaz E, Guitton A (2011) Fast full waveform inversion with random shot decimation. SEG, Expand Abstr 2804–2808. https://doi.org/10.1190/1.3627777
  • 7. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306
  • 8. Hennenfent G, Herrmann FJ (2008) Simply denoise: wavefield reconstruction via jittered under sampling. Geophysics 73(3):V19–V28
  • 9. Herrmann FJ (2009) Sub-Nyquist sampling and sparsity: getting more information from fewer samples. SEG, Expand Abstr 3410–3415. https://doi.org/10.1190/1.3255570
  • 10. Herrmann FJ (2010) Randomized sampling and sparsity: getting more information from fewer samples. Geophysics 75(6):WB173–WB187
  • 11. Herrmann FJ, Erlangga YA, Lin TT (2009) Compressive simultaneous full-waveform simulation. Geophysics 74(4):A35–A40
  • 12. Huang XG (2020) A simulation of acquisition design and data processing for offshore compressive sensing seismic. Oil Geophys Prospect 55(2):248–256
  • 13. Li TY (2016) Research on the measurement matrix optimization and application of compressed sensing. Xiangtan University, Xiangtan
  • 14. Li CB, Zhang Y (2018) CSI: an efficient high-resolution seismic acquisition technology based on compressive sensing. Geophys Prospect Pet 57(4):537–542
  • 15. Li X, Aravkin AY, Leeuwen TV et al (2012) Fast randomized full-wavform inversion with compressive sensing. Geophysics 77(3):A13–A17
  • 16. Lin TTY, Herrmann FJ (2009) Unified compressive sensing framework for simultaneous acquisition with primary estimation. SEG, Expand Abstr 3113–3117. https://doi.org/10.1190/1.3255502
  • 17. Lv GH, Di ZX, Huo SD et al (2018) Seismic data acquisition based on compressive sensing. Geophys Prospect Pet 57(6):831–841
  • 18. Ma JW (2011) Sparsity-promoting seismic exploration. CGS, Expand Abstr 31, pp 101–102
  • 19. Ma JW (2018) Compressive sensing in geophysical exploration. Geophys Prospect Pet 57(1):24–27
  • 20. Ma JW, Yu SW (2017) Sparsity in compressive sensing. Lead Edge 36(8):646–652
  • 21. Moldoveanu N (2010) Random sampling: a new strategy for marine acquisition. SEG, Expand Abstr 51–55. https://doi.org/10.1190/1.3513834
  • 22. Mosher CC, Kaplan ST (2012) Non-uniform optimal sampling for seismic survey design. EAGE, Extend Abstr 333–336. https://doi.org/10.3997/2214-4609.20148781
  • 23. Mosher CC, Keskula E, Kaplan ST et al (2012) Compressive seismic imaging. SEG, Expand Abstr 1–5. https://doi.org/10.1190/segam2012-1460.1
  • 24. Mosher CC, Li CB, Larry M et al (2014) Increasing the efficiency of seismic data acquisition via compressive sensing. Lead Edge 33(4):386–391
  • 25. Naghizadeh M, Sacchi MD (2010) Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data. Geophysics 75(6):WB189–WB202
  • 26. Neelamani RN, Krohn CE, Krebs JR et al (2010) Efficient seismic forward modeling using simultaneous random sources and sparsity. Geophysics 75(6):WB15–WB27
  • 27. Wang Y, Cao J, Yang C (2011) Recovery of seismic wavefield-based on compressive sensing by an L1-norm constrained trust region method and the piecewise random subsampling. Geophys J Int 187(1):199–213
  • 28. Wang HC, Tao CH, Chen SC et al (2016) Study on highly efficient seismic data acquisition method and theory based on sparsity constraint. Chin J Geophys 59(11):4246–4265
  • 29. Wason H, Herrmann FJ, Lin TT et al (2011) Sparsity-promoting recovery from simultaneous data: a compressive sensing approach. SEG, Expand Abstr, 6–10. https://doi.org/10.1190/1.3628174
  • 30. Yuan SY, Wang SX et al (2015) Simultaneous multitrace impedance inversion with transform-domain sparsity promotion. Geophysics 80(2):R71–R80
  • 31. Yuan SY, Jiao XQ et al (2022) Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery. Geophysics 87(2):R165–R181
  • 32. Zhang Y, Biondi B, Clapp R (2013) Accelerating residual moveout based wave-equation migration velocity analysis with compressed sensing. SEG, Expand Abstr 4744–4749. https://doi.org/10.1190/segam2013-1343.1
  • 33. Zhou S, Lv Y, Lv GH et al (2017) Irregular seismic geometry design and data reconstruction based on compressive sensing. Geophys Prospect Pet 56(5):617–625
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ee8043de-293d-490f-b225-ab3bd3de7c5d
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