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Regularized inversion of amplitude‑versus‑incidence angle (AVA) based on a piecewise‑smooth model

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Different from the stacked seismic data, pre-stack data includes abundant information about shear wave and density. Through inversing the shear wave and density information from the pre-stack data, we can determine oil-bearing properties from different incident angles. The state-of-the-art inversion methods obtain either low vertical resolution or lateral discontinuities. However, the practical reservoir generally has sharp discontinuities between different layers in vertically direction and is horizontally smooth. Towards obtaining the practical model, we present an inversion method based on the regularized amplitude-versus-incidence angle (AVA) data to estimate the piecewise-smooth model from pre-stack seismic data. This method considers subsurface stratum as a combination of two parts: a piecewise smooth part and a constant part. To fix the ill-posedness in the inversion, we adopt four terms to define the AVA inversion misfit function: the data misfit itself, a total variation regularization term acting as a sparsing operator for the piecewise constant part, a Tikhonov regularization term acting as a smoothing operator for the smooth part, and the last term to smoothly incorporate a priori information for constraining the magnitude of the estimated model. The proposed method not only can incorporate structure information and a priori model constraint, but also is able to derive into a convex objective function that can be easily minimized using iterative approach. Compared with inversion results of TV and Tikhonov regularization methods, the inverted P-wave velocity, S-wave velocity and density of the proposed method can better delineate the piecewise-smooth characteristic of strata.
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Bibliogr. 29 poz.
  • College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China,
  • Key Laboratory of Agricultural Information Engineering of Sichuan Province, Sichuan Agricultural University, Ya’an 625014, China
  • College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
  • Key Laboratory of Agricultural Information Engineering of Sichuan Province, Sichuan Agricultural University, Ya’an 625014, China
  • College of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
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Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
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