Mapping channel edges is a significant issue in 3Dseismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing themodified top-hat algorithm,we calculated themaximumcurvelet coefficients in all sub-bands. Employing top-hat in curvelet domain ismore effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edgemap resulting fromthe weighted average of all sub-edge imageswas obtained fromthe structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithmwas applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors.