Channel boundary detection based on 2D shearlet transformation: An application to the seismic data in the South Caspian Sea

Abstract

Because channels are seen as potential reservoirs and drilling risks, channel interpretation of seismic data can be considered as one of the demanding tasks in interpretation workstations. Shearlet transform, as a multi-scale and multi-directional transformation, is highly capable of detecting features with different dips and has found numerous applications in image processing tasks. The anisotropy property of the shearlet transform can be employed to detect edges where channels may occur in the seismic data. In this study, cone-adapted compactly-supported 2D shearlet transform was applied to both synthetic and real seismic data in the South Caspian Sea containing channels in order to decompose the data into different scales and directions. In order to detect the edges, the local maxima of shearlet coefficients were calculated at each pixel location at the finest scale of decomposition in both horizontal and vertical cones based on maximizing the shearlet coefficients for all shearing directions. Global thresholding based on the histogram of the local maxima followed by thinning were applied to delineate the edge pixels. The results of the proposed algorithm were compared qualitatively and quantitatively with those from classic image processing edge detectors such as Sobel and Canny. In both synthetic and real seismic data examples, our algorithm outperformed Sobel and Canny operators.