Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures. Features based on the effective singular values of curvelet coefficients are then used to train a classifier that can label an entire seismic volume with relatively high accuracy. Experiments on reference seismic sections in the Netherlands North Sea seismic dataset result in 73.8% mean pixel accuracy and 75.5% mean class accuracy, with an average labeling time of 5.2 seconds per section. These results are promising considering the nature of seismic images, and the lack of accurate edges between different geological structures.