![]() ![]() ![]() ![]() At the level of faulted and fractured Ratawi shale (~1300 ms), we display time slices through the input seismic amplitude volume (Figure 1a), and the results of the Sobel filter (Figure 1b) and energy-ratio coherence (Figure 1c), both computed from the seismic amplitude volume. We apply these attributes on a 3D seismic volume from south-east Kuwait. Unlike Luo et al.’s (1996) Sobel-filter coherence algorithm, equations 1-3 are not normalized by the RMS amplitude of the trace and thus provide a stronger response to an edge cutting a high-amplitude reflector than an edge cutting a low-amplitude reflector. The output “edge” attribute is the absolute value of the largest derivative of the nine. Rather than “smooth” along dip and azimuth, Al-Dossary and Al-Garni (2013) designed a structure-oriented Sobel filter wherein the derivatives are computed in nine non-orthogonal directions, ξ. Recent interest in structure-oriented filtering has resulted in a reexamination of this basic filter. That is balanced by the amplitude of the data within the analysis window. In spite of the “structural leakage” associated with computation without regard to dip, Aqrawi and Boe (2011) show some remarkable images using a simple 3D Sobel filter Subsequent Sobel filter based coherence similarity algorithms followed advances in semblance based coherence and are routinely computed along structural dip. (1996) developed the first such Sobel filter based similarity (or coherence) algorithm. One way to address this issue is to normalize equation (1) by a measure of the RMS amplitude within the same seismic window. In the presence of structural dip, applying equation (1) to a seismic amplitude time slice would result in strong changes as the wavelet varies laterally from peak to trough, overprinting lateral changes in reflectivity of interest. Unlike a photograph, seismic images have a third dimension. For a flat photograph containing pixels of amplitude a aligned along the x and y axes, the classical Sobel-filtered image, s, is simply Sobel filters are one of many filters that are commonly distributed when you purchase a digital camera. These range from suppression of spatial noise (random as well as coherent), through approximating missing data that give rise to acquisition footprint through 5D interpolation, to running structure-oriented edge-preserving filtering (Chopra and Marfurt, 2008, 2013). A wide variety of preprocessing steps are run on the seismic data to ensure an effective performance of the discontinuity attributes. Its importance has gradually been realized over the last decade and has reached a stage where preconditioning of the data has now become a regular practice. The preconditioning of seismic data is essential for the generation of seismic attributes. We demonstrate this simple cascaded workflow with examples from Kuwait and Canada, where one of the objectives is to provide improved attributes for subsequent automatic fault plane extraction. In the present work, we find the application of a Sobel filter to energy-ratio coherence volumes significantly sharpens faults and channel edges of interest. Since seismic attributes extract all subtle features in the seismic amplitude volume, preconditioning the data to enhance geologic edges and minimize edges due to acquisition and processing is critical to the analysis. Seismic discontinuity attributes that enhance edges not only accelerate the interpretation process, they also provide a quantitative measure of just how significant a given discontinuity is in relation to others. #SEISMAC DISCONTINUITY MANUAL#Careful manual interpretation of such features is both tedious and time consuming. While the more prominent features can often be easily visualized, smaller features critical to understanding the structural and depositional environment can be easily overlooked. Mapping geologic edges such as faults or channel levees forms a critical component in the interpretation on 3D seismic volumes. ![]()
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