Multispectral Dip for Improved Geometric Attribute Computations

Abstract

Accurate estimates of volumetric dip serve as input for curvature, aberrancy, reflector convergence attributes, guide the application of structure-oriented filters and the computation of coherence, amplitude gradient, as well as GLCM texture attributes. Different frequency components can exhibit slightly different dips, due to the differences in their resolution and sensitivity to noise. Research during the last few years has shown that multispectral coherence computed by summing the covariance matrices of individual spectral voice components can provide significant improvement over coherence computed from the covariance matrix computed from the original broadband data. A similar workflow can be constructed by computing multispectral dip by summing the gradient structure tensors computed from the individual spectral voice components. We apply this workflow to two different seismic datasets from the Taranaki and Canterbury Basins of New Zealand and find that the resulting multispectral dip estimates exhibit sharper images than those computed from the original broadband data. Images from the Taranaki dataset show improvements on the definition of a fault system, whereas those from the Canterbury dataset exhibit enhanced definition of faults and associated fractures as seen on the dip estimates, as well as both structural and amplitude curvature displays