LAUSR

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.