Paper Summary
The iconic coherence attribute is very useful for geologic feature imaging such as faults, deltas, submarine canyons, karst collapse, mass transport complexes, and more. Besides its preconditioning, the interpretation of discrete stratigraphic features on seismic data is also limited by its bandwidth, where in general the data with higher bandwidth yields crisper features than data with lower bandwidth. Some form of spectral balancing applied to the seismic
amplitude data can help in achieving such an objective, so that coherence run on spectrally balanced seismic data yields a better definition of the geologic features of interest. The quality of the generated coherence attribute is also dependent in part on the algorithm employed for its computation. In the eigenstructure decomposition procedure for coherence computation, spectral balancing equalizes each contribution to the covariance matrix, and thus yields crisper
features on coherence displays. There are other ways to modify the spectrum of the input data in addition to simple spectral balancing, including the amplitude-volume technique, taking the derivative of the input amplitude, spectral bluing, and thin-bed spectral inversion. We compare some of those techniques, and show their added value in seismic interpretation. We further examine the value of coherence computed from individual spectral voice components, called
multispectral coherence, as well as coherence computed from azimuth-limited seismic data volumes called multiazimuth coherence, both obtained as single volumes for interpretation.