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High-resolution reflectivity inversion based on joint sparse representation

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High-resolution reflectivity inversion is termed as a fundamental yet essential step for the prediction of thin-bedded hydrocarbon reservoirs. However, algorithms suffer from two key issues: (1) seismic inversion is an ill-posed problem that has multiple solutions, and the results of trace-by-trace seismic inversion are quite poor in lateral continuity, and (2) algorithm stability is likely to be decreased owing to the noise and distortion associated with the acquisition and processing flows. In the current article, we formulate a new joint sparse representation through the combination with L2,1- norm misfit function, which possesses superior noise robustness, in particular in the presence of outliers. On the basis of the L2,1- norm regularization, this specific approach enforces a common sparsity profile, together with consistently lowering the multiplicity of solution. Subsequent to that, the resultant algorithm is applied to the multi-trace seismic inversion. Besides, the wedge model trial and practical applications suggest that the proposed inversion algorithm is stable, in addition to having good noise robustness and lateral continuity; moreover, the vertical resolution of λ/8 is realized under the noise and outliers interference. The logging data calibration illustrates that the proposed methodology is accurate and credible.
Czasopismo
Rocznik
Strony
1535--1550
Opis fizyczny
Bibliogr. 56 poz.
Twórcy
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
  • Engineering and Technical College, Chengdu University of Technology, Leshan 614000, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
autor
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
  • School of Education, China West Normal University, Nanchong 637002, China
autor
  • CNOOC Research Institute, Beijing 100027, China
autor
  • CNOOC Research Institute, Beijing 100027, China
Bibliografia
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1bb2efc0-7373-4476-9b4b-b59c6a4c6725
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