Abstract
Random seismic noise, present in every authentic seismic data set, hampers both geoscientists’ manual interpretation of data and computerized delineation and analysis of seismic features. Therefore, many noise suppression techniques have the goal of preserving image quality. For channel detection, accurately suppressing seismic noise without damaging image detail is crucial. Theoretically, channel patterns can be automatically detected owing to their unique spatial footprint, which differentiates them from other three-dimensional seismic features. One notable characteristic of channels is their local linearity: Their spatial extent is much greater in one direction than in any other direction. A variety of techniques, such as spatial filters, can enhance the "slender" characteristic of channels. Unfortunately, most of these techniques reduce noise by smoothing the data, resulting in a loss of edge definition. During the past few years the literature has revealed several new edge-preserving noise reduction techniques, including edge-preserving smoothing and complex wavelet transforms. In this case study I illustrate the performance of edge-preserving smoothing based on the redundant wavelet transform (RWT) and demonstrate its usefulness before running an edge detection algorithm to reveal channel patterns in seismic data from Saudi Arabia. Our examples demonstrate that RWT can successfully preserve, enhance, and delineate channel edges that are otherwise not readily visible on conventional seismic amplitude displays.
