Decomposition and Application of Constrained Sparse Inversion Spectrum Based on ISTA Algorithm

doi:10.7657/XJPG20200413

Abstract

Abstract:

The resolution of conventional spectral decomposition can’t meet the needs of seismic interpretation, which can be well solved by the spectral decomposition of constrained sparse inversion. In this method, seismic signal is regarded as the convolution of known wavelet matrix library and pseudo-reflection coefficient, then the spectral decomposition is transformed into an inverse problem which focuses on how to obtain an optimum solution. The L₂ norm regularized by L₁ norm is used as the objective function for the constrained sparse inversion spectrum decomposition, then the iterative threshold algorithm is used to obtain the solution of the inverse problem. In order to improve the calculation speed of spectral decomposition, a new kind of operator is established on the basis of Ricker wavelet and the optimal solution can be obtained by ISTA algorithm. Based on which, the spectral decomposition of constrained sparse inversion is applied into numerical modeling and the results are compared with those of conventional spectral decomposition. The results show that the spectral decomposition of constrained sparse inversion has high time-frequency resolution. The actual application of the algorithm in the Wellblock Yi176 of Bonan sag in Jiyang depression indicates that the processing result is sensitive to oil and gas, which can be used to identify oil and gas reservoirs.

Key words: ISTA algorithm, constrained sparse inversion, spectral decomposition, continuous wavelet transform, hydrocarbon detection, L₁ norm regularization, Ricker wavelet, low-frequency shadow

CLC Number:

P631

GAO Qiuju, ZHANG Yunyin, QU Zhipeng, XU Yankai, WANG Zongjia, WANG Qianjun. Decomposition and Application of Constrained Sparse Inversion Spectrum Based on ISTA Algorithm[J]. Xinjiang Petroleum Geology, 2020, 41(4): 471-476.

Figures/Tables 5

References 19

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