Higher-order-statistics and supertrace-based coherence-estimation algorithm

This article proposes a new higher-order-statistics-based coherence-estimation algorithm, which we denote as HOSC. Unlike the traditional crosscorrelation-based C1 coherence algorithm, which sequentially estimates correlation in the inline and crossline directions and uses their geometric mean as a coherence estimate at the analysis point, our method exploits three seismic traces simultaneously to calculate a 2D slice of their normalized fourth-order moment with one zero-lag correlation and then searches for the maximum correlation point on the 2D slice as the coherence estimate. To include more seismic traces in the coherence estimation, we introduce a supertrace technique that constructs a new data cube by rearranging several adjacent seismic traces into a single supertrace. Combining our supertrace technique with the C1 and HOSC algorithms, we obtain two efficient coherence-estimation algorithms, which we call ST-C1 and ST-HOSC. Application results on the real data set show that our algorithms are able to reveal more details about the structural and stratigraphic features than the traditional C1 algorithm, yet still preserve its computational efficiency.