Coherence attribute applications on seismic data in various guises — Part 2 Satinder Chopra 1 and Kurt J. Marfurt 2 Abstract We have previously discussed some alternative means of modifying the frequency spectrum of the input seismic data to modify the resulting coherence image. The simplest method was to increase the high-frequency content by computing the first and second derivatives of the original seismic amplitudes. We also evaluated more sophisticated techniques, including the application of structure-oriented filtering to different spectral components before spectral balancing, thin-bed reflectivity inversion, bandwidth extension, and the amplitude volume technique. We further examine the value of coherence computed from individual spectral voice com- ponents, and alternative means of combining three or more such coherence images, providing a single volume for interpretation. Introduction Although most seismic data are processed to maxi- mize the bandwidth, some form of spectral balancing on the seismic data prior to attribute computation almost always helps in enhancing more subtle attribute anoma- lies. Most spectral-balancing algorithms assume the underlying reflectivity to be random, such that balanc- ing removes the spectral contribution of the seismic wavelet, with resulting frequency anomalies more closely associated with tuned reflections that occur at layers exhibiting quarter-wavelength thickness. Par- tyka et al. (1999) and Marfurt and Kirlin (2001) use spec- tral decomposition to quantify such tuning effects on 3D seismic data volumes. Partyka et al. (2005) show spec- tral magnitude components aðf Þ to be effective in map- ping lateral changes in vertical thickness, and they show spectral phase components φðf Þ to be effective in mapping faults and stratigraphic edges. Spectral voice components, vðf Þ ≡ mðf Þ exp½iφðf Þ, are less com- monly used by interpreters, but they often provide addi- tional insight into subsurface features (Fahmy et al., 2008; Chopra and Marfurt, 2016). Going one step fur- ther, coherence computed from such spectral voice components can highlight discontinuities that are pref- erentially imaged by a given spectral component. Although the analysis of multiple spectral components is common when restricted to a specific geologic target, the generation of 10–20 coherence volumes computed from a suite of spectral components proscribes simple analysis tools such as animation and interactive coren- dering for a large 3D seismic volume as a whole. Our goals therefore are to (1) determine what additional information is provided by coherence volumes com- puted from narrowband spectra and (2) evaluate alter- native ways to combine such multiple images into a single volume. Because different spectral components are sensitive to different scales of discontinuities, the challenge is to analyze these volumes effectively. Alter- native methods that can be used for this purpose in- clude using color, principal component analysis (PCA), self-organizing maps (SOMs), and multispectral coher- ence. We discuss three of these methods in this paper, leaving the SOM method for discussion in another paper. Integration of coherence and spectral decomposition Spectral decomposition In its simplest implementation, one can compute spectral components through the application of a suite of band-pass filters (often called filter banks) to the original seismic amplitude data. The interpretation value of a given spectral component is a function of the tuning thickness of a given geologic target and by the signal-to-noise ratio at that frequency. However, phase is also important, as demonstrated by Libak et al. (2017), who show that the lack of a coherence anomaly along a clearly discernable fault is often due to the unfortunate alignment of peaks and troughs of different reflectors across the fault. Such phase-alignment changes with frequency allow for improved fault imag- ing. Smaller, vertically localized faults are often best 1 TGS, Calgary, Alberta, Canada. E-mail: satinder.chopra@tgs.com. 2 The University of Oklahoma, Norman, Oklahoma, USA. E-mail: kmarfurt@ou.edu. Manuscript received by the Editor 5 January 2018; published ahead of production 24 March 2018; published online 29 June 2018; Repaginated version published online 6 August 2018. This paper appears in Interpretation, Vol. 6, No. 3 (August 2018); p. T531–T541, 8 FIGS. http://dx.doi.org/10.1190/INT-2018-0007.1. © 2018 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved. t Technical papers Interpretation / August 2018 T531 Interpretation / August 2018 T531 Downloaded 02/04/19 to 68.97.115.26. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/