Unsupervised seismic facies classification using deep convolutional autoencoder
This addresses the problem of reducing manual labor and subjectivity in seismic interpretation for geologists and researchers, though it is incremental as it builds on existing deep learning methods.
The paper tackles the challenge of manual labeling in seismic facies classification by proposing an unsupervised method using a deep convolutional autoencoder, which yields accurate results on real data and enables real-time analysis without human intervention.
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.