Multispectral coherence

Abstract

Seismic coherence is routinely used to delineate geologic features that might otherwise be overlooked on conventional seismic amplitude volumes. In general, one wishes to interpret the most broadband data possible. However, because of the thickness tuning effects, certain spectral components often better illuminate a given feature with higher signal-to-noise ratio than others. Clear images of channels and other stratigraphic features that may be buried in the broad-band data may "light up" at certain spectral components. For the same, coherence attributes computed from spectral voice components (equivalent to a filter bank) also often provide sharper images, with the "best" component being a function of tuning thickness and reflector alignment across faults. While one can co-render three coherence images using RGB blending, display of the information contained in more than three volumes in a single image is difficult. We address this problem by summing a suite of structure-oriented covariance matrices computed from spectral voices resulting in a "multi-spectral" coherence algorithm. We demonstrate the value of multi-spectral coherence by comparing it to both RGB blended volumes and coherence computed from spectrally balanced, broad-band seismic amplitude volume from a .megamerge survey acquired over the Red Fork Formation of the Anadarko Basin, Oklahoma. The multi-spectral coherence images provide better images of channel incisement and are less noisy than those computed from the broadband data. Multi-spectral coherence also provides several advantages over RGB blended volumes: first, one can combine the information content from more than three spectral voices; second, only one volume needs to be loaded into the workstation; and third, the resulting gray-scale images can be co-rendered with other attributes of interest, for example, petrophysics parameters, plotted against a polychromatic color bar.

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

“…), the coherence algorithm with robust local slope estimation (Wang, Lu and Zhang ), the multiazimuth coherence (Qi, Gu and Li ) and the multispectral coherence (Li et al . ).…”

Seismic fault enhancement using spectral decomposition assisted attributes

“…Multispectral coherence (Alaei, 2012;Dewett and Henza, 2016;Marfurt, 2017;Li et al, 2018;Lyu et al, 2018) improves the quality of the coherence images by combining the coherence results from different spectral components. It better delineates the seismic edges and boundaries over the coherence from the full-bandwidth seismic data.…”

Multi-sparsity-based spectral attributes for discontinuity detection

“…Several seismic attributes, such as coherence, curvature, phase, spectral decomposition, and other edge-sensitive attributes (e.g., Luo et al 2003;Oyedele 2005;Yuan et al 2019a, b;Li et al 2018;Wang et al 2018c), have been proposed and used to detect geological anomalies. Different seismic attributes are sensitive to different geological structures.…”