An efficient and robust scheme for the implementation of eigenstructure-based coherency algorithms

The coherence attribute has been widely used to detect faults and channels as well as other structural and stratigraphic discontinuities since its introduction. Currently, the most commonly used algorithm for coherence computation is the eigenstructure-based coherence method (C3). When seismic data are of a low signal-to-noise ratio, more traces should be involved in coherence computation to improve the robustness to noise; thus, a larger analysis window shall be used. However, this will lead to increased time costs and blurred coherence images. To address these issues, we have adopted a modified eigenstructure-based coherency algorithm (C3m) based on an inverse distance weighting method and dimensionality reduction of the covariance matrix for eigendecomposition in the case of a large lateral analysis window whose size is larger than 3 x 3 (nine traces). To further improve the computational efficiency, the add-drop algorithm is also used for covariance matrix construction. In the case of large analysis windows, the time cost by C3m is less than the time cost by C3 as demonstrated in synthetic and field data studies; also, the coherence results computed via C3m are superior to those computed via the traditional C3 method, and the new method is more favorable to seismic interpretation. Thus, our method shall be a potentially efficient and robust alternative to C3 for coherence attribute computation.