A deep learning framework for seismic facies classification

We propose a deep neural network based framework for seismic facies classification. The proposed framework utilizes a generative adversarial network for segmentation to learn a mapping from seismic reflection data to lithological facies. We incorporate uncertainty analysis into the workflow using a Bayesian framework. The proposed approach accelerates the interpretation process by reducing the need for human intervention, and also lessens individual biases that an interpreter may bring. We demonstrate the effectiveness of the proposed algorithm by testing on field data examples, and show that the proposed workflow classifies facies accurately. This may potentially enable the development of depositional environment maps in areas of low well density.