Choice of decomposition in multispectral coherence computation

Abstract

Abstract The iconic coherence attribute generally computed on the full-bandwidth seismic data has been used for 25 years to delineate structural and stratigraphic subsurface geometric features. More recently, multispectral coherence has been introduced to not only better define the more prominent features, but also more subtle features that may be poorly imaged by traditional broadband coherence attribute displays. Multispectral coherence generation utilizes the spectrally decomposed seismic data in the form of complex voice components within the seismic bandwidth. There are various methods available for spectral decomposition of seismic data, including the continuous wavelet transform, complex matching pursuit and the maximum entropy methods. In addition, a nonlinear spectral probe algorithm is also available and could be utilized for multispectral coherence generation. With all these methods available with the seismic interpreter, a question that pops up is which of these methods could be utilized for a more effective generation of multispectral coherence attribute. We first discuss multispectral coherence itself and its generation and follow that with a description of the different spectral decomposition methods mentioned above. Next, we generate spectral voice components and spectral probe components on two seismic data volumes from northeast British Columbia, Canada, and put them through multispectral coherence generation. A comparative performance of the different methods is then evaluated in terms of the multispectral coherence displays. Finally, some conclusions are drawn from the complete exercise.