cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation

cigKarst is a dataset created by the Computational Interpretation Group (CIG) for the deep-learning-based peleokarst interpretation in 3D seismic images, Xinming Wu is the main contributor to the dataset. This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth: Wu, X., S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. JGR, Solid Earth, Vol. 125(9), 1-23, e2020JB019685. [PDF]. doi: 10.1029/2020JB019685 Below are some brief description of the dataset: 1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256; 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip