Full-time domain matching pursuit and empirical mode decomposition based sparse fixed-point seismic inversion

Abstract

Abstract Sparse seismic inversion is wildly utilized for reservoir prediction and resolution improvement. Matching pursuit (MP) is an effective algorithm for solving L0-norm and obtaining sparse inversion results. Sparse seismic inversion based on MP (MPSI) can estimate the sparse parameters of subsurface from observations by controlling the iterations and threshold. However, the low convergence and stability limit the application of MPSI. To accelerate the convergence of MP, the full-time domain matching pursuit (FTMP) algorithm is first proposed. The seismic inversion based on FTMP can realize the multi-point inversion simultaneously instead of searching the inversion results one by one, which is the process of MPSI. Also, the prior model constraint is then involved in the objective function to improve the stability and the layer-boundary fidelity of the inversion results. Furthermore, the empirical mode decomposition (EMD) algorithm is introduced into inversion framework to recover the features of the seismic signal from the noisy seismic signal. The fixed-point (FP) algorithm is adopted to solve the objective function in this study. The optimal inversion results can be estimated after finite iterations by the FP algorithm. Combining the FTMP sparse seismic inversion framework, prior model constraint, EMD and FP algorithms, a complete sparse seismic inversion method named the full-time domain matching pursuit-based sparse fixed-point seismic inversion is ultimately proposed. The synthetic and field examples are utilized to demonstrate the stability and practicality of this approach. Compared with MPSI, the inversion results of the proposed method have higher resolution and fidelity of the layer-boundary.