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Sharp and laterally constrained multitrace impedance inversion based on blocky coordinate descent

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Seismic impedance inversion is a well-known method used to obtain the image of subsurface geological structures. Utilizing the spatial coherence among seismic traces, the laterally constrained multitrace impedance inversion (LCI) is superior to trace-by-trace inversion and can produce a more realistic image of the subsurface structures. However, when the traces are numerous, it will take great computational cost and a lot of memory to solve the large-scale matrix in the multitrace inversion, which restricts the efficiency and applicability of the existing multitrace inversion algorithm. In addition, the multitrace inversion methods are not only needed to consider the lateral correlation but also should take the constraints in temporal dimension into account. As usual, these vertical constraints represent the stratigraphic characteristics of the reservoir. For instance, total-variation regularization is adopted to obtain the blocky structure. However, it still limits the magnitude of model parameter variation and therefore somewhat distorts the real image. In this paper, we propose two schemes to solve these issues. Firstly, we introduce a fast algorithm called blocky coordinate descent (BCD) to derive a new framework of laterally constrained multitrace impedance inversion. This new BCD-based inversion approach is fast and spends fewer memories. Next, we introduce a minimum gradient support regularization into the BCD-based laterally constrained inversion. This new approach can adapt to sharp layer boundaries and keep the spatial coherence. The feasibility of the proposed method is illustrated by numerical tests for both synthetic data and field seismic data.
Czasopismo
Rocznik
Strony
623--631
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
autor
  • Center for Information Geoscience and School of Resource and Environment, University of Electronic Science and Technology of China, Chengdu 5162125, China
autor
  • Center for Information Geoscience and School of Resource and Environment, University of Electronic Science and Technology of China, Chengdu 5162125, China
autor
  • Center for Information Geoscience and School of Resource and Environment, University of Electronic Science and Technology of China, Chengdu 5162125, China
autor
  • Center for Information Geoscience and School of Resource and Environment, University of Electronic Science and Technology of China, Chengdu 5162125, China
autor
  • Center for Information Geoscience and School of Resource and Environment, University of Electronic Science and Technology of China, Chengdu 5162125, China
autor
  • BGP, Zhuozhou 072751, China
Bibliografia
  • 1. Auken E, Christiansen AV, Jacobsen BH, Foged N, Sorensen KI (2005) Piecewise 1D laterally constrained inversion of resistivity data. Geophys Prospect 53(4):497–506. https://doi.org/10.1111/j.1365-2478.2005.00486.x
  • 2. Calvetti D, Morigi S, Reichel L, Sgallari F (2000) Tikhonov regularization and the L-curve for large discrete ill-posed problems. J Comput Appl Math 123(1):423–446. https://doi.org/10.1016/S0377-0427(00)00414-3
  • 3. Gholami A (2015) Nonlinear multichannel impedance inversion by total-variation regularization. Geophysics 80(5):R217–R224. https://doi.org/10.1190/geo2015-0004.1
  • 4. Hamid H, Pidlisecky A (2015) Multitrace impedance inversion with lateral constraints. Geophysics 80(6):M101–M111. https://doi.org/10.1190/geo2014-0546.1
  • 5. Kumar R, Das B, Chatterjee R, Sain K (2016) A methodology of porosity estimation from inversion of post-stack seismic data. J Nat Gas Sci Eng 28:356–364. https://doi.org/10.1016/j.jngse.2015.12.028
  • 6. Li Z, Li Z, Lu W (2016) Multichannel predictive deconvolution based on the fast iterative shrinkage-thresholding algorithm. Geophysics 81(1):V17–V30. https://doi.org/10.1190/geo2015-0325.1
  • 7. Liu X and Yin X (2015) Blocky inversion with total variation regularization and bounds constraint. In: SEG technical program expanded abstracts 3497–3501
  • 8. Liu Z, Li G, Wei J, Zhang B (2016) Sparse reflectivity inversion with lateral constraint for nonstationary seismic data. In: SEG technical program expanded abstracts 2016 (3757–3761). https://doi.org/10.1190/segam2016-13775693.1
  • 9. Nose-Filho K, Takahata AK, Lopes R, Romano JMT (2016) A fast algorithm for sparse multichannel blind deconvolution. Geophysics 81(1):V7–V16. https://doi.org/10.1190/geo2015-0069.1
  • 10. Oldenburg DW, Scheuer T, Levy S (1983) Recovery of the acoustic impedance from reflection seismograms. Geophysics 48(10):1318–1337
  • 11. Portniaguine O, Zhdanov MS (1999) Focusing geophysical inversion images. Geophysics 64(3):874–887. https://doi.org/10.1190/1.1444596
  • 12. Russell B, Hampson D (1991) Comparison of poststack seismic inversion methods. In: SEG technical program expanded abstracts 1991 (876–878). https://doi.org/10.1190/1.1888870
  • 13. Russell B, Hampson D, Bankhead B (2006) An inversion primer. CSEG Rec 31(2):96–103
  • 14. Theune U, Jensås IØ, Eidsvik J (2010) Analysis of prior models for a blocky inversion of seismic AVA data. Geophysics 75(3):C25–C35. https://doi.org/10.1190/1.3427538
  • 15. Tikhonov AN, Arsenin VY (1977) Solutions of Ill-posed problems. Math Comput 32(144):491
  • 16. Yuan S, Wang S, Luo C, He Y (2015) Simultaneous multitrace impedance inversion with transform-domain sparsity promotion. Geophysics 80(2):R71–R80. https://doi.org/10.1190/GEO2014-0065.1
  • 17. Yuan S, Wang S, Ma M, Ji Y, Deng L (2017) Sparse Bayesian learning-based time-variant deconvolution. IEEE Trans Geosci Remote Sens 55(11):6182–6194. https://doi.org/10.1109/TGRS.2017.2722223
  • 18. Zhang R, Castagna J (2011) Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics 76(6):R147–R158. https://doi.org/10.1190/geo2011-0103.1
  • 19. Zhao Q, Meng D, Xu Z, Gao C (2015) A block coordinate descent approach for sparse principal component analysis. Neurocomputing 153:180–190. https://doi.org/10.1016/j.neucom.2014.11.038
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-317d5743-cc49-4ae8-b067-5a2a72bd3e7c
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