cigFacies: a massive-scale benchmark dataset of seismic facies and its application

cigFacies is a dataset create by the Computational Interpretation Group (CIG) for the AI-based automatic seismic facies classification in 3-D seismic data, Hui Gao, Xinming Wu, Xiaoming Sun and Mingcai Hou are the main contributors to the dataset. This is the benchmark skeletonization datasets of seismic facies, guided by the knowledge graph of seismic facies and constructed from three different stategies (field seismic data, synthetic data and GAN-based generation). Below are some brief desription of the datasets: 1) The "The benchmark skeletonization datasets" file consists of 5 classes of seismic facies. 2) The "parallel_class", "clinoform_class", "fill_class", "hummocky_class" and "chaotic_class" consist of 2000, 1500, 1500, 1500, 1500 stratigraphic skeletonization data constructed from field seismic data, synthetic data and GAN-based generation, respectively. The source codes for constructing the benchmark dataset of seismic facies and deep learning for seismic facies classification have been uploaded to Github and are freely available at cigFaciesNet.