Automatic feature detection from seismic data is a demanding task in today's interpretation workstations. Channels are among important stratigraphic features in seismic data both due to their reservoir capability or drilling hazard potential. Shearlet transform as a multi‐scale and multi‐directional transformation is capable of detecting anisotropic singularities in two and higher dimensional data. Channels occur as edges in seismic data, which can be detected based on maximizing the shearlet coefficients through all sub‐volumes at the finest scale of decomposition. The detected edges may require further refinement through the application of a thinning methodology. In this study, a three‐dimensional, pyramid‐adapted, compactly supported shearlet transform was applied to synthetic and real channelised, three‐dimensional post‐stack seismic data in order to decompose the data into different scales and directions for the purpose of channel boundary detection. In order to be able to compare the edge detection results based on three‐dimensional shearlet transform with some famous gradient‐based edge detectors, such as Sobel and Canny, a thresholding scheme is necessary. In both synthetic and real data examples, the three‐dimensional shearlet edge detection algorithm outperformed Sobel and Canny operators even in the presence of Gaussian random noise.

Identification of geomorphological features in seismic data is a key element of seismic interpretation. Channels in the shallow subsurface are potential geohazards. At deeper levels, they can be the actual targets for (horizontal) drilling. Either way, it is important to optimally delineate these features prior to well location positioning and drilling. We have studied a poststack 3D seismic data from the South Caspian Sea featuring shallow channels that are considered potential geohazards for drilling operations. In the first step, we attenuate the acquisition footprints along the inline direction using a geostatistics approach based on factorial kriging. To better visualize channels in the presence of stratigraphic dips, we create a dense set of horizons using an inversion-based flattening algorithm. In the next step, we compare various discontinuity attributes such as semblance, similarity, curvature, and the relatively new attribute based on the multiscale and multidirectional shearlet transformation to determine which one best images our features of interest. Curvature attributes clearly image channel levies (positive curvature) and channel centers (negative curvature). Lateral changes in the curvature magnitude infer sedimentation from the north. Similarity, semblance, and shearlet transform attributes also successfully delineate channel edges, but these attributes do not contain additional geologic information. In the final step, we qualitatively analyze channel thickness variations by the red-green-blue blending of three spectral components based on short window Fourier transforms.

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