  The recent interest in using deep learning for seismic interpretation tasks,
such as facies classification, has been facing a significant obstacle, namely
the absence of large publicly available annotated datasets for training and
testing models. As a result, researchers have often resorted to annotating
their own training and testing data. However, different researchers may
annotate different classes, or use different train and test splits. In
addition, it is common for papers that apply machine learning for facies
classification to not contain quantitative results, and rather rely solely on
visual inspection of the results. All of these practices have lead to
subjective results and have greatly hindered the ability to compare different
machine learning models against each other and understand the advantages and
disadvantages of each approach.
  To address these issues, we open-source a fully-annotated 3D geological model
of the Netherlands F3 Block. This model is based on the study of the 3D seismic
data in addition to 26 well logs, and is grounded on the careful study of the
geology of the region. Furthermore, we propose two baseline models for facies
classification based on a deconvolution network architecture and make their
codes publicly available. Finally, we propose a scheme for evaluating different
models on this dataset, and we share the results of our baseline models. In
addition to making the dataset and the code publicly available, this work helps
advance research in this area by creating an objective benchmark for comparing
the results of different machine learning approaches for facies classification.
