cigChannel: A massive-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation

Abstract. Identifying buried channels in 3D seismic volumes is essential for characterizing hydrocarbon reservoirs and offering insights into paleoclimate conditions, yet it remains a labor-intensive and time-consuming task. The data-driven deep learning methods are highly promising to automate the seismic channel interpretation with high efficiency and accuracy, as they have already achieved significant success in similar image segmentation tasks within the field of computer vision (CV). However, unlike the CV domain, the field of seismic exploration lacks a comprehensive benchmark dataset for channels, severely limiting the development, application, and evaluation of deep learning approaches in seismic channel interpretation. Manually labeling 3D channels in field seismic volumes can be a tedious and subjective work and most importantly, many field seismic volumes are proprietary and not accessible to most of the researchers. To overcome these limitations, we propose a comprehensive workflow of geological channel simulation and geophysical forward modeling to create a massive-scale synthetic seismic dataset containing 1,200 256×256×256 seismic volumes with labels of more than 10,000 diverse channels and their associated sedimentary facies. It is by far the most comprehensive dataset for channel identification, providing realistic and geologically reasonable seismic volumes with meandering, distributary, and submarine channels. Trained with this synthetic dataset, a convolutional neural network (simplified from the U-Net) model performs well in identifying various types of channels in field seismic volumes, which indicates the diversity and representativeness of the dataset. We have made the dataset, codes generating the data, and trained model publicly available for facilitating further research and validation of deep learning approaches for seismic channel interpretation.