Coherence algorithm with a high‐resolution time–time transform and feature matrix for seismic data

Abstract

ABSTRACTTraditional coherence algorithms are often based on the assumption that seismic traces are stationary and Gaussian. However, seismic traces are actually non‐stationary and non‐Gaussian. A constant time window and the canonical correlation analysis in traditional coherence algorithms are not optimal for non‐stationary seismic traces and cannot describe the similarity between adjacent seismic traces in detail. To overcome this problem, a new coherence algorithm using the high‐resolution time–time transform and the feature matrix is designed. The high‐resolution time–time transform used to replace the constant time window can produce a frequency‐dependent time local series to analyse non‐stationary seismic traces. The feature matrix, constructed by the frequency‐dependent time local series and the related local gradients, defines a new correlation metric that enhances more details of the geological discontinuities in seismic images than does the canonical correlation analysis. Additionally, the Riemannian metric is introduced for related calculations because the feature matrices are not defined in a Euclidean space but rather in a manifold space. Application to field data illustrates that the proposed method reveals more details of structural and stratigraphic features.