Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary

Abstract

Regularization techniques are commonly used to mitigate the ill-posed problems for pre-stack AVO (amplitude variation with offset) inversion. In recent years, a data-driven regularization method has successfully been applied to achieve desirable inversion accuracy. In contrast of assuming a certain distribution of the data, this method uses the dictionary learning algorithm to learn the structural characteristics of elastic parameters and then takes this information as a prior constraint in AVO inversion. However, in this method, KSVD (An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation) algorithm is used, which is of low computational efficiency and involves many parameters. In this paper, we propose a novel data-driven pre-stack inversion approach based on the orthogonal dictionary (ORTD) learning method. Because the atoms are orthogonal, the efficiency of dictionary operation is nearly 100 times higher than that of KSVD redundant dictionary. Instead of regulating the sparsity, atomic size and atomic length for the KSVD method, the proposed method depends on two parameters: threshold parameters and dictionary atomic length. Moreover, the parameter manipulation has little impacts on the operation efficiency for this approach. This greatly improves the applicability of the data-driven inversion method. We use model tests and field data applications to verify the performance of the proposed method.