  With the increased size and complexity of seismic surveys, manual labeling of
seismic facies has become a significant challenge. Application of automatic
methods for seismic facies interpretation could significantly reduce the manual
labor and subjectivity of a particular interpreter present in conventional
methods. A recently emerged group of methods is based on deep neural networks.
These approaches are data-driven and require large labeled datasets for network
training. We apply a deep convolutional autoencoder for unsupervised seismic
facies classification, which does not require manually labeled examples. The
facies maps are generated by clustering the deep-feature vectors obtained from
the input data. Our method yields accurate results on real data and provides
them instantaneously. The proposed approach opens up possibilities to analyze
geological patterns in real time without human intervention.
