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Model parameterizations in the time domain multi parameter acoustic least squares reverse time migration

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Języki publikacji
EN
Abstrakty
EN
Acoustic least-squares reverse time migration (LSRTM) can retrieve the improved refection images. However, the most existing acoustic LSRTM approaches generally ignore the density variation of the subsurface. The multi-parameter acoustic LSRTM approach in the presence of a density parameter can overcome this weakness. However, diferent model parameterizations in such an acoustic LSRTM approach can lead to diferent migration artifacts and infuence the rate of convergence. In this paper, we mainly investigate and analyze the refectivity images of diferent model parameterizations in the multi-parameter acoustic LSRTM approach, in which the velocity–density parameterization can provide reliable refection images. According to Green’s representation theory, we derive the gradients of the objective function with regard to the multi-parameter refectivity images in detail, in which both the migration image of density in the velocity–density model parameterization and the migration image of impedance in the impedance–velocity model parameterization are free from the low-frequency artifacts. Through numerical examples using the layered and fault models, we have proved that the multiparameter acoustic LSRTM approach with the velocity–density model parameterization can provide the migration images with higher resolution and improved amplitudes. Meanwhile, a correlation-based objective function is less sensitive to amplitude errors than the conventional waveform-matching objective function in the multi-parameter acoustic LSRTM approach.
Czasopismo
Rocznik
Strony
441--458
Opis fizyczny
Bibliogr. 39 poz.
Twórcy
autor
  • School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • National Engineering Laboratory for Ofshore Oil Exploration, Xi’an Jiaotong University, Xi’an 710049, China
autor
  • School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • National Engineering Laboratory for Ofshore Oil Exploration, Xi’an Jiaotong University, Xi’an 710049, China
Bibliografia
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  • 6. Dutta G, Sinha M, Schuster GT (2014) A cross-correlation objective function for least-squares migration and visco-acoustic imaging. In: 84th Annual international meeting, SEG, expanded abstracts, pp 3985–3989
  • 7. Feng Z, Guo B, Schuster GT (2018) Multiparameter deblurring filter and its application to elastic migration and inversion. Geophysics 83:1–49. https://doi.org/10.1190/GEO2017-0572.1
  • 8. Gao Z, Li C, Zhang B, Jiang X, Pan Z, Gao J, Xu Z (2020) Building large-scale density model via a deep learning based data-driven method. Geophysics. https://doi.org/10.1190/geo2019-0332.1
  • 9. Guo X, Liu H, Shi Y, Wang W, Zhang Z (2017) Improving waveform inversion using modified interferometric imaging condition. Acta Geophys 66:71–80. https://doi.org/10.1007/s11600-017-0107-9
  • 10. Guo X, Liu H, Shi Y, Wang W, Zhang Z (2020) Wavefield decomposition in arbitrary diction and an imaging condition based on stratigraphic dip. Geophysics. https://doi.org/10.1190/geo2019-0617.1
  • 11. Li Q, Huang J, Li Z, Yong P, Li N (2016) Multi-source least-squares reverse time migration based on the first-order velocity-stress wave equation. Chin J Geophys 59(12):4666–4676. https://doi.org/10.6038/cjg20161226
  • 12. Liu Y, Teng J, Xu T, Bai Z, Lan H, Badal J (2016) An efficient step length formula for correlative least-squares reverse time migration. Geophysics 81(4):S221–S238. https://doi.org/10.1190/geo2015-0529.1
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  • 18. Ren ZM, Liu Y, Sen MK (2017) Least-squares reverse time migration in elastic media. Geophys J Int 208:1103–1125. https://doi.org/10.1093/gji/ggw443
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  • 29. Yang P, Gao J, Wang B (2015) A graphics processing unit implementation of time-domain full waveform inversion. Geophysics 80(3):F31–F39. https://doi.org/10.1190/geo2014-0283.1
  • 30. Yang P, Brossier R, Métivier L, Virieux J, Zhou W (2018) A time-domain preconditioned truncated Newton approach to multiparameter visco-acoustic full waveform inversion. SIAM J Sci Comput 40:B1101–B1130. https://doi.org/10.1137/17M1126126
  • 31. Yang J, Liu Y, Dong L (2016) Least-squares reverse time migration in the presence of density variations. Geophysics 81:S497–S509. https://doi.org/10.1190/GEO2016-0075.1
  • 32. Yu J, Hu J, Schuster GT, Estill R (2006) Prestack migration deconvolution. Geophysics 71(2):S53–S62. https://doi.org/10.1190/1.2187783
  • 33. Zhang W, Shi Y (2019) Imaging conditions for elastic reverse time migration. Geophysics 84(2):S95–S111. https://doi.org/10.1190/GEO2018-0197.1
  • 34. Zhang W, Gao J, Gao Z, Shi Y (2020) 2D and 3D amplitude-preserving elastic reverse time migration based on the vector-decomposed P- and S-wave records. Geophys Prospect 68:2712–2737. https://doi.org/10.1111/1365-2478.13023
  • 35. Zhang Y, Duan L, Xie Y (2015) A stable and practical implementation of least-squares reverse time migration. Geophysics 80(1):V23–V31. https://doi.org/10.1190/geo2013-0461.1
  • 36. Zhang Y, Sun J (2009) Practical issues in reverse time migration: true amplitude gathers, noise removal and harmonic source encoding. First Break 27:53–59. https://doi.org/10.3997/1365-2397.2009002
  • 37. Zhang Y, Gao J, Peng J (2018) Variable-order finite difference scheme for numerical simulation in 3-D poroelastic media. IEEE Trans Geosci Remote Sens 56(5):2991–3001
  • 38. Zhou W, Brossier R, Operto S, Virieux J (2015) Full waveform inversion of diving and reflected waves for velocity model building with impedance inversion based on scale separation. Geophys J Int 202:1535–1554. https://doi.org/10.1093/gji/ggv228
  • 39. Zhu T, Carcione JM (2014) Theory and modeling of constant-Q P- and S-waves using fractional spatial derivatives. Geophys J Int 196:1787–1795. https://doi.org/10.1093/gji/ggt483
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-901c1af4-e8b8-4abb-8daa-4f682d9f2cf1
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