Seismic coherence volumes are routinely used to delineate geologic features that might otherwise be overlooked on conventional amplitude volumes. In general, the quality of a coherence image is a direct function of the quality of the input seismic amplitude data. However, even after careful processing, certain spectral components will better illuminate a given feature than others. For this reason, one may wish to not only examine coherence computed from different filter banks, but somehow combine them into a single composite image. I do so by summing structure-oriented covariance matrices computed from spectral voices prior to computing coherence. I show that multispectral coherence images are superior to traditional broadband coherence images, even if the seismic amplitude data have been previously spectrally balanced. While much of this improvement can also be found in RGB blended volumes, multispectral coherence provides several advantages: (1) one can combine the information content of more than three coherence volumes, (2) there is only one rather than three volumes to be loaded into the workstation, and (3) the resulting grey-scale images can be co-rendered with other attributes of interest plotted against a polychromatic colour bar, such as P-impedance vs. Poisson’s ratio or SOM cluster results.


Article metrics loading...

Loading full text...

Full text loading...


  1. Chopra, S., and Marfurt, K. J.
    [2007] Seismic attributes for prospect identification and reservoir characterization. SEG Geophysical Development Series, 11.
    [Google Scholar]
  2. Dewett, D. T, and Hensa, A. A.
    [2015] Spectral similarity fault enhancement. Interpretation, 4, SB149–SB159.
    [Google Scholar]
  3. Gao, D.
    [2013] Wavelet spectral probe for seismic structure interpretation and fracture characterization: A workflow with case studies. Geophysics, 78, O56–O67.
    [Google Scholar]
  4. Gersztenkorn, A., and Marfurt, K. J.
    [1999] Eigenstructure-based coherence computation as an aid to 3-D structural and stratigraphic mapping. Geophysics, 64, 1468–1479.
    [Google Scholar]
  5. Hardage, B.
    [2009] Frequencies are fault finding factors: Looking low aids data interpretation. AAPG Explorer, 30, no 9, 34.
    [Google Scholar]
  6. Honorio, B. C. Z., Correia, U. M. da C., de Matos, M. C., and Vidal, A. C.
    [2016] Similarity attributes from differential resolution components. Interpretation, 4, T65–T73.
    [Google Scholar]
  7. Li, F., and Lu, W.
    [2014] Coherence attribute at different spectral scales. Interpretation, 2, 1–8.
    [Google Scholar]
  8. Sui, J.-K., Zheng, X.-D. and Li, Y.-D.
    [2015] A seismic coherency method using spectral attributes. Applied Geophysics, 12, no. 3, 353–361.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error