Seismic sparse inversion in mixed-domain utilizing fast matching pursuit algorithm

Key words:  fast matching pursuit  sparse reflectivity  mixed-domain model  low-frequency constraint  resolution enhancement

Abstract:

Seismic inversion utilizing matching pursuit algorithm (FMP-SI) is one of the most effective sparse inversion methods with superior resolution by considering the sparse characteristic on model reflectivity. Considering the excellent anti-noise property and the low computational efficiency of traditional matching pursuit algorithms, we proposed a novel mixed-domain sparse inversion utilizing the fast matching pursuit algorithm to improve the inversion efficiency. Sparse optimal solution of the subsurface reflectivity can be achieved by controlling the number of iterations and iteration's threshold. Firstly, the mixed-domain modeling operator and regularization equation are established by jointing the mixed-domain convolution with the low-frequency constraint, which can reduce the search space of model parameters effectively. In addition, the object function in the mixed-frequency domain can be deduced by a polyatomic fast matching pursuit algorithm. The recovery efficiency and convergence precision of model parameters can be improved. Finally, the feasibility and excellent stability are illustrated by several synthetic simulations and a field case. From the results, we conclude that the proposed method can achieve superior resolution and high anti-noise ability compared with the conventional time domain inversions. Furthermore, the problem of local extremum in single-atom matching pursuit algorithms can be avoided effectively.