Volumetric fault image enhancement — Some applications

The interpretation of faults on 3D seismic data is often aided by the use of geometric attributes such as coherence and curvature. Unfortunately, these same attributes also delineate stratigraphic boundaries (geologic signal) and apparent discontinuities due to cross-cutting seismic noise. Effective fault mapping thus requires enhancing piecewise continuous faults and suppressing stratabound edges and unconformities as well as seismic noise. To achieve this objective, we apply two passes of edge-preserving structure-oriented filtering followed by a recently developed fault enhancement algorithm based on a directional Laplacian of a Gaussian operator. We determine the effectiveness of this workflow on a 3D seismic volume from central British Columbia, Canada.

Multichannel Parallel Deblurring and Collaborative Registration Using Gaussian Total Variation Regularization for Image Fusion

We focus on the multichannel image fusion problem for the purpose of reaching the diffraction-limited resolution of turbulence-degraded images observed by multiple acquisition channels. A hybrid strategy consisting of multichannel parallel deblurring followed by collaborative registration is developed for the final fusion. In particular, a Gaussian total variation regularization scheme taking advantage of low-order Gaussian derivative operators is proposed, which integrates the deblurring and registration problems into a unified mathematical formalization. Specifically, the gradient magnitude of Gaussian operator is proposed to define the total variation norm, and the Laplacian of Gaussian operator is used to adjust the regularization parameter when searching the extremum in each iterative step. In addition, the coordination technique involving the regularization parameter among different channels is also considered.