Unsupervised seismic facies classification using deep convolutional autoencoder

Abstract

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 seismic interpretation techniques is based on deep neural networks. These approaches are data-driven and require large labeled data sets for network training. We have developed 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, which allows an interpreter to identify the dominant seismic features. The proposed approach opens possibilities to analyze geologic patterns in real time without human intervention.