Enhanced coherence using principal component analysis

Abstract

Coherence is a measure of similarity between seismic waveforms. It gives a quantitative description of lateral reflection changes and highlights variations of the geologic features within a seismic image. However, subtle changes in waveforms are often difficult to capture using traditional coherence measures because of the high similarity among the remaining parts in the vertical analysis window. We have developed an attribute called enhanced coherence based on principal component analysis (PCA) with the goal of reducing redundancy within the vertical analysis window, which is often composed of the parts with a high similarity between neighboring traces, and highlighting subtle lateral changes. In computing such a coherence image, we first extract seismic data within a specified time window along a picked horizon. Then, we calculate the enhanced coherence from reduced data obtained using a dimension-reduction technique. Because seismic data typically consist of large volumes, PCA is chosen for dimension reduction due to its insensitivity to the amount of data. We also find that reduced data based on PCA is equivalent to applying texture model regression with multiple models obtained from the data. We have evaluated the enhanced coherence by applying it to poststack data and prestack data acquired over the Sichuan Basin in southwestern China. We determined that the enhanced coherence has a higher resolution for delineating subtle lateral changes. Additionally, enhanced coherence calculated from prestack data is proven to be able to capture anisotropic features.