Sparse Bayesian linearized amplitude‐versus‐angle inversion

ABSTRACT The technique of seismic amplitude‐versus‐angle inversion has been widely used to estimate lithology and fluid properties in seismic exploration. The amplitude‐versus‐angle inversion problem is intrinsically ill‐posed and generally stabilized by the use of L2‐norm regularization methods but with drawback of smoothing important boundaries between adjacent layers. In this study, we propose a sparse Bayesian linearized solution for amplitude‐versus‐angle inversion problem to preserve the sharp geological interfaces. In this regard, a priori constraint term with two regularization functions is presented: the sparse constraint regularization and the low‐frequency model information. In addition, to obtain high‐resolution reflectivity estimation, the model parameters decorrelation technique combined with dipole decomposition method is employed. We validate the applicability of the presented method by both synthetic and real seismic data from the Gulf of Mexico. The accuracy improvement of the presented method is also confirmed by comparing the results with the commonly used Bayesian linearized amplitude‐versus‐angle inversion.