Conventional amplitude images are routinely and often exclusively used to interpret subsurface structures; however, the amplitude images are composite seismic response to various geologic features at different scales. The interference makes it difficult to investigate structural details that are not visually discernible in the amplitude images. This paper presents a new algorithmic workflow to unravel seismic structural details by differentiating waveform characteristics at varying scales. Using a wavelet model as a spectral probe, the algorithm performs a least-squares linear regression between the model and data at each sample location to calculate the absolute correlation coefficient. The resulting absolute correlation coefficient attribute, which is indicative of instantaneous waveform similarity relative to the wavelet spectral probe, provides a superior image with enhanced precision and resolution of the structural grain. Two case studies in the Powder River and the Appalachian Basins demonstrate that the new algorithm helps investigate kinematic relationships among structural elements and differentiate between structural styles. In particular, the algorithm helps better define the along-strike structural variation and segmentation associated with cross-strike lineaments in the foreland basins. Comparative analysis indicates that the wavelet spectral probe algorithm helps shed new light on subtle but critical structural fabrics not easily discernible from conventional amplitude images. Revealing such information has important implications for better defining basin structural complexities and reservoir heterogeneity, detecting faults and fractures at different scales, and modeling fractured reservoirs.

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